diff --git a/01_document/_freeze/example_census_race_ethnicity_calculation/execute-results/html.json b/01_document/_freeze/example_census_race_ethnicity_calculation/execute-results/html.json index 502ec4c..20c7c81 100644 --- a/01_document/_freeze/example_census_race_ethnicity_calculation/execute-results/html.json +++ b/01_document/_freeze/example_census_race_ethnicity_calculation/execute-results/html.json @@ -1,9 +1,11 @@ { - "hash": "e36a4455e2d64da9c13563770e4b884a", + "hash": "14eda42f29bdd13f91193b5a6588303f", "result": { "engine": "knitr", - "markdown": "---\ntitle: \"Estimating Demographics of Custom Spatial Features\"\nsubtitle: \"Accessing U.S. Census Bureau Data & Calculating Weighted Averages with Areal- and Population-Weighted Interpolation\"\nnumber-sections: true\ntoc: true\ntoc-depth: 4\nformat:\n html:\n self-contained: false\nbibliography: references.bib\n---\n\n```{=html}\n \n\n```\n\n## Background {#sec-background}\n\n::: callout-note\nFor comments, suggestions, corrections, or questions on anything below, contact [david.altare\\@waterboards.ca.gov](mailto:david.altare@waterboards.ca.gov), or [open an issue](https://github.com/daltare/example-census-race-ethnicity-calculation/issues) on github.\n:::\n\n::: callout-warning\nThis document is a work in progress, and may change significantly.\n:::\n\nThis document provides an example of how to use tools available from the [R programming language](https://www.R-project.org/) [@R] to estimate characteristics of any given *target* spatial area(s) (e.g., neighborhoods, project boundaries, water supplier service areas, etc.) based on data from a *source* dataset containing the characteristic data of interest (e.g., census data, CalEnvrioScreen scores, etc.), especially when the boundaries of the *source* and *target* areas overlap but don't necessarily align with each other. It also provides some brief background on the various types of data available from the U.S Census Bureau, and links to a few places to find more in-depth information.\n\nThis particular example estimates demographic characteristics of community water systems in the Sacramento County area (the *target* dataset). It uses the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package [@tidycensus] to access selected demographic data from the U.S. Census Bureau (the *source* dataset) for census units whose spatial extent covers those water systems' service areas, then uses the [`sf`](https://r-spatial.github.io/sf/) package [@sf] package (for working with spatial data) and the [`tidyverse`](https://www.tidyverse.org/) collection of packages (for general data cleaning and transformation) to estimate some demographic characteristics of each water system based on that census data. It also uses the [`areal`](https://chris-prener.github.io/areal/) R package [@areal] to check some of the results, and as general guidance on the principles and techniques for implementing areal interpolation.\n\nThis example is just intended to be a simplified demonstration of a possible workflow. For a real analysis, additional steps and considerations -- that may not be covered here -- may be needed to deal with data inconsistencies (e.g., missing or incomplete data), required level of precision and acceptable assumptions (e.g. more fine-grained datasets or more sophisticated techniques could be used to estimate/model population distributions), or other project-specific issues that might arise.\n\n## Setup {#sec-setup}\n\nThe code block below loads required packages for this analysis, and sets some user-defined options and defaults. If they aren't already installed on your computer, you can install them with the R command `install.packages('package-name')` (and replace `package-name` with the name of the package you want to install).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# packages ----\nlibrary(tidycensus)\nlibrary(tigris)\nlibrary(tidyverse)\nlibrary(sf)\nlibrary(areal)\nlibrary(janitor)\nlibrary(here)\nlibrary(units)\n# library(Polychrome)\nlibrary(knitr)\nlibrary(kableExtra)\nlibrary(tmap)\nlibrary(patchwork)\nlibrary(scales)\nlibrary(digest)\nlibrary(mapview)\n\n# conflicts ----\nlibrary(conflicted)\nconflicts_prefer(dplyr::filter)\n\n# options ----\noptions(scipen = 999) # turn off scientific notation\noptions(tigris_use_cache = TRUE) # use data caching for tigris\n\n# reference system ----\ncrs_projected <- 3310 # set a common projected coordinate reference system to be used throughout this analysis - see: https://epsg.io/3310\n```\n:::\n\n\n## Census Data Overview {#sec-census-overview}\n\nThis section provides some brief background on the various types of data available from the U.S. Census Bureau (a later section - @sec-census-access - demonstrates how to retrieve data from the U.S. Census Bureau using the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package). Most of the information covered here comes from the book [Analyzing US Census Data: Methods, Maps, and Models in R](https://walker-data.com/census-r/index.html), which is a great source of information if you'd like more detail about any of the topics below [@walker2023].\n\n::: callout-note\nIf you're already familiar with Census data and want to skip this overview, go directly to the next section: @sec-system-boundaries\n:::\n\nDifferent census products/surveys contain data on different variables, at different geographic scales, over varying periods of time, and with varying levels of certainty. Therefore, there are a number of judgement calls to make when determining which type of census data to use for an analysis -- e.g., which data product to use (Decennial Census or American Community Survey), which geographic scale to use (e.g., Block, Block Group, Tract, etc.), what time frame to use, which variables to assess, etc.\n\nMore detailed information about U.S. Census Bureau's data products and other topics mentioned below is available [here](https://walker-data.com/census-r/the-united-states-census-and-the-r-programming-language.html#the-united-states-census-and-the-r-programming-language).\n\n### Census Unit Geography / Hierarchy {#sec-census-hierarchy}\n\nPublicly available datasets from the U.S Census Bureau generally consist of individual survey responses aggregated to defined census units (e.g., census tracts) that cover varying geographic scales. Some of these units are nested and can be neatly aggregated (e.g., each census tract is composed of a collection of block groups, and each block group is composed of a collection of blocks), while other census units are outside this hierarchy (e.g., Zip Code Tabulation Areas don't coincide with any other census unit). @fig-census-hierarchies shows the relationship of all of the various census units.\n\nCommonly used census statistical units like tracts and block groups have target population size ranges, and can be adjusted every 10 years (with the decennial census) based on population changes. For example, all ACS 5-year datasets prior to 2020 use the 2010 boundaries for tracts, block groups, and blocks, and all ACS 5-year datasets from [2020 onward](https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2020/geography-changes.html) (presumably through 2029) use the 2020 boundaries for those units. [Census tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13) are generally around 4,000 people, with a range from about 1,200 to 8,000, and [block groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4) generally contain 600 to 3,000 people. [Blocks](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_5) are the smallest census units, and are \"areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads\". For example, a census block may be \"a city block bounded on all sides by streets\", while \"blocks in suburban and rural areas may be larger, more irregular in shape, and bounded by a variety of features, such as roads, streams, and transmission lines\".\n\n::: callout-caution\nCensus boundaries can change over time. Commonly used statistical units like tracts, block groups, and blocks tend to be revised every 10 years (with the decennial census), so it's important to use a census boundary dataset that matches the version of the census demographic data you're retrieving; otherwise, the demographic data may not match geographic areas in your boundary dataset. In some cases, a census unit that exists in a given year of the census data may not exist at all in a different year's dataset, because census units can be split or merged when boundaries are revised.\n\nFor more information, see [here](https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_geography_handbook_2020_ch02.pdf) or [here](https://www.census.gov/programs-surveys/acs/geography-acs/geography-boundaries-by-year.html) or [here](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_6) or [here](https://www.census.gov/data/academy/data-gems/2021/compare-2020-census-and-2010-census-redistricting-data.html).\n:::\n\nFor a list of the different geographic units available for each of the different census products/surveys (see @sec-census-datasets) that can be accessed via the `tidycensus` package, go [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus).\n\n![Census Unit Hierarchies](https://walker-data.com/census-r/img/screenshots/census-hierarchies.png){#fig-census-hierarchies}\n\n### Census Datasets / Surveys {#sec-census-datasets}\n\nThe Decennial Census is conducted every 10 years, and is intended to provide a complete count of the US population and assist with political redistricting. As a result, it collects a relatively limited set of basic demographic data, but (should) provide a high degree of precision (i.e., in general it should provide exact counts). It is available for geographic units down to the census block (the smallest census unit available -- see @sec-census-hierarchy). For information about existing and planned future releases of 2020 census data products, go [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html).\n\nThe American Community Survey (ACS) provides a much larger array of demographic information than the Decennial Census, and is updated more frequently. The ACS is based on a sample of the population (rather than a count of the entire population, as in the Decennial Census), so it represents estimated values rather than precise counts; therefore, each data point is available as an estimate (typically labeled with an \"E\" in census variable codes, which are discussed in @sec-census-variables ) along with an associated margin of error (typically labeled with \"M\" or \"MOE\" in census variable codes) around its estimated value.\n\nThe ACS is available in two formats. The 5-year ACS is a rolling average of 5 years of data (e.g., the 2021 5-year ACS dataset is an average of the ACS data from 2017 through 2021), and is generally available for geographic units down to the census block group (though some 5-year ACS data may only be available at less granular levels). The 1-year ACS provides data for a single year, and is only available for geographies with population greater than 65,000 (e.g., large cities and counties). Therefore, only the 5-year ACS will be useful for any analysis at a relatively fine scale (e.g., anything that requires data at or more detailed than the census tract level, or any analysis that considers smaller counties/cities -- by definition, census tracts always contain significantly fewer than 65,000 people).\n\nIn addition to the Decennial Census and ACS data, a number of other census data products/surveys are also available. For example, see the `censusapi` R package ([here](https://github.com/hrecht/censusapi) or [here](https://www.hrecht.com/censusapi/index.html)) for access to over 300 census API endpoints. For historical census data, see the discussion [here](https://walker-data.com/census-r/other-census-and-government-data-resources.html?q=API%20endpoint#other-census-and-government-data-resources) on using NHGIS, IPUMS, and the `ipumsr` package.\n\n### Census Variables / Codes {#sec-census-variables}\n\nEach census product collects data for many different demographic variables, and each variable is generally associated with an identifier code. In order to access census data programmatically, you often need to know the code associated with each variable of interest. When determining which variables to use, you need to consider what census product contains those variables (see @sec-census-datasets) and how they differ in terms of time frame, precision, spatial granularity (see @sec-census-hierarchy), etc.\n\nThe `tidycensus` package offers a convenient generic way to search for variables across different census products using the `load_variables()` function, as described [here](https://walker-data.com/tidycensus/articles/basic-usage.html#searching-for-variables).\n\nThe following websites may also be helpful for exploring the various census data products and finding the variable names and codes they contain:\n\n- Census Reporter (for ACS data): (especially )\n\n- Census Bureau's list of variable codes, e.g.:\n\n - 2020 Census codes: \n\n - 2022 ACS 5 year codes: \n\n- Census Bureau's data interface (for Decennial Census and ACS, and other census datasets): \n\n- National Historical Geographic Information System (NHGIS) (for ACS data and historical decennial Census data): \n\n## Target Data Boundaries (Water Systems) {#sec-system-boundaries}\n\nIn this section, we'll get the service area boundaries for Community Water Systems within the Sacramento County area. This will serve as the *target* dataset – i.e., the set of areas which we'll be estimating the characteristics of – and will also be used to specifying what census data we want to retrieve. We'll also get a dataset of county boundaries which overlap the water service areas in this study, which can also help with specifying what census data to access and/or with making maps and visualizations.\n\n### Read Water System Data\n\nIn this case, we'll get the water system dataset from a shapefile that's saved locally, then transform that dataset into a common coordinate reference system for mapping and analysis (which is defined above in the variable `crs_projected`).\n\nThis water system dataset comes from the [California Drinking Water System Area Boundaries dataset](https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc). For this example, the dataset has been pre-filtered for systems within Sacramento County (by selecting records where the `COUNTY` field is \"SACRAMENTO\") and for Community Water Systems (by selecting records where the `STATE_CLAS` field is \"COMMUNITY\"). Some un-needed fields have also been dropped, remaining fields have been re-orderd.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_systems_sac <- st_read(here('02_data_input', \n 'water_supplier_boundaries_sac', \n 'System_Area_Boundary_Layer_Sac.shp')) %>% \n st_transform(crs_projected) # transform to common coordinate system\n```\n:::\n\n\nWe can use the `glimpse` function (below) to take get a sense of what type of information is available in the water system dataset and how it's structured.\n\nNote that this dataset already includes a `POPULATION` variable that indicates the population served by each water system. However, for this analysis we'll be making our own estimate of the population within each system's service area based on U.S. Census Bureau data and the spatial representation of the system boundaries. I don't know exactly how the `POPULATION` variable was derived in this dataset, and it likely will not exactly match the population estimates from this analysis, but may serve as a useful check to make sure our estimates are reasonable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_systems_sac)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 12\n$ WATER_SY_1 \"HOOD WATER MAINTENCE DIST [SWS]\", \"MC CLELLAN MHP\", \"MAGNO…\n$ WATER_SYST \"CA3400101\", \"CA3400179\", \"CA3400130\", \"CA3400135\", \"CA3400…\n$ GLOBALID \"{36268DB3-9DB2-4305-A85A-2C3A85F20F34}\", \"{E3BF3C3E-D516-4…\n$ BOUNDARY_T \"Water Service Area\", \"Water Service Area\", \"Water Service …\n$ OWNER_TYPE \"L\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\",…\n$ COUNTY \"SACRAMENTO\", \"SACRAMENTO\", \"SACRAMENTO\", \"SACRAMENTO\", \"SA…\n$ REGULATING \"LPA64 - SACRAMENTO COUNTY\", \"LPA64 - SACRAMENTO COUNTY\", \"…\n$ FEDERAL_CL \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUN…\n$ STATE_CLAS \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUN…\n$ SERVICE_CO 82, 199, 34, 64, 128, 83, 28, 50, 164, 5684, 14798, 115, 33…\n$ POPULATION 100, 700, 40, 150, 256, 150, 32, 100, 350, 18005, 44928, 20…\n$ geometry MULTIPOLYGON (((-132703 403..., MULTIPOLYGON (…\n```\n\n\n:::\n:::\n\n\n#### Alternative Data Retrieval Method\n\nReading in data from a shapefile is shown above because it's likely one of the more common ways that users will access their *target* boundary data. However, depending on the dataset, there may be other ways to access the data. For example, the code chunk below demonstrates an alternative -- using the [`arcgislayers`](https://r.esri.com/arcgislayers/index.html) package [@arcgislayers] -- that connects directly to the source dataset (to retrieve the most recent version) and applies the filters needed to reproduce the dataset in the `System_Area_Boundary_Layer_Sac.shp` file.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load arcgislayers package (see: https://r.esri.com/arcgislayers/index.html)\ninstall.packages('pak') # only needed if the pak package is not already installed\npak::pkg_install(\"R-ArcGIS/arcgislayers\", dependencies = TRUE)\n\nlibrary(arcgislayers)\n\n# define link to data source\nurl_feature <- 'https://gispublic.waterboards.ca.gov/portalserver/rest/services/Drinking_Water/California_Drinking_Water_System_Area_Boundaries/FeatureServer/0'\n\n# connect to data source\nwater_systems_feature_layer <- arc_open(url_feature)\n\n# download and filter data from source\nwater_systems_sac <- arc_select(\n water_systems_feature_layer,\n # apply filters\n where = \"COUNTY = 'SACRAMENTO' AND STATE_CLASSIFICATION = 'COMMUNITY'\",\n # select fields\n fields = c('WATER_SYSTEM_NAME', 'WATER_SYSTEM_NUMBER', 'GLOBALID',\n 'BOUNDARY_TYPE', 'OWNER_TYPE_CODE', 'COUNTY',\n 'REGULATING_AGENCY', 'FEDERAL_CLASSIFICATION', 'STATE_CLASSIFICATION',\n 'SERVICE_CONNECTIONS', 'POPULATION')) %>%\n # transform to common coordinate system\n st_transform(crs_projected) %>%\n # rename fields to match names from the shapefile (which automatically truncates field names)\n rename(WATER_SY_1 = WATER_SYSTEM_NAME,\n WATER_SYST = WATER_SYSTEM_NUMBER,\n BOUNDARY_T = BOUNDARY_TYPE,\n OWNER_TYPE = OWNER_TYPE_CODE,\n REGULATING = REGULATING_AGENCY,\n FEDERAL_CL = FEDERAL_CLASSIFICATION,\n STATE_CLAS = STATE_CLASSIFICATION,\n SERVICE_CO = SERVICE_CONNECTIONS)\n```\n:::\n\n\n### Get County Boundaries {#sec-county-boundaries}\n\nWhen accessing census data using the `tidycensus` R package as shown below (in @sec-census-access), it's often useful (though not strictly required) to know which counties overlap the target dataset (note that, even though the dataset is filtered for systems in Sacramento county, there are some systems whose boundaries extend into neighboring counties). County boundaries may also be useful for making maps in later stages of the analysis. We can get a dataset of county boundaries in California from the [TIGER dataset](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html), which can be accessed with R using the [`tigris`](https://github.com/walkerke/tigris) R package [@tigris].\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncounties_ca <- counties(state = 'CA', \n cb = TRUE) %>% # simplified\n st_transform(crs_projected) # transform to common coordinate system\n```\n:::\n\n\nThen, we can get a list of counties that overlap with the boundaries of the Sacramento area community water systems obtained above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncounties_overlap <- counties_ca %>% \n st_filter(water_systems_sac, \n .predicate = st_overlaps)\n\ncounties_list <- counties_overlap %>% pull(NAME)\n```\n:::\n\n\nThe counties in the `counties_list` variable are: San Joaquin, Yolo, Placer, Sacramento.\n\n### Plot Target Data\n\n@fig-suppliers-counties shows the water systems and county boundaries in an interactive map.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmapview(counties_overlap, \n alpha.regions = 0, \n zcol = 'NAME', \n layer.name = 'County', \n legend = FALSE) + \n mapview(water_systems_sac, \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE)\n```\n\n::: {#fig-suppliers-counties .cell-output-display}\n\n```{=html}\n
\n\n```\n\n\nSelected water systems (with county boundaries for reference).\n:::\n:::\n\n\n## Accessing Census Data {#sec-census-access}\n\nThe following sections demonstrate how to retrieve census data from the Decennial Census and the ACS using the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package.\n\nIn order to use the `tidycensus` R package, you'll need to obtain a personal API key from the US Census Bureau (which is free and available to anyone) by signing up here: . Once you have your API key, you'll need to register it in R by entering the command `census_api_key(key = \"YOUR API KEY\", install = TRUE)` in the console. Note that the `install = TRUE` argument means that the key is saved for all future R sessions, so you'll only need to run that command once on your computer (rather than including it in your scripts). Alternatively, you could save your key to an environment variable and retrieve it using `Sys.getenv()`. Either way will help you avoid the possibility of entering your API key into any scripts that could be shared publicly.\n\n::: callout-caution\nBecause the boundaries of census units (e.g., tracts, block groups, blocks, etc) can change over time, it's important to make sure that the version (year) of the census data you're retrieving matches the version of the census boundary dataset you're using. The methods shown below retrieve the census boundary dataset together with the census demographic data, which ensures that this won't be a potential problem. However, if you use a different workflow that retrieves the geographic boundaries and demographic data via separate processes, you should ensure that the versions are consistent.\n:::\n\n### Decennial Census {#sec-census-access-decennial}\n\nThis section retrieves census data from the Decennial Census, using the `get_decennial` function from the `tidycensus` package. As of this writing, the most recent version of the decennial census data available is from 2020, and we can set that as a variable below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# set year\ndecennial_year <- 2020\n```\n:::\n\n\nNext, we can define the list of demographic variables we'd like to retrieve tabular data for, by saving the census variables we want in the `census_vars_decennial` object (see @sec-census-variables for more information about how to discover variables of interest and find their associated codes). Note that here we're providing descriptive names associated with each variable code, which makes the data easier to work with later, but isn't strictly necessary (i.e., you could just supply the variable codes alone).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# define variables to pull from the decennial census\ncensus_vars_decennial <- c(\n 'population_hispanic_or_latino_count' = 'P2_002N', # Total Hispanic or Latino\n 'population_white_count' = 'P2_005N', # White (Not Hispanic or Latino)\n 'population_black_or_african_american_count' = 'P2_006N', # Black or African American (Not Hispanic or Latino)\n 'population_native_american_or_alaska_native_count' = 'P2_007N', # American Indian and Alaska Native (Not Hispanic or Latino)\n 'population_asian_count' = 'P2_008N', # Asian (Not Hispanic or Latino)\n 'population_pacific_islander_count' = 'P2_009N', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)\n 'population_other_count' = 'P2_010N', # Some other race (Not Hispanic or Latino)\n 'population_multiple_count' = 'P2_011N', # Two or more races (Not Hispanic or Latino)\n 'population_total_count' = 'P2_001N'\n)\n```\n:::\n\n\nThen, we can create an object that we can use to filter our request to the census API so that it will only return census units that overlap with our target areas (the object will be passed to the `filter_by` argument of the `get_decennial` function below). Note that this isn't strictly necessary (you could also apply the filter after making the API request), but may helpful to speed the query and reduce memory usage, especially in the case of large queries.\n\n::: callout-note\nAt the time of this writing, the `filter_by` argument of the tidycensus `get_decennial` function is fairly new, and not yet included in the official documentation.\n\nAlso, the `filter_by` argument is optional, and only appears to accept a simple features (sf) object with a single row / feature (e.g., a single water system), and will not accept an sf object with multiple rows / features. The process below attempts to work around this constraint by joining all of the selected water systems into a single multi-part polygon (i.e., an sf object with a single row). However, if you only want to retrieve data for census units that overlap a single target area (e.g., a single water system), you can skip this step.\n:::\n\n\n::: {.cell}\n\n```{#lst-filter_obj .r .cell-code lst-cap=\"Create object for filtering the API query\"}\nwater_systems_filter <- water_systems_sac %>% \n st_union() %>% \n st_as_sf()\n```\n:::\n\n\nFinally, we can make the data request, using the `get_decennial` function, which accepts several arguments that specify exactly what data to return.\n\nFor this example we're getting data at the 'Block' level (with the `geography = 'block'` argument) for the demographic variables defined above in the `census_vars_decennial` object (which is passed to the `variables` argument). As noted above, block-level data is the most granular level of spatial data available, and should provide the best results when estimating demographics for areas whose boundaries don't align with census unit boundaries. However, depending on the use case, it may require too much time and computational resources to use the most granular spatial data, and may not be necessary to obtain a reasonable estimate. Also, keep in mind that block-level data may not be available for all variables, and some variables may only be available at less granular spatial scales (like block groups or tracts).\n\nIn addition to the tabular data associated with the demographic variables in our list, we'll also get the spatial data -- i.e., the boundaries of the census blocks -- by setting the `geometry = TRUE` argument. When we do this, the tabular demographic data is pre-joined to the spatial data, so the API request returns a single dataset with both the spatial and attribute (demographic) data combined.\n\n::: callout-note\nThe `tidycensus` package generally returns the Census Bureau's [cartographic boundary shapefiles](https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html) by default (as opposed to the [core TIGER/Line shapefiles](https://www.census.gov/geo/maps-data/data/tiger-line.html), which is the default format returned by the `tigris` R package). The default cartographic boundary shapefiles are pre-clipped to the US coastline, and are smaller/faster to process (alternatively you can use `cb = FALSE` to get the core TIGER/Line data) (see [here](https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html#better-cartography-with-spatial-overlay)). So the default spatial data returned by `tidycensus` may be somewhat different than the default spatial data returned by the `tigris` package, but in general I find it's best to use the default `tidycensus` spatial data.\n:::\n\n::: callout-warning\nAt the block level, it appears that `tidycensus` only returns the more detailed core TIGER/Line shapefiles (i.e., they are identical to the default block-level geographic data returned by `tigris`). In some cases, that can create minor inconsistencies when working with both blocks and block groups and using the default geographies.\n:::\n\nWe also narrow down the search parameters geographically by specifying the state (with `state = 'CA'`) and counties (`county = counties_list`) we're seeking data for.\n\n::: callout-note\nSupplying a list of counties may not be strictly necessary, especially in cases where you supply the optional `filter_by` argument. However, especially when working with granular data like blocks, supplying the county argument seems to greatly speed the API request.\n:::\n\nAlso, while by default the `tidycensus` package returns data in long/tidy format, we're getting the data in wide format for this example (by specifying `output = 'wide'`) because it'll be easier to work with for the interpolation method described below to estimate demographics for non-census geographies.\n\n\n::: {.cell}\n\n```{#lst-get_decennial .r .cell-code lst-cap=\"Retrieve decennial census data\"}\n# get census data\ncensus_data_decennial <- get_decennial(geography = 'block', # can be 'block', 'block group', 'tract', 'county', etc.\n state = 'CA', \n county = counties_list,\n filter_by = water_systems_filter,\n year = decennial_year,\n variables = census_vars_decennial,\n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) # convert to common coordinate system\n```\n:::\n\n\nThe output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the population of each racial/ethnic group is reported in a separate column. Here's a view of the contents and structure of the Decennial Census data that's returned:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_decennial)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 17,745\nColumns: 12\n$ GEOID \"060670019003011\", \"…\n$ NAME \"Block 3011, Block G…\n$ population_hispanic_or_latino_count 4, 6, 8, 11, 1, 14, …\n$ population_white_count 20, 4, 167, 70, 86, …\n$ population_black_or_african_american_count 2, 2, 0, 8, 9, 18, 0…\n$ population_native_american_or_alaska_native_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_asian_count 19, 5, 2, 1, 23, 8, …\n$ population_pacific_islander_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_other_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_multiple_count 8, 3, 4, 10, 5, 10, …\n$ population_total_count 53, 20, 181, 100, 12…\n$ geometry POLYGON ((-1…\n```\n\n\n:::\n:::\n\n\n### American Community Survey (ACS) {#sec-census-access-acs}\n\nTo get data from the ACS, you can use the `get_acs()` function, which is very similar to the `get_decennial()` function used above. As of this writing, the most recent version of the 5-year ACS data available is the 2018-2022 ACS, and we can set that as a variable below (which makes it easier to update this document in future years).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# set year\nacs_year <- 2022\n```\n:::\n\n\nHowever, since the ACS data contains data on a much broader set of socio-economic metrics, the requested data includes a greatly expanded list of variables, defined in the `census_vars_acs` object (see @sec-census-variables for more information about how to discover variables of interest and find their associated codes). As above, we can provide descriptive names associated with each variable code, which makes the data easier to work with later, but isn't strictly necessary (i.e., you could just supply the variable codes alone).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# define variables to pull from the ACS\ncensus_vars_acs <- c(\n # --- population variables ---\n 'population_total_count' = 'B01003_001',\n 'population_hispanic_or_latino_count' = 'B03002_012', # Total Hispanic or Latino\n 'population_white_count' = 'B03002_003', # White (Not Hispanic or Latino)\n 'population_black_or_african_american_count' = 'B03002_004', # Black or African American (Not Hispanic or Latino)\n 'population_native_american_or_alaska_native_count' = 'B03002_005', # American Indian and Alaska Native (Not Hispanic or Latino)\n 'population_asian_count' = 'B03002_006', # Asian (Not Hispanic or Latino)\n 'population_pacific_islander_count' = 'B03002_007', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)\n 'population_other_count' = 'B03002_008', # Some other race (Not Hispanic or Latino)\n 'population_multiple_count' = 'B03002_009', # Two or more races (Not Hispanic or Latino)\n \n # --- poverty variables ---\n 'poverty_total_assessed_count' = 'B17021_001', # also available from 'B17020_001' (at the tract level only). Total population for whom poverty status is determined. Poverty status was determined for all people except institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years old. These groups were excluded from the numerator and denominator when calculating poverty rates.\n 'poverty_below_count' = 'B17021_002', # also available from 'B17020_002' (at the tract level only). Population whose income in the past 12 months is below federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.\n 'poverty_above_count' = 'B17021_019', # also available from 'B17020_010' (at the tract level only). Population whose income in the past 12 months is at or above federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.\n \n # --- household variables ---\n 'households_count' = 'B19001_001', # also available from variable 'B19053_001'. A household includes all the people who occupy a housing unit - a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied. People not living in households are classified as living in group quarters.\n 'average_household_size' = 'B25010_001', # A measure obtained by dividing the number of people living in occupied housing units by the total number of occupied housing units. This measure is rounded to the nearest hundredth.\n \n # --- household income variables ---\n 'median_household_income' = 'B19013_001', # also available from 'B19019_001' (at the tract level only). Income in the past 12 months is the sum of wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income.\n 'households_income_below_10k_count' = 'B19001_002', # count of households with income below $10,000 \n 'households_income_10k_15k_count' = 'B19001_003', # count of households with income $10,000 to $15,000 \n 'households_income_15k_20k_count' = 'B19001_004', \n 'households_income_20k_25k_count' = 'B19001_005', \n 'households_income_25k_30k_count' = 'B19001_006', \n 'households_income_30k_35k_count' = 'B19001_007', \n 'households_income_35k_40k_count' = 'B19001_008', \n 'households_income_40k_45k_count' = 'B19001_009', \n 'households_income_45k_50k_count' = 'B19001_010', \n 'households_income_50k_60k_count' = 'B19001_011', \n 'households_income_60k_75k_count' = 'B19001_012', \n 'households_income_75k_100k_count' = 'B19001_013', \n 'households_income_100k_125k_count' = 'B19001_014', \n 'households_income_125k_150k_count' = 'B19001_015', \n 'households_income_150k_200k_count' = 'B19001_016',\n 'households_income_above_200k_count' = 'B19001_017', # count of households with income above $200,000\n\n # --- housing costs variables (% of household income) ---\n # Housing Costs as a Percentage of Household Income in the past 12 months - NOTE: THIS TABLE IS NEW FOR THE 2022 ACS, AND WON'T BE AVAILABLE FOR PREVIOUS YEARS - Table B25140 shows the count of households paying more than 30% of their income towards housing costs broken out by three tenure categories (owned with a mortgage, owned without a mortgage, and rented). The table also shows the number of households paying more than 50% of their income toward housing costs.\n # 'households_count' = 'B25140_001', \n 'households_mortgage_total_count' = 'B25140_002',\n 'households_mortgage_over30pct_count' = 'B25140_003',\n 'households_mortgage_over50pct_count' = 'B25140_004',\n 'households_no_mortgage_total_count' = 'B25140_006',\n 'households_no_mortgage_over30pct_count' = 'B25140_007',\n 'households_no_mortgage_over50pct_count' = 'B25140_008',\n 'households_rent_total_count' = 'B25140_010',\n 'households_rent_over30pct_count' = 'B25140_011',\n 'households_rent_over50pct_count' = 'B25140_012',\n \n # --- other income / economic variables ---\n 'per_capita_income' = 'B19301_001' # note: per capita income by race (at block group level) available in table B19301I\n)\n```\n:::\n\n\nFinally, we can make the data request, using the `get_acs` function, which is very similar to the `get_decennial` function described above ( @sec-census-access-decennial). However, for this example we're getting data at the 'Block Group' level (with the `geography = 'block group'` argument), which is the most granular level of spatial data available for ACS data. But, keep in mind that block group-level data may not be available for all variables, and some variables may only be available at less granular spatial scales (like tracts). Note that the `water_systems_filter` object supplied to the `filter_by` argument was created above in @lst-filter_obj.\n\n\n::: {.cell}\n\n```{#lst-get_acs .r .cell-code lst-cap=\"Retrieve ACS data\"}\n# get census data\ncensus_data_acs <- get_acs(geography = 'block group',\n state = 'CA', \n county = counties_list,\n filter_by = water_systems_filter,\n year = acs_year,\n survey = 'acs5',\n variables = census_vars_acs, \n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) # convert to common coordinate system\n```\n:::\n\n\nAs above, the output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the each demographic variable is reported in a separate column. Here's a view of the contents and structure of the 2022 5-year ACS data that's returned (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_acs[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 1,054\nColumns: 21\n$ GEOID \"060670081451\", \"06…\n$ NAME \"Block Group 1; Cen…\n$ population_total_countE 1768, 1881, 1098, 2…\n$ population_total_countM 520, 585, 395, 583,…\n$ population_hispanic_or_latino_countE 38, 327, 376, 782, …\n$ population_hispanic_or_latino_countM 59, 298, 280, 315, …\n$ population_white_countE 1627, 1337, 293, 18…\n$ population_white_countM 521, 475, 191, 460,…\n$ population_black_or_african_american_countE 0, 1, 272, 26, 351,…\n$ population_black_or_african_american_countM 13, 3, 251, 38, 334…\n$ population_native_american_or_alaska_native_countE 41, 0, 0, 26, 0, 0,…\n$ population_native_american_or_alaska_native_countM 58, 13, 13, 42, 13,…\n$ population_asian_countE 45, 0, 105, 58, 144…\n$ population_asian_countM 71, 13, 116, 66, 18…\n$ population_pacific_islander_countE 0, 98, 0, 0, 27, 13…\n$ population_pacific_islander_countM 13, 98, 13, 13, 50,…\n$ population_other_countE 0, 0, 39, 0, 0, 0, …\n$ population_other_countM 13, 13, 63, 13, 13,…\n$ population_multiple_countE 17, 118, 13, 39, 15…\n$ population_multiple_countM 27, 125, 20, 57, 25…\n$ geometry POLYGON ((-…\n```\n\n\n:::\n:::\n\n\nNote that the dataset that's returned includes fields corresponding to Margin of Error (MOE) for each variable we've requested (these are the fields that end with two digits and an M -- e.g., \"001M\"), since, as noted above in @sec-census-datasets , the ACS is based on a sample of the population and reports estimated values.\n\n::: callout-tip\nIt is possible to calculate MOEs for derived estimates – e.g., when aggregating groups of census units – and in many cases it may be worthwhile to do that to provide extra context to the data. However, it may be difficult to do for more complex aggregations, such as the areal interpolation shown below. For guidance on how calculate MOEs for some types of derived estimates, see [this document](https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch08.pdf).\n\n`tidycensus` also has functions for calculating derives margins of error based on Census-supplied formulas, including [`moe_sum()`](https://walker-data.com/tidycensus/reference/moe_sum.html), [`moe_product()`](https://walker-data.com/tidycensus/reference/moe_product.html), [`moe_ratio()`](https://walker-data.com/tidycensus/reference/moe_ratio.html), and [`moe_prop()`](https://walker-data.com/tidycensus/reference/moe_prop.html).\n:::\n\nBecause we won't be incorporating those MOEs into the analysis below, we can drop them for this example, then clean up the field names.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# drop MOE fields\ncensus_data_acs <- census_data_acs %>% \n select(-matches('M$')) # the $ specifies \"ends with\"\n\n# clean names\nnames(census_data_acs) <- names(census_data_acs) %>% \n str_remove('E$') %>% # remove 'E' (estimate) from field names\n str_replace('NAM', 'NAME') # add 'E' back to NAME field\n```\n:::\n\n\nHere's a view of the contents and structure of the revised 2022 5-year ACS dataset (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_acs[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 1,054\nColumns: 21\n$ GEOID \"060670081451\", \"060…\n$ NAME \"Block Group 1; Cens…\n$ population_total_count 1768, 1881, 1098, 27…\n$ population_hispanic_or_latino_count 38, 327, 376, 782, 3…\n$ population_white_count 1627, 1337, 293, 181…\n$ population_black_or_african_american_count 0, 1, 272, 26, 351, …\n$ population_native_american_or_alaska_native_count 41, 0, 0, 26, 0, 0, …\n$ population_asian_count 45, 0, 105, 58, 144,…\n$ population_pacific_islander_count 0, 98, 0, 0, 27, 13,…\n$ population_other_count 0, 0, 39, 0, 0, 0, 0…\n$ population_multiple_count 17, 118, 13, 39, 15,…\n$ poverty_total_assessed_count 1768, 1847, 1098, 27…\n$ poverty_below_count 101, 328, 272, 116, …\n$ poverty_above_count 1667, 1519, 826, 263…\n$ households_count 680, 718, 405, 905, …\n$ average_household_size 2.59, 2.62, 2.71, 2.…\n$ median_household_income 123500, 66768, 56216…\n$ households_income_below_10k_count 18, 47, 10, 22, 6, 1…\n$ households_income_10k_15k_count 0, 0, 24, 0, 15, 231…\n$ households_income_15k_20k_count 0, 13, 18, 0, 51, 12…\n$ geometry POLYGON ((-1…\n```\n\n\n:::\n:::\n\n\nFor further analysis, we may want to get the statewide data as a baseline for comparison (this could also be done for other scales, like the county level). We can use a similar process to get that data and clean/format it to match the more detailed data obtained above. Note that in this case we're also using the 5-year ACS (even though the 1-year ACS is also available at the statewide level, and would provide more up-to-date data) so that the statewide data will be directly comparable to the block group level data obtained above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_acs_state <- get_acs(geography = 'state',\n state = 'CA', \n year = acs_year,\n survey = 'acs5',\n variables = census_vars_acs, \n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) %>% # convert to common coordinate system\n select(-matches('M$')) %>% # the $ specifies \"ends with\"\n # clean names (note this is a little different than the way we renamed fields above, either works)\n rename_with(.fn = ~ str_remove(., # remove 'E' (estimate) from field names\n pattern = 'E$')) %>% \n rename_with(.fn = ~ str_replace(., # add 'E' back to NAME field\n pattern = 'NAM', \n replacement = 'NAME'))\n```\n:::\n\n\n### Plot Census & Supplier Data {#sec-census-plot}\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsystem_plot <- 'SACRAMENTO SUBURBAN WATER DISTRICT'\n```\n:::\n\n\n@fig-suppliers-census-map shows the 2022 5-year ACS census units that overlap with one of the water systems (Sacramento Suburban Water District) that we'll compute demographics for below (plotting the census units that overlap all systems tends to be slow in this format).\n\n::: {#fig-suppliers-census-map}\n\n::: {.cell}\n\n```{.r .cell-code}\n# label: fig-suppliers-census-map\n# fig-cap: \"Water system (filled polygon) and boundaries of census units (light blue) that will be used to estimate water system demographics.\"\n\nmapview(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot), \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE) +\n mapview(census_data_acs %>% \n st_filter(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot)), \n alpha.regions = 0, \n color = 'cyan', \n lwd = 1.3, label = 'NAME', \n layer.name = 'ACS Data', \n legend = FALSE) # zcol = 'NAME'\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\nWater system Sacramento Suburban Water District (filled polygon) and boundaries of census units (light blue) that will be used to estimate water system demographics.\n:::\n\n## Compute Water System Demographics {#sec-estimate-demographics}\n\nNow we can perform the calculations to estimate demographic characteristics for our *target* areas (water system service boundaries in the Sacramento County area) from our *source* demographic dataset (the census data we obtained above). For this example, we'll use the 2022 5-year ACS data that we retrieved above (which is saved in the `census_data_acs` variable) as our source of demographic data, and we'll estimate the following for each water system's service area:\n\n- Population of each racial/ethnic group (using the racial/ethnic categories defined in the census dataset), and each racial/ethnic group's portion of the total service area population\n- Socio-economic variables like poverty rate, median household income, income distributions, and per capita income\n\nThere are multiple ways this estimation can be done. For this example, we'll employ a three step strategy:\n\n1. Estimate values for count-based variables (typically referred to as 'extensive' data types) -- e.g., total population, popultion by race/ethnicity, population above / below poverty rate, households by income bracket -- for overlapping census unit, using areal interpolation. This is essentially an area weighted average, which estimates how much of each *source* unit's (census unit) count applies to the *target* area (a given water service area), based on the portion of its area that overlaps that target area -- for more information about the process, see [this documentation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html) from the `areal` R package. For example, for a census unit that partially overlaps a service area, only a fraction of its count for a given variable will be applied to that service area; for a census unit that completely overlaps a service area, the full count for that variable will be applied to the service area.\n\n The major simplifying assumption of this approach is that the population or count-based variable of interest are evenly distributed within each unit in the *source* data. For example, in this case we're assuming that population (including the total population and the population of each racial/ethic group), households of each income bracket, populations above / below the poverty rate, etc. are evenly distributed within each census block group.\n\n::: callout-tip\nWhile this section uses the block group-level count data from the 5-year ACS, there may be cases where it could be useful or necessary to use more granular block-level population data from the decennial census to estimate population densities and distributions within larger census units, like block groups and tracts. This could especially be the case when estimating characteristics for small areas in rural environments. See @sec-small-area-estimates and/or @sec-detailed-pop-estimates for more information.\n:::\n\n2. Using the estimated count data (populations, households, etc), compute weighted values for variables that describe those populations, using the associated count data as a weighting factor (e.g., population-weighted values for population based data, or household-weighted values for household-based data) -- these variables are typically referred to as 'intensive' data types.\n\n::: callout-tip\nAlthough it's possible to use areal interpolation to aggregate these variables as well, the multi-step approach described here can be useful because we know (from the population / household count data) that population densities differ between census units. Since we have a reasonable estimate of the count data (population, households, etc) within each census unit, using a population or household weighted average likely will yield more accurate results than a simple area-weighted average for these variables. For example, for per capita income, we can use the estimated population counts to produce a population weighted average per capita income (rather than an area weighted average per capita income, which is likely less meaningful as it over-weights large census areas with lower population densities). Areal interpolation may be more useful for cases where we generally have no other information about how density varies between the source polygons (unless significantly more effort is invested, such as looking at aerial imagery data)\n:::\n\n3. Aggregate interpolated values at the water system level.\n\n### Prepare Census Data\n\nNote that we already transformed the 2022 5-year ACS dataset into the common projected coordinate reference system used for this example immediately after we downloaded the data using the `get_acs()` function (see @lst-get_acs). This allows us to work with the water system data and the census data together in a common coordinate system.\n\nBefore calculating demographics for the *target* areas, we can do a bit of additional transformation to prepare the census data if needed. For example, we can combine the 'other' and 'multiple' racial/ethnic groupings into one 'other or multiple' racial/ethnic group.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## combine other and multiple\ncensus_data_acs <- census_data_acs %>% \n mutate('population_other_or_multiple_count' = population_other_count + population_multiple_count, \n .after = population_pacific_islander_count) %>% \n select(-c(population_other_count, population_multiple_count))\n```\n:::\n\n\nWe can also calculate the poverty rate for each census unit (which may be useful for presenting results later).\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_acs <- census_data_acs %>% \n mutate(poverty_rate_pct_calc_census_unit = 100 * poverty_below_count / poverty_total_assessed_count, \n .after = poverty_above_count)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# We can also drop census units with zero population, since they won't contribute anything to our calculations.\n\n## drop census units with zero population\n# census_data_acs <- census_data_acs %>% \n# filter(population_total > 0)\n```\n:::\n\n\n### Interpolation Step 1: Areal Interpolation (for Count Variables) {#sec-areal-interp}\n\nThere are a couple of ways to implement the areal interpolation method. The example below 'manually' implements the process using functions from the `sf` package, for reasons described below. However, note that there are R packages which make it possible to perform areal interpolation with a single function - for example, the `sf` package's [`st_interpolate_aw`](https://r-spatial.github.io/sf/reference/interpolate_aw.html) function and the [`areal`](https://chris-prener.github.io/areal/) package's [`aw_interpolate`](https://chris-prener.github.io/areal/reference/aw_interpolate.html) function. This example uses a more 'manual' approach because this makes it possible to use the multi-step process described above, and also produces useful intermediate calculated data for mapping and visualization. However, we can use the single-function approach to double check our implementation of the areal interpolation approach for the count data (see @sec-check-areal-interp).\n\nFirst, we clip the census data to the water system boundaries:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_clip <- census_data_acs %>% \n mutate(cenus_unit_area = st_area(.)) %>% \n st_intersection(water_systems_sac) %>% \n mutate(clipped_area = st_area(.)) %>% \n mutate(areal_weight_factor = drop_units(clipped_area / cenus_unit_area))\n```\n:::\n\n\n@fig-map-clipped-polygons shows a plot of the census units clipped to the Sacramento Suburban Water District water system, along with the original/complete census units. Note that you can toggle layers on and off (and change their order of appearance) using the layers button in the upper left part of the map (below the zoom buttons).\n\n::: {#fig-map-clipped-polygons}\n\n::: {.cell}\n\n```{.r .cell-code}\n# label: fig-map-clipped-polygons\n# fig-cap: \"Water systems (filled polygons), boundaries of overalpping census units (grey), and clipped portions of census units (light blue) that will be used to estimate water system demographics.\"\n\nmapview(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot), \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE) + \n mapview(census_data_acs %>% \n st_filter(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot)), \n alpha.regions = 0.15, \n col.regions = 'grey', \n color = 'black', \n lwd = 1, \n label = 'NAME', \n layer.name = 'ACS Data Full', \n legend = FALSE) +\n mapview(census_data_clip %>% \n filter(WATER_SY_1 == system_plot),\n alpha.regions = 0, \n color = 'cyan', \n lwd = 1.3, \n label = 'NAME', \n layer.name = 'ACS Data Clipped', \n legend = FALSE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\nWater system Sacramento Suburban Water District (filled polygon), boundaries of overalpping census units (grey), and clipped portions of census units (light blue) that will be used to estimate water system demographics.\n:::\n\nNext, we can compute the area-weighted counts for the portions of census units that overlap each water system boundary:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_interpolate <- census_data_clip %>% \n mutate(\n across(\n .cols = ends_with('_count'),\n .fns = ~ .x * areal_weight_factor #,\n # .names = \"{str_replace(.col, 'population_', 'percent_')}\"\n )) \n```\n:::\n\n\nAs noted above, it's also possible to use pre-built functions from several R packages to perform areal interpolation in a single step. Since we're using a three-step process, which also implements population weighted averaging for some variables, we're not using those functions directly in this example. However, they can be a useful check to validate our computed count data, but only after we aggregate our data at the system level -- see @sec-check-areal-interp for more details.\n\n### Interpolation Step 2: Compute Population Weighted Values (Intensive Variables) {#sec-pop-interp}\n\nCompute population weighted values\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_interpolate <- census_data_interpolate %>% \n mutate(average_household_size_weighted = average_household_size * households_count,\n median_household_income_weighted = median_household_income * households_count,\n per_capita_income_weighted = per_capita_income * population_total_count)\n```\n:::\n\n\n::: callout-caution\nTo calculate an aggregated value for a variable like median household income, which depends on the distribution of the underling data, it may be worth considering whether a weighed average value is an appropriate measure. In some cases, it may be more appropriate to use the counts in each income bracket to estimate a median income, and/or present the income distribution rather than a single value.\n\nFor a discussion of the problem and a proposed solution, see [this document](https://www.documentcloud.org/documents/6165014-How-to-Recalculate-a-Median.html#document/p1).\n:::\n\n### Interpolation Step 3: Aggregate by Water System\n\nNext, we need to combine the weighted values calculated above to produce the estimates for each water system, and can also use those combined values to compute some additional metrics for each system (like rates, income distributions, etc.).\n\n#### Combine Results by Water System\n\nFirst, combine the results by summing all of the count-based variables (derived from areal interpolation), and calculating weighted averages for all variables computed in step 2 above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- census_data_interpolate %>% \n group_by(WATER_SY_1) %>% \n summarize(\n across(\n .cols = ends_with('_count'),\n .fns = ~ sum(.x)\n ),\n average_household_size_hh_weighted = sum(average_household_size_weighted) / sum(households_count),\n median_household_income_hh_weighted = sum(median_household_income_weighted) / sum(households_count),\n per_capita_income_pop_weighted = sum(per_capita_income_weighted) / sum(population_total_count)\n ) %>% \n ungroup()\n```\n:::\n\n\n#### Check - Variables Estimated with Areal Interpolation {#sec-check-areal-interp}\n\nAs noted above, it's also possible to use pre-built functions for areal interpolation. This section demonstrates those functions and uses them as a check of our computed count data.\n\nFrom the `sf` package, we can use the `st_interpolate_aw` function:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# NOTE: it's only necessary to check the estimated values for one variable - \n# this just checks the total estimated population\n\n# sf package\ncheck_sf <- st_interpolate_aw(x = census_data_acs %>% \n select(population_total_count),\n to = water_systems_sac,\n extensive = TRUE) %>% \n bind_cols(water_systems_sac %>% st_drop_geometry)\n\n# check - should be TRUE if results are equivalent\nall(check_sf %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5) ==\n water_system_demographics %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5)\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n:::\n\n\nFrom the `areal` package, we can use the `aw_interpolate` function. Note that there are some settings that you may need to modify in the `aw_interpolate` function depending on the type of analysis you're doing. In particular, for more information about the `weight` argument -- which can be either `sum` or `total` -- see [this section of the documentation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html#calculating-weights-for-extensive-interpolations). For more information about extensive versus intensive interpolations, see [this section of the documenation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html#extensive-and-intensive-interpolations) (as noted above, the method applied here avoids using areal interpolation to calculate intensive variables, because area may not be a good metric for determining how to weight those variables, considering that we can estimate associated counts for populations/households/etc.).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# NOTE: it's only necessary to check the estimated values for one variable - \n# this just checks the total estimated population\n\n# areal package\ncheck_areal <- aw_interpolate(water_systems_sac,\n tid = WATER_SY_1,\n source = census_data_acs,\n sid = GEOID,\n weight = 'total',\n extensive = c('population_total_count'))\n\n# check - should be TRUE if results are equivalent\nall(check_areal %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5) ==\n water_system_demographics %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5)\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n:::\n\n\n#### Clean & Format Results {#sec-results-clean}\n\nWe could stop here, and save the dataset containing the results to an output file (done below - see @sec-results-save). But, it may be useful to do some additional computations and re-formatting before saving the dataset. For example, in this case it may be useful to calculate the racial/ethnic breakdown of each system's population as percentages of the total population (in addition to the total counts computed above), and calculate other rates / distributions.\n\nFirst we can add fields with each racial/ethnic group's estimated percent of the total population within each water system's service area:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>%\n mutate(\n across(\n .cols = starts_with('population_'),\n .fns = ~ round(.x / population_total_count * 100, 2),\n .names = \"{str_replace(.col, '_count', '_percent')}\"\n ),\n .after = population_other_or_multiple_count) %>% \n select(-population_total_percent) # this always equals 1, not needed\n```\n:::\n\n\nWe can also calculate the estimated poverty rate for each water system's service area.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(poverty_rate_percent = 100 * poverty_below_count / poverty_total_assessed_count, \n .after = poverty_above_count)\n```\n:::\n\n\nAnd compute income brackets in 25k increments:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(households_income_0_25k_count = \n households_income_below_10k_count + \n households_income_10k_15k_count + \n households_income_15k_20k_count +\n households_income_20k_25k_count,\n households_income_25k_50k_count =\n households_income_25k_30k_count + \n households_income_30k_35k_count +\n households_income_35k_40k_count +\n households_income_40k_45k_count +\n households_income_45k_50k_count,\n households_income_50k_75k_count =\n households_income_50k_60k_count +\n households_income_60k_75k_count,\n .after = households_income_above_200k_count\n ) # note - above 75k is already in 25k increments\n```\n:::\n\n\nAnd compute income brackets in 50k increments:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(households_income_0_50k_count = \n households_income_0_25k_count + \n households_income_25k_50k_count,\n households_income_50k_100k_count =\n households_income_50k_75k_count +\n households_income_75k_100k_count,\n households_income_100k_150k_count =\n households_income_100k_125k_count +\n households_income_125k_150k_count,\n .after = households_income_50k_75k_count\n ) # above 150k is already in 50k increments\n```\n:::\n\n\nAnd compute grouped median household income:\n\n\n::: {.cell}\n\n:::\n\n\nAnd compute \\# and % of households below income thresholds:\n\n\n::: {.cell}\n\n:::\n\n\nAnd, compute other variables (% households by % housing cost, ...)\n\n\n::: {.cell}\n\n:::\n\n\nFinally, we can round the estimated values to appropriate levels of precision:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>%\n mutate(\n across(\n .cols = ends_with('_count'),\n .fns = ~ round(.x, 0)\n )) %>%\n mutate(\n across(\n .cols = ends_with('_percent'),\n .fns = ~ round(.x, 2)\n ))\n```\n:::\n\n\nWe've now got a dataset with the selected census data estimated for each of the *target* geographic features (water system service areas). Here's a view of the contents and structure of the re-formatted dataset (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_system_demographics[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 21\n$ WATER_SY_1 \"B & W RESORT MARI…\n$ population_total_count 0, 22603, 33120, 1…\n$ population_hispanic_or_latino_count 0, 10939, 5245, 34…\n$ population_white_count 0, 3504, 19456, 23…\n$ population_black_or_african_american_count 0, 2663, 3199, 197…\n$ population_native_american_or_alaska_native_count 0, 121, 113, 70, 0…\n$ population_asian_count 0, 4075, 2947, 108…\n$ population_pacific_islander_count 0, 240, 77, 59, 0,…\n$ population_other_or_multiple_count 0, 1060, 2082, 110…\n$ population_hispanic_or_latino_percent 41.43, 48.40, 15.8…\n$ population_white_percent 52.47, 15.50, 58.7…\n$ population_black_or_african_american_percent 0.00, 11.78, 9.66,…\n$ population_native_american_or_alaska_native_percent 0.00, 0.54, 0.34, …\n$ population_asian_percent 4.55, 18.03, 8.90,…\n$ population_pacific_islander_percent 0.00, 1.06, 0.23, …\n$ population_other_or_multiple_percent 1.56, 4.69, 6.29, …\n$ poverty_total_assessed_count 0, 22556, 33034, 1…\n$ poverty_below_count 0, 6010, 3389, 313…\n$ poverty_above_count 0, 16546, 29645, 6…\n$ poverty_rate_percent 22.60, 26.64, 10.2…\n$ geometry POLYGON ((…\n```\n\n\n:::\n:::\n\n\n@tbl-water-sys-demographics-rev provides a complete view of the cleaned and re-formatted dataset. These results are saved locally in tabular and spatial format in @sec-results-save.\n\n\n::: {#tbl-water-sys-demographics-rev .cell .tbl-cap-location-top tbl-cap='Water System Demographics'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n kable(caption = 'A Caption') %>%\n scroll_box(height = \"400px\")\n```\n\n::: {.cell-output-display}\n`````{=html}\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n
A Caption
WATER_SY_1 population_total_count population_hispanic_or_latino_count population_white_count population_black_or_african_american_count population_native_american_or_alaska_native_count population_asian_count population_pacific_islander_count population_other_or_multiple_count population_hispanic_or_latino_percent population_white_percent population_black_or_african_american_percent population_native_american_or_alaska_native_percent population_asian_percent population_pacific_islander_percent population_other_or_multiple_percent poverty_total_assessed_count poverty_below_count poverty_above_count poverty_rate_percent households_count households_income_below_10k_count households_income_10k_15k_count households_income_15k_20k_count households_income_20k_25k_count households_income_25k_30k_count households_income_30k_35k_count households_income_35k_40k_count households_income_40k_45k_count households_income_45k_50k_count households_income_50k_60k_count households_income_60k_75k_count households_income_75k_100k_count households_income_100k_125k_count households_income_125k_150k_count households_income_150k_200k_count households_income_above_200k_count households_income_0_25k_count households_income_25k_50k_count households_income_50k_75k_count households_income_0_50k_count households_income_50k_100k_count households_income_100k_150k_count households_mortgage_total_count households_mortgage_over30pct_count households_mortgage_over50pct_count households_no_mortgage_total_count households_no_mortgage_over30pct_count households_no_mortgage_over50pct_count households_rent_total_count households_rent_over30pct_count households_rent_over50pct_count average_household_size_hh_weighted median_household_income_hh_weighted per_capita_income_pop_weighted geometry
B & W RESORT MARINA 0 0 0 0 0 0 0 0 41.43 52.47 0.00 0.00 4.55 0.00 1.56 0 0 0 22.60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.030000 51977.00 40522.00 POLYGON ((-138282.2 13643.2...
CAL AM FRUITRIDGE VISTA 22603 10939 3504 2663 121 4075 240 1060 48.40 15.50 11.78 0.54 18.03 1.06 4.69 22556 6010 16546 26.64 6900 354 339 521 263 367 302 359 355 565 692 876 784 459 235 287 141 1477 1948 1569 3425 2352 694 1620 745 345 1236 95 58 4044 2131 1059 3.257806 53040.44 20519.57 POLYGON ((-127001.4 54266.8...
CALAM - ANTELOPE 33120 5245 19456 3199 113 2947 77 2082 15.84 58.74 9.66 0.34 8.90 0.23 6.29 33034 3389 29645 10.26 10529 315 184 101 122 116 469 248 368 449 737 1077 1669 1501 1077 1158 937 723 1650 1814 2373 3483 2578 5544 1861 621 1747 184 106 3238 1678 649 3.134530 93741.55 34660.44 POLYGON ((-120906.3 77326.5...
CALAM - ARDEN 10112 3433 2392 1977 70 1082 59 1100 33.95 23.65 19.55 0.69 10.70 0.58 10.87 10034 3130 6904 31.19 3823 201 259 239 167 319 190 142 236 207 440 394 535 228 148 62 58 866 1093 834 1959 1368 376 265 84 46 133 8 3 3426 2124 1170 2.623643 49624.62 22770.82 POLYGON ((-123052 64046.06,...
CALAM - ISLETON 34 14 17 0 0 2 0 1 42.06 51.14 0.00 0.00 4.55 0.00 2.25 34 7 27 20.89 16 1 1 0 1 1 0 1 1 0 2 1 1 3 1 0 1 4 3 3 6 4 4 6 4 1 7 2 2 4 1 1 2.078994 57361.76 40672.21 POLYGON ((-138730.9 17272.8...
CALAM - LINCOLN OAKS 42916 9056 26529 1486 143 2706 288 2708 21.10 61.82 3.46 0.33 6.31 0.67 6.31 42823 4074 38749 9.51 15621 740 375 308 622 488 616 585 629 645 1035 1641 2442 1889 1272 1555 778 2046 2964 2675 5010 5118 3161 7390 2671 919 3332 503 298 4900 2523 1302 2.730281 NA 33728.94 POLYGON ((-117495.2 73240.4...
CALAM - PARKWAY 58635 18665 8921 6965 21 19228 1386 3449 31.83 15.21 11.88 0.04 32.79 2.36 5.88 58434 9804 48630 16.78 17667 1081 753 514 713 694 640 713 700 727 1145 1918 2490 1634 1532 1546 865 3061 3475 3064 6536 5554 3166 7163 2719 1049 3418 647 383 7086 3517 1917 3.284608 NA 26938.14 POLYGON ((-124522.5 52428.5...
CALAM - SUBURBAN ROSEMONT 57897 13791 25062 7725 91 6905 380 3942 23.82 43.29 13.34 0.16 11.93 0.66 6.81 57661 8374 49287 14.52 21045 1156 612 472 744 653 568 582 874 628 1289 2508 3438 2595 1594 1671 1661 2985 3305 3797 6290 7235 4189 8262 2262 730 3425 439 271 9358 4521 2320 2.726937 NA 34497.37 POLYGON ((-119360.4 58937.6...
CALAM - WALNUT GROVE 12 5 5 0 0 1 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 12 2 10 15.75 5 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 1 2 2 2 0 2 0 0 1 0 0 2 1 0 2.490000 68248.00 38950.00 POLYGON ((-131705.3 26403.6...
CALIFORNIA STATE FAIR 532 78 262 91 0 48 0 52 14.68 49.25 17.13 0.00 9.10 0.00 9.85 526 152 374 28.89 285 65 13 8 5 9 14 2 0 23 29 30 35 21 11 17 3 91 48 59 140 93 32 0 0 0 0 0 0 285 177 95 1.820000 52886.00 33141.00 POLYGON ((-125611.2 65287.3...
CARMICHAEL WATER DISTRICT 39253 6192 25026 2230 68 3326 295 2116 15.78 63.76 5.68 0.17 8.47 0.75 5.39 38700 5000 33700 12.92 15937 570 534 513 472 398 607 522 684 541 996 1595 1782 1724 1200 1678 2122 2088 2751 2591 4839 4373 2924 5256 1399 669 3147 358 177 7534 4056 2068 2.405914 NA 46901.80 POLYGON ((-117711 65208.06,...
CITRUS HEIGHTS WATER DISTRICT 68912 12380 48148 2092 162 2875 71 3186 17.96 69.87 3.04 0.23 4.17 0.10 4.62 68581 6961 61620 10.15 25633 1012 569 446 769 665 867 841 723 1165 1875 3057 3954 2744 2332 2533 2080 2796 4261 4932 7057 8886 5075 10344 3553 1380 4293 554 286 10996 5759 2620 2.653808 82960.78 37323.17 POLYGON ((-114405.5 72735.6...
CITY OF SACRAMENTO MAIN 516189 151211 159508 62060 1249 98585 9242 34334 29.29 30.90 12.02 0.24 19.10 1.79 6.65 508800 77003 431797 15.13 194000 9540 9401 6217 6407 5804 6255 6278 6139 6729 13349 17396 26982 20453 15080 17439 20531 31564 31205 30745 62769 57728 35533 67435 21769 8217 29857 3476 1805 96708 47510 24524 2.609594 NA 39105.61 POLYGON ((-133314 51929.51,...
DEL PASO MANOR COUNTY WATER DI 5592 687 3967 390 15 119 31 382 12.28 70.95 6.97 0.26 2.13 0.56 6.84 5592 621 4971 11.10 2222 170 45 54 66 21 51 66 237 40 158 278 166 171 120 347 231 336 416 436 752 601 291 922 326 189 572 112 68 729 509 114 2.516895 NA 40254.83 POLYGON ((-120068.3 65980.9...
DELTA CROSSING MHP 0 0 0 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 0 0 0 17.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 POLYGON ((-132498.4 40410.2...
EAST WALNUT GROVE [SWS] 3 2 2 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 3 1 3 15.75 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 2.490000 68248.00 38950.00 POLYGON ((-132506.3 25966.4...
EDGEWATER MOBILE HOME PARK 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-153562.3 7972.28...
EL DORADO MOBILE HOME PARK 139 84 11 15 0 19 0 11 60.26 7.80 10.48 0.00 13.27 0.00 8.19 139 60 79 43.12 48 6 10 0 4 6 1 0 8 1 7 0 1 0 4 0 1 19 15 8 34 9 4 3 0 0 10 5 5 35 17 10 2.710000 29468.00 17394.00 POLYGON ((-124341.2 53660.1...
EL DORADO WEST MHP 148 89 12 16 0 20 0 12 60.26 7.80 10.48 0.00 13.27 0.00 8.19 147 63 84 43.12 51 6 10 0 4 6 1 0 8 2 8 0 1 0 5 0 1 20 16 8 37 9 5 3 0 0 10 6 6 38 18 10 2.710000 29468.00 17394.00 POLYGON ((-124532.3 53662.9...
ELEVEN OAKS MOBILE HOME COMMUNITY 233 45 94 56 0 37 0 1 19.27 40.19 24.01 0.00 15.91 0.00 0.62 233 87 146 37.48 71 7 2 3 6 10 2 1 1 3 1 13 17 3 0 3 0 17 17 15 34 32 3 8 3 1 21 1 1 42 29 23 3.280000 60521.00 18213.00 POLYGON ((-119819.8 71950.9...
ELK GROVE WATER SERVICE 42647 7656 19550 3209 70 8939 388 2835 17.95 45.84 7.53 0.16 20.96 0.91 6.65 42258 3264 38994 7.72 13239 430 202 253 224 328 102 345 292 245 667 1117 1441 1470 1386 1907 2832 1108 1311 1784 2420 3225 2856 7552 1903 628 2861 283 113 2826 1595 864 3.179068 122771.00 43429.03 POLYGON ((-118730.1 42496.7...
FAIR OAKS WATER DISTRICT 36003 4655 27050 708 94 1372 12 2113 12.93 75.13 1.97 0.26 3.81 0.03 5.87 35775 2852 32923 7.97 14233 546 332 113 229 208 391 206 469 293 804 1064 2214 1447 1568 1875 2474 1220 1568 1868 2788 4082 3016 7090 1872 845 3092 261 108 4051 1844 768 2.480217 NA 54435.01 POLYGON ((-112317.5 69577.6...
FLORIN COUNTY WATER DISTRICT 9951 2963 1548 1394 7 2743 866 430 29.78 15.56 14.01 0.07 27.56 8.70 4.32 9835 1285 8550 13.06 2755 84 125 53 154 103 46 86 176 224 258 223 432 297 215 143 137 417 635 481 1051 913 512 981 426 90 675 49 28 1100 476 260 3.573005 67048.12 24517.64 POLYGON ((-122791.9 52602.2...
FOLSOM STATE PRISON 3536 1257 652 1390 57 70 34 77 35.55 18.43 39.31 1.60 1.97 0.96 2.17 29 1 28 2.20 23 0 0 0 0 0 0 0 0 0 0 0 0 4 4 12 1 0 0 0 0 1 8 3 1 0 0 0 0 19 0 0 NA 161047.22 2271.22 POLYGON ((-99838.11 75350.0...
FOLSOM, CITY OF - ASHLAND 3845 318 2934 43 1 125 1 423 8.26 76.32 1.12 0.03 3.26 0.02 10.99 3780 143 3637 3.79 1800 44 17 104 43 34 209 103 74 43 43 158 248 132 80 123 345 208 463 201 670 449 212 594 164 90 847 368 82 358 196 74 NA NA 56773.97 POLYGON ((-102605.9 74922.1...
FOLSOM, CITY OF - MAIN 62462 8433 35222 1693 105 12934 177 3897 13.50 56.39 2.71 0.17 20.71 0.28 6.24 62115 3405 58710 5.48 22409 807 218 390 477 418 283 329 373 451 670 1181 2255 2382 1747 4083 6344 1892 1855 1851 3747 4106 4129 11491 2728 1179 3590 237 146 7328 3010 1321 NA NA NA POLYGON ((-101870.6 66094.5...
FREEPORT MARINA 3 2 1 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 3 1 3 17.42 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 POLYGON ((-130970.7 50553.3...
GALT, CITY OF 21490 9314 9952 520 22 872 20 789 43.34 46.31 2.42 0.10 4.06 0.09 3.67 21341 1404 19937 6.58 6988 139 168 243 210 141 342 161 347 152 550 687 807 1096 504 789 650 761 1143 1237 1904 2044 1601 3724 907 523 1454 109 44 1809 906 414 3.048249 NA 33685.54 MULTIPOLYGON (((-113921.6 2...
GOLDEN STATE WATER CO - ARDEN WATER SERV 6556 1706 2887 322 0 888 11 742 26.02 44.04 4.91 0.00 13.54 0.16 11.32 6453 1626 4828 25.19 2173 19 82 19 141 53 173 34 179 37 139 351 319 132 172 141 183 262 476 490 738 809 303 728 239 123 131 0 0 1315 599 335 2.897716 NA 30417.36 POLYGON ((-121143.9 63698.4...
GOLDEN STATE WATER CO. - CORDOVA 48115 9009 26042 3982 229 6050 188 2615 18.72 54.13 8.28 0.48 12.57 0.39 5.43 47835 4408 43427 9.21 18022 509 482 310 496 480 437 389 469 598 1276 1692 2653 2565 1671 1948 2047 1796 2374 2968 4170 5621 4236 7380 2174 836 3506 364 201 7137 2744 1410 NA NA NA POLYGON ((-112985.4 62375.3...
HAPPY HARBOR (SWS) 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-139842 11256.85,...
HOLIDAY MOBILE VILLAGE 46 18 7 3 0 15 0 3 38.66 15.12 7.10 0.00 32.49 0.00 6.64 46 10 36 22.33 16 2 1 0 1 0 1 5 1 0 0 2 2 1 0 0 0 4 7 2 11 4 1 2 0 0 2 1 1 12 6 4 2.860000 38491.00 16707.00 POLYGON ((-123874.7 52485.3...
HOOD WATER MAINTENCE DIST [SWS] 1 1 0 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 1 0 1 17.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 MULTIPOLYGON (((-132506 403...
IMPERIAL MANOR MOBILEHOME COMMUNITY 209 52 129 1 0 6 0 21 24.93 61.63 0.45 0.00 2.93 0.00 10.05 209 45 164 21.48 124 4 26 18 3 0 16 7 5 6 1 4 29 0 0 0 6 51 34 5 84 34 0 9 0 0 89 37 34 27 27 22 1.680363 31831.84 32878.17 POLYGON ((-115390.2 74250.3...
KORTHS PIRATES LAIR 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-137314.9 10213.1...
LAGUNA DEL SOL INC 24 5 18 0 0 0 0 0 21.55 75.20 0.00 0.67 1.46 0.00 1.12 24 2 22 6.40 9 0 1 1 0 0 0 0 0 0 0 0 2 0 0 1 2 2 1 0 3 2 1 5 2 2 3 0 0 2 0 0 2.640000 95227.00 50793.00 POLYGON ((-104662.2 49197.3...
LAGUNA VILLAGE RV PARK 20 3 2 1 0 11 2 2 12.79 8.48 7.28 0.00 52.62 8.38 10.45 20 2 18 11.79 7 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 2 2 1 3 1 0 1 0 0 3 1 0 3.030000 84332.00 32668.00 POLYGON ((-122461.8 48066.6...
LINCOLN CHAN-HOME RANCH 4 2 2 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 4 1 3 15.75 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 2.490000 68248.00 38950.00 POLYGON ((-136788.6 36526.1...
LOCKE WATER WORKS CO [SWS] 1 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 1 0 1 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-131952.8 27176.6...
MAGNOLIA MUTUAL WATER 1 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 1 0 1 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-137022.9 36118.9...
MC CLELLAN MHP 269 52 108 65 0 43 0 2 19.27 40.19 24.01 0.00 15.91 0.00 0.62 269 101 168 37.48 82 8 2 3 7 11 2 2 1 3 1 15 20 3 0 3 0 20 19 17 39 36 3 9 4 2 25 1 1 48 34 27 3.280000 60521.00 18213.00 POLYGON ((-119814.9 72169.0...
OLYMPIA MOBILODGE 290 70 81 18 0 101 16 3 24.12 28.03 6.30 0.00 34.95 5.53 1.08 290 68 222 23.43 114 11 0 6 10 9 3 13 0 0 10 19 8 3 12 5 5 28 25 29 53 36 14 31 22 10 51 12 10 33 9 7 2.510000 53786.00 29451.00 POLYGON ((-123342.4 53061.6...
ORANGE VALE WATER COMPANY 17387 2658 12308 241 181 633 86 1281 15.28 70.79 1.39 1.04 3.64 0.49 7.37 17288 1904 15384 11.01 6595 389 111 61 94 226 58 274 120 181 372 752 990 901 626 678 766 655 858 1123 1512 2113 1526 3246 1021 453 1686 315 185 1663 693 305 2.608348 92693.71 42509.89 POLYGON ((-108131.3 74330.4...
PLANTATION MOBILE HOME PARK 10 4 1 1 0 3 0 1 38.66 15.12 7.10 0.00 32.49 0.00 6.64 10 2 7 22.33 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 2 1 0 1 0 0 0 0 0 2 1 1 2.860000 38491.00 16707.00 POLYGON ((-124180.4 53321.5...
RANCHO MARINA 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-138041.4 11320.9...
RANCHO MURIETA COMMUNITY SERVI 3239 661 2157 120 7 188 0 106 20.42 66.59 3.71 0.21 5.80 0.00 3.26 3239 199 3040 6.13 1402 59 42 0 6 5 18 74 27 75 44 81 88 118 204 241 319 108 199 125 307 213 323 1029 205 103 270 63 57 103 41 40 2.307704 144993.81 66451.34 POLYGON ((-92457.85 52674.7...
RIO COSUMNES CORRECTIONAL CENTER [SWS] 22 6 8 4 1 1 0 2 25.74 37.49 16.82 2.97 4.50 1.81 10.66 4 0 4 0.00 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 3.450000 115897.00 11095.00 POLYGON ((-124032.5 32206.2...
RIO LINDA/ELVERTA COMMUNITY WATER DIST 11831 2585 7595 337 17 765 21 512 21.85 64.19 2.85 0.14 6.46 0.18 4.33 11829 1619 10210 13.69 3762 177 156 67 169 56 113 116 114 118 173 297 607 492 431 416 259 569 518 470 1087 1077 922 1918 573 157 773 114 47 1070 519 340 3.123012 NA 33734.49 POLYGON ((-126609.8 73568.2...
RIVER'S EDGE MARINA & RESORT 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-141102.2 11867.3...
SAC CITY MOBILE HOME COMMUNITY LP 229 82 17 7 0 123 0 0 35.66 7.50 3.27 0.00 53.57 0.00 0.00 229 110 119 48.14 89 11 16 9 10 8 0 0 4 2 7 1 13 4 4 0 0 46 14 8 60 21 8 4 2 2 15 2 0 71 41 30 2.530000 22380.00 16689.00 POLYGON ((-124544.3 56147.0...
SACRAMENTO SUBURBAN WATER DISTRICT 193126 43047 97872 17684 834 20602 624 12464 22.29 50.68 9.16 0.43 10.67 0.32 6.45 190984 33399 157585 17.49 72505 3817 3001 3069 2884 3205 3100 3337 2893 2342 5541 6792 10037 6480 4342 5488 6177 12771 14878 12333 27649 22370 10822 23467 7204 2837 12037 2087 1160 37001 21072 10274 2.635471 NA 35321.18 MULTIPOLYGON (((-122206.9 6...
SAN JUAN WATER DISTRICT 30122 3409 21349 831 287 2762 17 1467 11.32 70.87 2.76 0.95 9.17 0.06 4.87 30014 1718 28297 5.72 10750 389 168 100 275 128 160 111 133 127 472 684 984 854 876 1032 4256 932 658 1156 1591 2141 1730 6210 1754 724 2883 528 357 1658 726 339 2.783858 NA 72978.42 POLYGON ((-104526.8 73044.7...
SCWA - ARDEN PARK VISTA 8086 990 6016 270 12 396 8 395 12.24 74.40 3.33 0.15 4.90 0.10 4.88 8038 523 7515 6.51 3303 79 36 48 77 65 38 18 49 162 139 187 253 465 208 416 1065 241 330 326 571 579 673 1823 520 112 673 76 23 807 384 225 2.424845 NA 84548.46 POLYGON ((-120985.4 62883.8...
SCWA - LAGUNA/VINEYARD 145495 27502 38496 16568 246 50411 2220 10052 18.90 26.46 11.39 0.17 34.65 1.53 6.91 145198 14710 130489 10.13 45137 1692 666 742 878 839 1336 850 788 752 2363 3198 6037 5323 5057 6578 8038 3978 4565 5561 8543 11598 10380 24581 7232 2916 7878 861 471 12677 6368 3337 3.207447 NA 41415.71 MULTIPOLYGON (((-126550 404...
SCWA MATHER-SUNRISE 18249 2708 8114 1553 23 4507 164 1180 14.84 44.47 8.51 0.12 24.70 0.90 6.47 18211 1005 17206 5.52 5503 228 35 97 57 68 39 12 20 36 189 320 533 645 755 1003 1469 416 174 509 590 1042 1399 3756 881 266 855 60 43 893 318 167 NA NA NA MULTIPOLYGON (((-112526.7 5...
SEQUOIA WATER ASSOC 0 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 0 0 0 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-136929.5 36128.1...
SOUTHWEST TRACT W M D [SWS] 174 29 42 24 3 75 1 0 16.58 24.48 13.69 1.55 43.11 0.60 0.00 174 38 136 21.83 57 1 2 7 0 7 0 0 10 12 3 2 5 0 1 2 4 10 29 6 39 10 1 3 1 0 8 0 0 45 29 7 3.040000 45671.00 36348.00 MULTIPOLYGON (((-125843.6 5...
SPINDRIFT MARINA 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-139920.3 11468.3...
TOKAY PARK WATER CO 652 214 134 37 0 239 0 28 32.80 20.55 5.61 0.00 36.69 0.00 4.35 652 113 539 17.29 173 2 2 3 21 0 0 13 13 10 18 27 36 14 4 10 0 27 36 45 64 81 18 81 38 11 44 0 0 48 32 12 3.757973 62802.24 19400.05 POLYGON ((-122824.8 54197.9...
TUNNEL TRAILER PARK 0 0 0 0 0 0 0 0 49.74 34.94 0.00 0.00 4.65 0.00 10.67 0 0 0 0.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.950000 153092.00 42507.00 POLYGON ((-136160.9 24171.2...
VIEIRA'S RESORT, INC 4 2 2 0 0 0 0 0 41.43 52.47 0.00 0.00 4.55 0.00 1.56 4 1 3 22.60 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 2.030000 51977.00 40522.00 POLYGON ((-143780.4 18567.4...
WESTERNER MOBILE HOME PARK 32 6 6 9 0 10 0 1 17.59 17.62 28.31 0.55 31.36 0.00 4.57 31 7 24 23.76 10 1 0 0 0 1 0 0 1 0 2 1 1 2 0 1 0 1 2 3 3 4 2 4 2 1 1 0 0 5 3 2 3.160000 59296.00 23437.00 POLYGON ((-122657.2 48977.8...
\n\n`````\n:::\n:::\n\n\n
\n\n#### Transform Results to Long Format {#sec-results-transform-long}\n\nFor further analysis and exploration / visualization of the results, it will help to convert the results from wide to long format, and edit the group names so that they can be used as titles.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# pivot from wide to long format\nwater_system_demographics_long <- water_system_demographics %>% \n # select(WATER_SY_1, starts_with('percent_')) %>% # select only the fields with percentages, and the water system name/id\n # convert to long format\n # st_drop_geometry() %>% \n pivot_longer(cols = !c(WATER_SY_1, geometry), \n names_to = 'variable', \n values_to = 'value')\n\n# clean variable names and add grouping fields (type, group_type)\nwater_system_demographics_long <- water_system_demographics_long %>% \n mutate(variable = variable %>% \n # str_remove_all(pattern = 'percent_') %>% \n str_replace_all(pattern = '_', replacement = ' ') %>% \n str_replace_all(pattern = ' or ', replacement = ' / ') %>% \n str_to_title(.) %>% \n str_remove_all(pattern = ' / Alaska Native')) %>% \n mutate(type = case_when(\n str_detect(variable, pattern = 'Count') ~ 'Count',\n str_detect(variable, pattern = 'Percent') ~ 'Percent',\n str_detect(variable, pattern = 'Pop Weighted') ~ 'Pop Weighted',\n str_detect(variable, pattern = 'Hh Weighted') ~ 'Hh Weighted',\n .default = NA), \n .after = variable) %>% \n mutate(group_type = case_when(\n str_detect(variable, pattern ='Population') ~ 'Population',\n str_detect(variable, pattern = 'Households') ~ 'Households',\n str_detect(variable, pattern = 'Average Household Size Hh Weighted') ~ 'Household Weighted', \n str_detect(variable, pattern = 'Median Household Income Hh Weighted') ~ 'Household Weighted',\n str_detect(variable, pattern = 'Per Capita Income Pop Weighted') ~ 'Population Weighted',\n str_detect(variable, pattern = 'Poverty') ~ 'Population'),\n .after = type) %>% \n mutate(variable = case_when(\n str_detect(variable, pattern = 'Households Count') ~ 'Households Total',\n .default = str_remove_all(variable, pattern = 'Households'))) %>% \n mutate(variable = case_when(\n str_detect(variable, 'Population Total Count') ~ 'Population Total',\n .default = str_remove_all(variable, 'Population'))) %>%\n mutate(variable = str_remove_all(variable, \n pattern = 'Count')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = 'Percent')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = ' Hh Weighted')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = ' Pop Weighted')) %>% \n mutate(variable = str_replace_all(variable, \n pattern = 'Over30pct', \n replacement = 'Over 30% Income')) %>% \n mutate(variable = str_replace_all(variable, \n pattern = 'Over50pct', \n replacement = 'Over 50% Income')) %>% \n mutate(variable = str_trim(variable)) %>%\n mutate(variable = str_replace_all(variable,\n pattern = 'k ',\n replacement = 'k-')) %>%\n mutate(variable = str_replace_all(variable,\n pattern = '0 ',\n replacement = '0-'))\n```\n:::\n\n\nHere's a view of the structure of the reformatted data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_system_demographics_long)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 3,348\nColumns: 6\n$ WATER_SY_1 \"B & W RESORT MARINA\", \"B & W RESORT MARINA\", \"B & W RESORT…\n$ geometry POLYGON ((-138282.2 13643.2..., POLYGON ((-138282.2…\n$ variable \"Population Total\", \"Hispanic / Latino\", \"White\", \"Black-/ …\n$ type \"Count\", \"Count\", \"Count\", \"Count\", \"Count\", \"Count\", \"Coun…\n$ group_type \"Population\", \"Population\", \"Population\", \"Population\", \"Po…\n$ value 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 41.43, 52.4…\n```\n\n\n:::\n:::\n\n\n### Save Results {#sec-results-save}\n\nOnce we've finished the computations and verified the outputs look reasonable, we can save the results to output files so they can be re-used and shared. The results can be saved in tabular (e.g., csv, excel) and/or spatial (e.g., shapefile, geopackage) formats, which may be helpful for different use cases. Note that you may need to think about exactly what variables to include in the output file(s) and how to format the output datasets (e.g., wide versus long format).\n\nThe chunk of code below (which is hidden by default), just tests to see whether any of the datasets to be saved have been changed since the previous version was saved. In general this is probably not needed for a typical workflow and can be ignored for most use cases -- it is just used here to make rendering of this document a little more efficient.\n\n\n::: {.cell}\n\n```{.r .cell-code code-fold=\"true\"}\n# compute hash for datasets to be saved (i.e., a unique identifier for each dataset), and compare against previous versions\n\n## define file that stores hash (unique identifier for dataset)\nhash_file <- here('03_data_results',\n 'dataset_hash.csv')\n\n## compute hashes (unique identifier for datasets)\nhash_current <- digest(object = water_system_demographics,\n algo = 'md5')\nhash_current_long <- digest(object = water_system_demographics_long,\n algo = 'md5')\nhash_table_current <- tibble(dataset = c('water_system_demographics', 'water_system_demographics_long'),\n hash = c(hash_current, hash_current_long))\n\n## get the previous hashes from file (if it exists), else create a new file to store the hashes\nif (file.exists(hash_file)) {\n hash_table_previous <- read_csv(file = hash_file)\n} else {\n file.create(hash_file)\n hash_table_previous <- tibble(dataset = c('water_system_demographics', 'water_system_demographics_long'),\n hash = c('missing', 'missing'))\n}\n\n## if new hash is different from previous hash, set flag to update the output file (i.e., write a new version of the file)\nfile_update <- !identical(hash_table_current %>% \n filter(dataset == 'water_system_demographics') %>% \n pull(hash),\n hash_table_previous %>% \n filter(dataset == 'water_system_demographics') %>% \n pull(hash))\nfile_update_long <- !identical(hash_table_current %>% \n filter(dataset == 'water_system_demographics_long') %>% \n pull(hash),\n hash_table_previous %>% \n filter(dataset == 'water_system_demographics_long') %>% \n pull(hash))\n\n## write current hashes to file (for comparison with future versions)\nwrite_csv(x = hash_table_current,\n file = hash_file,\n append = FALSE)\n```\n:::\n\n\n#### Tabular Dataset {#sec-results-save-tabular}\n\nThe code below saves the tabular results to a csv file -- note that this dataset is in the 'wide' format we originally produced the results in:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update == TRUE) {\n write_csv(water_system_demographics %>%\n st_drop_geometry(), # drop the spatial data since this is a tabular format\n file = here('03_data_results',\n 'water_system_demographics_sac.csv'))\n}\n```\n:::\n\n\nWe can also save the data in the long/tidy format we developed above:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update_long == TRUE) {\n write_csv(water_system_demographics_long %>%\n st_drop_geometry(), # drop the spatial data since this is a tabular format\n file = here('03_data_results',\n 'water_system_demographics_sac_long.csv'))\n}\n```\n:::\n\n\n#### Spatial Dataset {#sec-results-save-spatial}\n\nTo save the output in a geospatial format, it may be best to save the data in a wide format, so that all of the attribute data for each *target* area (water system) is in a single row along with its spatial data (i.e. the system boundary information) (saving in long format may create a very large file). The code below saves the results -- in wide format -- to a geopackage file, which is a spatial file format that is similar to a shapefile.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update == TRUE) {\n st_write(water_system_demographics,\n here('03_data_results',\n 'water_system_demographics_sac.gpkg'),\n append = FALSE)\n}\n```\n:::\n\n\n## Explore and Visualize Results {#sec-results-explore}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nFor simplicity, this section will focus on presenting estimated demographics for some of the largest water suppliers in the Sacramento county region (results for small water systems may not be very accurate and should be used with some caution - see @sec-check-pop-estimated-reported and @sec-small-area-estimates for more investigation of the results for small systems).\n\n\\[TO DO: add visualizations\\]\n\n## Check - Estimated vs Reported Population Estimates {#sec-check-pop-estimated-reported}\n\n\\[TO DO: Create map\\]\n\nBased on the map above, it's apparent that it will be difficult to obtain reasonable estimates for some suppliers, such as the suppliers with very small service areas in the southern portion of the county where the block groups are very large (and the supplier's service are is only a small fraction of the total area of the block group). These issues are explored further in @sec-small-area-estimates.\n\nNote that there are a number of reasons why the estimated population values are likely to differ from the population numbers in the water system dataset (e.g., the depicted boundaries may not be correct or exact, the supplier may have used different methods to count/estimate the population they serve, the time frames for the estimates may be different, etc.). But, there may also be some cases where the numbers differ significantly -- depending on the actual analysis being performed, this may mean that further work is needed for certain areas, or could mean that this method may not be sufficient and different methods are needed.\n\nAs a check, we can add a column to the interpolated dataset (which we'll call `population_percent_difference`) to compute the difference between the estimated total population (in the `population_total` field) and the total population listed in the `POPULATION` field (the reported value from the water system dataset).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n left_join(water_systems_sac %>% \n st_drop_geometry() %>% \n select(WATER_SY_1, POPULATION),\n by = 'WATER_SY_1')\n\nwater_system_demographics <- water_system_demographics %>%\n mutate(population_percent_difference =\n round(100 * (population_total_count - POPULATION) / POPULATION, \n 2), \n .after = POPULATION)\n```\n:::\n\n\nFor water systems with a small population and/or service area, the estimated demographics may not match the population numbers in the original water system dataset very well. You can see this in @tbl-pop-est-small by comparing the `POPULATION` field, which contains the total population values from the water supplier dataset, with the `population_total` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. This probably indicates that, for small areas, some adjustments and/or further analysis may be needed, and the preliminary estimated values should be treated with some caution/skepticism.\n\nNote: See @sec-small-area-estimates below for some more investigation into water systems whose estimated population is at or near zero.\n\n\n::: {#tbl-pop-est-small .cell tbl-cap='10 Smallest Water Systems by Population'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n arrange(POPULATION) %>%\n slice(1:10) %>%\n select(WATER_SY_1, POPULATION, population_total_count, population_percent_difference) %>%\n st_drop_geometry() %>%\n kable()\n```\n\n::: {.cell-output-display}\n\n\n|WATER_SY_1 | POPULATION| population_total_count| population_percent_difference|\n|:---------------------------|----------:|----------------------:|-----------------------------:|\n|DELTA CROSSING MHP | 30| 0| -100.00|\n|LAGUNA VILLAGE RV PARK | 32| 20| -37.50|\n|LINCOLN CHAN-HOME RANCH | 33| 4| -87.88|\n|EDGEWATER MOBILE HOME PARK | 40| 0| -100.00|\n|MAGNOLIA MUTUAL WATER | 40| 1| -97.50|\n|FREEPORT MARINA | 42| 3| -92.86|\n|PLANTATION MOBILE HOME PARK | 44| 10| -77.27|\n|TUNNEL TRAILER PARK | 44| 0| -100.00|\n|SEQUOIA WATER ASSOC | 54| 0| -100.00|\n|HAPPY HARBOR (SWS) | 60| 0| -100.00|\n\n\n:::\n:::\n\n\nBut for larger water systems, the estimated population values seem to be more in line with the population numbers in the original dataset. You can see this in @tbl-pop-est-large by, as above, comparing the `POPULATION` field, which contains the total population values from the water supplier dataset, with the `population_total` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field.\n\n\n::: {#tbl-pop-est-large .cell tbl-cap='10 Largest Water Systems by Population'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n arrange(desc(POPULATION)) %>%\n slice(1:10) %>%\n select(WATER_SY_1, POPULATION, population_total_count, population_percent_difference) %>%\n st_drop_geometry() %>%\n kable()\n```\n\n::: {.cell-output-display}\n\n\n|WATER_SY_1 | POPULATION| population_total_count| population_percent_difference|\n|:----------------------------------|----------:|----------------------:|-----------------------------:|\n|CITY OF SACRAMENTO MAIN | 510931| 516189| 1.03|\n|SACRAMENTO SUBURBAN WATER DISTRICT | 184385| 193126| 4.74|\n|SCWA - LAGUNA/VINEYARD | 172666| 145495| -15.74|\n|FOLSOM, CITY OF - MAIN | 68122| 62462| -8.31|\n|CITRUS HEIGHTS WATER DISTRICT | 65911| 68912| 4.55|\n|CALAM - SUBURBAN ROSEMONT | 53563| 57897| 8.09|\n|CALAM - PARKWAY | 48738| 58635| 20.31|\n|CALAM - LINCOLN OAKS | 47487| 42916| -9.63|\n|GOLDEN STATE WATER CO. - CORDOVA | 44928| 48115| 7.09|\n|ELK GROVE WATER SERVICE | 42540| 42647| 0.25|\n\n\n:::\n:::\n\n\n## Considerations for Detailed Population Estimates {#sec-detailed-pop-estimates}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nIf you're primarily only interested in population estimates (possibly including population by race/ethnicity, age, gender, etc.) and need an estimate that's as geographically accurate as possible, it may make more sense to use the block-level population data from the decennial census rather than block group level population data from the ACS. However, since the decennial census only occurs once every 10 years, those estimates won't reflect recent population changes (and will get especially less accurate as we get farther from the last decennial census). But keep in mind that even the 5-year ACS is an average that encompasses previous years' estimates, so it's not necessarily temporally precise either.\n\nIt's also possible to use the block-level decennial population data as a weighing factor for ACS population data (to allocate the population within block-group level ACS data).\n\n\\[TO DO: add example\\]\n\n## Considerations for Small / Rural Area Estimates {#sec-small-area-estimates}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nFor some water systems, the estimated population using the areal interpolation above (@sec-areal-interp) was at or near zero, and it may be useful to look at an example to see what's going on with one of those cases.\n\n(because the water system may encompass only a small portion of one or a few census units, and the entire census unit(s) may not be representative of the small portion(s)), especially those in rural environments (where population densities are lower, population centers tend to be spread out, and census units tend to be larger).\n\n\\[TO DO: insert map\\]\n\nFrom the map above \\[TO DO: insert map\\], you can see that the service area reported for some systems are very small, only covering a small fraction of a single census unit, resulting in a population estimate that is very low. In these cases, it could be that the system area was drawn incorrectly (i.e., maybe it doesn't really depict the entire service area), in which case the reported service area should be revised. Or, it's possible that the population within the given census unit is very un-evenly distributed and instead there's a relatively high density population cluster in the depicted service area, in which case a more sophisticated method than an area-weighted average should be used (e.g., maybe consider the density of buildings, roads, and/or other features associated with inhabited areas).\n\n## Alternative Computation Methods {#sec-alternative-methods}\n\n::: callout-warning\nThis section is in progress...\n:::\n\n### Population Weighted Interpolation {#sec-alternative-interpolate_pw}\n\nThe `tidycensus` package also has a function for population weighted interpolation, [`interpolate_pw`](https://walker-data.com/tidycensus/reference/interpolate_pw.html), but it uses a somewhat different methodology than the population weighted interpolation procedure applied above in @sec-pop-interp.\n\nNote that some water systems may not get an estimated value using this method, even if `NA` values are removed from the source data first (TO DO: check whether this depends on which type of boundary dataset is used - i.e., tigris with cb = FALSE or TRUE).\n\nUsing original `census_data_acs` variable gives multiple `NA`s - it looks like those are small areas:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresults_interpolate_pw <- interpolate_pw(from = census_data_acs %>%\n filter(!is.na(population_total_count)) %>% # population_total_count median_household_income\n select(population_total_count),\n to = water_systems_sac,\n to_id = 'WATER_SY_1',\n extensive = TRUE, # use FALSE for median_household_income\n weights = census_data_decennial,\n # weight_placement = 'surface',\n weight_column = 'population_total_count') %>%\n # rename(median_household_income_interpolate_pw = median_household_income) # rename results field\n rename(population_total_count_interpolate_pw = population_total_count) %>% \n mutate(population_total_count_interpolate_pw = round(population_total_count_interpolate_pw, 0))\n\n# sum(is.na(results_interpolate_pw$median_household_income_interpolate_pw))\nsum(is.na(results_interpolate_pw$population_total_count_interpolate_pw))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 17\n```\n\n\n:::\n:::\n\n\nUsing detailed block group geometry - looks like the same results?:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# # get detailed block group geometry\n# block_groups_detailed <- block_groups(state = 'CA',\n# county = 'Sacramento',\n# cb = FALSE,\n# year = acs_year) %>%\n# st_transform(crs_projected)\n# block_groups_detailed <- block_groups_detailed %>%\n# st_filter(water_systems_sac) %>%\n# select(GEOID)\n# \n# block_groups_detailed <- block_groups_detailed %>%\n# left_join(census_data_acs %>%\n# st_drop_geometry() %>%\n# select(GEOID, population_total_count, median_household_income),\n# by = 'GEOID')\n# \n# # interpolate\n# results_interpolate_pw_detailed <- interpolate_pw(from = block_groups_detailed %>%\n# filter(!is.na(population_total_count)) %>% # population_total_count median_household_income\n# select(population_total_count),\n# to = water_systems_sac,\n# to_id = 'WATER_SY_1',\n# extensive = TRUE, # use FALSE for median_household_income\n# weights = census_data_decennial,\n# weight_placement = 'surface',\n# weight_column = 'population_total_count') %>%\n# # rename(median_household_income_interpolate_pw = median_household_income) # rename results field\n# rename(population_total_count_interpolate_pw = population_total_count)\n# \n# # sum(is.na(results_interpolate_pw_detailed$median_household_income_interpolate_pw))\n# sum(is.na(results_interpolate_pw_detailed$population_total_count_interpolate_pw))\n```\n:::\n\n\nCompare results using `interpolate_pw` to reported population counts:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresults_interpolate_pw <- results_interpolate_pw %>%\n left_join(water_systems_sac %>%\n st_drop_geometry() %>%\n select(SERVICE_CO, POPULATION, WATER_SY_1),\n by = 'WATER_SY_1') %>% \n relocate(POPULATION, .after = population_total_count_interpolate_pw)\n```\n:::\n\n\nView results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(results_interpolate_pw)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 5\n$ WATER_SY_1 \"HOOD WATER MAINTENCE DIST [SWS]…\n$ population_total_count_interpolate_pw 74, 412, 96, NA, 1031, NA, NA, 1…\n$ POPULATION 100, 700, 40, 150, 256, 150, 32,…\n$ SERVICE_CO 82, 199, 34, 64, 128, 83, 28, 50…\n$ geometry MULTIPOLYGON (((-13…\n```\n\n\n:::\n:::\n\n\n## Working with Other Source Datasets {#sec-other-sources}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nIn addition to using census data, it's possible to use other datasets to compute characteristics of custom *target* areas like water systems. The process is generally likely to be similar to the processes shown above using census data, but each dataset may require unique considerations (e.g., to handle missing values, uncertain boundaries, etc.).\n\n### CalEnviroScreen {#sec-calenviroscreen}\n\n\\[TO DO: compute population weighted average scores, using interpolated population estimates - (1) first compute interpolated populations by clipping ACS tracts to CES boundaries, then (2) clip those boundaries to water systems and compute populations, then (3) calculate population weighted averages (using areal-weighted populations from step 2)?\\]\n\nNotes to consider:\n\n- Some census tracts are missing CES scores (overall and/or for certain indicators), and have to deal with those missing values somehow.\n\n- CES 4.0 is tract-level data, and uses 2010 census boundaries (so boundaries won't match current ACS boundaries)\n\n
\n", - "supporting": [], + "markdown": "---\ntitle: \"Estimating Demographics of Custom Spatial Features\"\nsubtitle: \"Accessing U.S. Census Bureau Data & Calculating Weighted Averages with Areal- and Population-Weighted Interpolation\"\nnumber-sections: true\ntoc: true\ntoc-depth: 4\nformat:\n html:\n self-contained: false\nbibliography: references.bib\n---\n\n```{=html}\n \n\n```\n\n## Background {#sec-background}\n\n::: callout-note\nFor comments, suggestions, corrections, or questions on anything below, contact [david.altare\\@waterboards.ca.gov](mailto:david.altare@waterboards.ca.gov), or [open an issue](https://github.com/daltare/example-census-race-ethnicity-calculation/issues) on github.\n:::\n\n::: callout-warning\nThis document is a work in progress, and may change significantly.\n:::\n\nThis document provides an example of how to use tools available from the [R programming language](https://www.R-project.org/) [@R] to estimate characteristics of any given *target* spatial area(s) (e.g., neighborhoods, project boundaries, water supplier service areas, etc.) based on data from a *source* dataset containing the characteristic data of interest (e.g., census data, CalEnvrioScreen scores, etc.), especially when the boundaries of the *source* and *target* areas overlap but don't necessarily align with each other. It also provides some brief background on the various types of data available from the U.S Census Bureau, and links to a few places to find more in-depth information.\n\nThis particular example estimates demographic characteristics of community water systems in the Sacramento County area (the *target* dataset). It uses the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package [@tidycensus] to access selected demographic data from the U.S. Census Bureau (the *source* dataset) for census units whose spatial extent covers those water systems' service areas, then uses the [`sf`](https://r-spatial.github.io/sf/) package [@sf] package (for working with spatial data) and the [`tidyverse`](https://www.tidyverse.org/) collection of packages [@tidyverse] (for general data cleaning and transformation) to estimate some demographic characteristics of each water system based on that census data. It also uses the [`areal`](https://chris-prener.github.io/areal/) R package [@areal] to check some of the results, and as general guidance on the principles and techniques for implementing areal interpolation.\n\nThis example is just intended to be a simplified demonstration of a possible workflow. For a real analysis, additional steps and considerations -- that may not be covered here -- may be needed to deal with data inconsistencies (e.g., missing or incomplete data), required level of precision and acceptable assumptions (e.g. more fine-grained datasets or more sophisticated techniques could be used to estimate/model population distributions), or other project-specific issues that might arise.\n\n## Setup {#sec-setup}\n\nThe code block below loads required packages for this analysis, and sets some user-defined options and defaults. If they aren't already installed on your computer, you can install them with the R command `install.packages('package-name')` (and replace `package-name` with the name of the package you want to install).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# packages ----\nlibrary(tidycensus)\nlibrary(tigris)\nlibrary(tidyverse)\nlibrary(sf)\nlibrary(areal)\nlibrary(janitor)\nlibrary(here)\nlibrary(units)\n# library(Polychrome)\nlibrary(knitr)\nlibrary(kableExtra)\nlibrary(tmap)\nlibrary(patchwork)\nlibrary(scales)\nlibrary(digest)\nlibrary(mapview)\nlibrary(biscale)\nlibrary(cowplot)\nlibrary(glue)\nlibrary(ggtext)\n\n# conflicts ----\nlibrary(conflicted)\nconflicts_prefer(dplyr::filter)\n\n# options ----\noptions(scipen = 999) # turn off scientific notation\noptions(tigris_use_cache = TRUE) # use data caching for tigris\n\n# reference system ----\ncrs_projected <- 3310 # set a common projected coordinate reference system to be used throughout this analysis - see: https://epsg.io/3310\n```\n:::\n\n\n## Census Data Overview {#sec-census-overview}\n\nThis section provides some brief background on the various types of data available from the U.S. Census Bureau (a later section - @sec-census-access - demonstrates how to retrieve data from the U.S. Census Bureau using the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package). Most of the information covered here comes from the book [Analyzing US Census Data: Methods, Maps, and Models in R](https://walker-data.com/census-r/index.html), which is a great source of information if you'd like more detail about any of the topics below [@walker2023].\n\n::: callout-note\nIf you're already familiar with Census data and want to skip this overview, go directly to the next section: @sec-system-boundaries\n:::\n\nDifferent census products/surveys contain data on different variables, at different geographic scales, over varying periods of time, and with varying levels of certainty. Therefore, there are a number of judgement calls to make when determining which type of census data to use for an analysis -- e.g., which data product to use (Decennial Census or American Community Survey), which geographic scale to use (e.g., Block, Block Group, Tract, etc.), what time frame to use, which variables to assess, etc.\n\nMore detailed information about U.S. Census Bureau's data products and other topics mentioned below is available [here](https://walker-data.com/census-r/the-united-states-census-and-the-r-programming-language.html#the-united-states-census-and-the-r-programming-language).\n\n### Census Unit Geography / Hierarchy {#sec-census-hierarchy}\n\nPublicly available datasets from the U.S Census Bureau generally consist of individual survey responses aggregated to defined census units (e.g., census tracts) that cover varying geographic scales. Some of these units are nested and can be neatly aggregated (e.g., each census tract is composed of a collection of block groups, and each block group is composed of a collection of blocks), while other census units are outside this hierarchy (e.g., Zip Code Tabulation Areas don't coincide with any other census unit). @fig-census-hierarchies shows the relationship of all of the various census units.\n\nCommonly used census statistical units like tracts and block groups have target population size ranges, and can be adjusted every 10 years (with the decennial census) based on population changes. For example, all ACS 5-year datasets prior to 2020 use the 2010 boundaries for tracts, block groups, and blocks, and all ACS 5-year datasets from [2020 onward](https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2020/geography-changes.html) (presumably through 2029) use the 2020 boundaries for those units. [Census tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13) are generally around 4,000 people, with a range from about 1,200 to 8,000, and [block groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4) generally contain 600 to 3,000 people. [Blocks](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_5) are the smallest census units, and are \"areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads\". For example, a census block may be \"a city block bounded on all sides by streets\", while \"blocks in suburban and rural areas may be larger, more irregular in shape, and bounded by a variety of features, such as roads, streams, and transmission lines\".\n\n::: callout-caution\nCensus boundaries can change over time. Commonly used statistical units like tracts, block groups, and blocks tend to be revised every 10 years (with the decennial census), so it's important to use a census boundary dataset that matches the version of the census demographic data you're retrieving; otherwise, the demographic data may not match geographic areas in your boundary dataset. In some cases, a census unit that exists in a given year of the census data may not exist at all in a different year's dataset, because census units can be split or merged when boundaries are revised.\n\nFor more information, see [here](https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_geography_handbook_2020_ch02.pdf) or [here](https://www.census.gov/programs-surveys/acs/geography-acs/geography-boundaries-by-year.html) or [here](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_6) or [here](https://www.census.gov/data/academy/data-gems/2021/compare-2020-census-and-2010-census-redistricting-data.html).\n:::\n\nFor a list of the different geographic units available for each of the different census products/surveys (see @sec-census-datasets) that can be accessed via the `tidycensus` package, go [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus).\n\n![Census Unit Hierarchies](https://walker-data.com/census-r/img/screenshots/census-hierarchies.png){#fig-census-hierarchies}\n\n### Census Datasets / Surveys {#sec-census-datasets}\n\nThe Decennial Census is conducted every 10 years, and is intended to provide a complete count of the US population and assist with political redistricting. As a result, it collects a relatively limited set of basic demographic data, but (should) provide a high degree of precision (i.e., in general it should provide exact counts). It is available for geographic units down to the census block (the smallest census unit available -- see @sec-census-hierarchy). For information about existing and planned future releases of 2020 census data products, go [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html).\n\nThe American Community Survey (ACS) provides a much larger array of demographic information than the Decennial Census, and is updated more frequently. The ACS is based on a sample of the population (rather than a count of the entire population, as in the Decennial Census), so it represents estimated values rather than precise counts; therefore, each data point is available as an estimate (typically labeled with an \"E\" in census variable codes, which are discussed in @sec-census-variables ) along with an associated margin of error (typically labeled with \"M\" or \"MOE\" in census variable codes) around its estimated value.\n\nThe ACS is available in two formats. The 5-year ACS is a rolling average of 5 years of data (e.g., the 2021 5-year ACS dataset is an average of the ACS data from 2017 through 2021), and is generally available for geographic units down to the census block group (though some 5-year ACS data may only be available at less granular levels). The 1-year ACS provides data for a single year, and is only available for geographies with population greater than 65,000 (e.g., large cities and counties). Therefore, only the 5-year ACS will be useful for any analysis at a relatively fine scale (e.g., anything that requires data at or more detailed than the census tract level, or any analysis that considers smaller counties/cities -- by definition, census tracts always contain significantly fewer than 65,000 people).\n\nIn addition to the Decennial Census and ACS data, a number of other census data products/surveys are also available. For example, see the `censusapi` R package ([here](https://github.com/hrecht/censusapi) or [here](https://www.hrecht.com/censusapi/index.html)) for access to over 300 census API endpoints. For historical census data, see the discussion [here](https://walker-data.com/census-r/other-census-and-government-data-resources.html?q=API%20endpoint#other-census-and-government-data-resources) on using NHGIS, IPUMS, and the `ipumsr` package.\n\n### Census Variables / Codes {#sec-census-variables}\n\nEach census product collects data for many different demographic variables, and each variable is generally associated with an identifier code. In order to access census data programmatically, you often need to know the code associated with each variable of interest. When determining which variables to use, you need to consider what census product contains those variables (see @sec-census-datasets) and how they differ in terms of time frame, precision, spatial granularity (see @sec-census-hierarchy), etc.\n\nThe `tidycensus` package offers a convenient generic way to search for variables across different census products using the `load_variables()` function, as described [here](https://walker-data.com/tidycensus/articles/basic-usage.html#searching-for-variables).\n\nThe following websites may also be helpful for exploring the various census data products and finding the variable names and codes they contain:\n\n- Census Reporter (for ACS data): (especially )\n\n- Census Bureau's list of variable codes, e.g.:\n\n - 2020 Census codes: \n\n - 2022 ACS 5 year codes: \n\n- Census Bureau's data interface (for Decennial Census and ACS, and other census datasets): \n\n- National Historical Geographic Information System (NHGIS) (for ACS data and historical decennial Census data): \n\n## Target Data Boundaries (Water Systems) {#sec-system-boundaries}\n\nIn this section, we'll get the service area boundaries for Community Water Systems within the Sacramento County area. This will serve as the *target* dataset – i.e., the set of areas which we'll be estimating the characteristics of – and will also be used to specifying what census data we want to retrieve. We'll also get a dataset of county boundaries which overlap the water service areas in this study, which can also help with specifying what census data to access and/or with making maps and visualizations.\n\n### Read Water System Data\n\nIn this case, we'll get the water system dataset from a shapefile that's saved locally, then transform that dataset into a common coordinate reference system for mapping and analysis (which is defined above in the variable `crs_projected`).\n\nThis water system dataset comes from the [California Drinking Water System Area Boundaries dataset](https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc). For this example, the dataset has been pre-filtered for systems within Sacramento County (by selecting records where the `COUNTY` field is \"SACRAMENTO\") and for Community Water Systems (by selecting records where the `STATE_CLAS` field is \"COMMUNITY\"). Some un-needed fields have also been dropped, remaining fields have been re-orderd.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_systems_sac <- st_read(here('02_data_input', \n 'water_supplier_boundaries_sac', \n 'System_Area_Boundary_Layer_Sac.shp')) %>% \n st_transform(crs_projected) # transform to common coordinate system\n```\n:::\n\n\nWe can use the `glimpse` function (below) to take get a sense of what type of information is available in the water system dataset and how it's structured.\n\nNote that this dataset already includes a `POPULATION` variable that indicates the population served by each water system. However, for this analysis we'll be making our own estimate of the population within each system's service area based on U.S. Census Bureau data and the spatial representation of the system boundaries. I don't know exactly how the `POPULATION` variable was derived in this dataset, and it likely will not exactly match the population estimates from this analysis, but may serve as a useful check to make sure our estimates are reasonable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_systems_sac)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 12\n$ WATER_SY_1 \"HOOD WATER MAINTENCE DIST [SWS]\", \"MC CLELLAN MHP\", \"MAGNO…\n$ WATER_SYST \"CA3400101\", \"CA3400179\", \"CA3400130\", \"CA3400135\", \"CA3400…\n$ GLOBALID \"{36268DB3-9DB2-4305-A85A-2C3A85F20F34}\", \"{E3BF3C3E-D516-4…\n$ BOUNDARY_T \"Water Service Area\", \"Water Service Area\", \"Water Service …\n$ OWNER_TYPE \"L\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"P\",…\n$ COUNTY \"SACRAMENTO\", \"SACRAMENTO\", \"SACRAMENTO\", \"SACRAMENTO\", \"SA…\n$ REGULATING \"LPA64 - SACRAMENTO COUNTY\", \"LPA64 - SACRAMENTO COUNTY\", \"…\n$ FEDERAL_CL \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUN…\n$ STATE_CLAS \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUNITY\", \"COMMUN…\n$ SERVICE_CO 82, 199, 34, 64, 128, 83, 28, 50, 164, 5684, 14798, 115, 33…\n$ POPULATION 100, 700, 40, 150, 256, 150, 32, 100, 350, 18005, 44928, 20…\n$ geometry MULTIPOLYGON (((-132703 403..., MULTIPOLYGON (…\n```\n\n\n:::\n:::\n\n\n#### Alternative Data Retrieval Method\n\nReading in data from a shapefile is shown above because it's likely one of the more common ways that users will access their *target* boundary data. However, depending on the dataset, there may be other ways to access the data. For example, the code chunk below demonstrates an alternative -- using the [`arcgislayers`](https://r.esri.com/arcgislayers/index.html) package [@arcgislayers] -- that connects directly to the source dataset (to retrieve the most recent version) and applies the filters needed to reproduce the dataset in the `System_Area_Boundary_Layer_Sac.shp` file.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load arcgislayers package (see: https://r.esri.com/arcgislayers/index.html)\ninstall.packages('pak') # only needed if the pak package is not already installed\npak::pkg_install(\"R-ArcGIS/arcgislayers\", dependencies = TRUE)\n\nlibrary(arcgislayers)\n\n# define link to data source\nurl_feature <- 'https://gispublic.waterboards.ca.gov/portalserver/rest/services/Drinking_Water/California_Drinking_Water_System_Area_Boundaries/FeatureServer/0'\n\n# connect to data source\nwater_systems_feature_layer <- arc_open(url_feature)\n\n# download and filter data from source\nwater_systems_sac <- arc_select(\n water_systems_feature_layer,\n # apply filters\n where = \"COUNTY = 'SACRAMENTO' AND STATE_CLASSIFICATION = 'COMMUNITY'\",\n # select fields\n fields = c('WATER_SYSTEM_NAME', 'WATER_SYSTEM_NUMBER', 'GLOBALID',\n 'BOUNDARY_TYPE', 'OWNER_TYPE_CODE', 'COUNTY',\n 'REGULATING_AGENCY', 'FEDERAL_CLASSIFICATION', 'STATE_CLASSIFICATION',\n 'SERVICE_CONNECTIONS', 'POPULATION')) %>%\n # transform to common coordinate system\n st_transform(crs_projected) %>%\n # rename fields to match names from the shapefile (which automatically truncates field names)\n rename(WATER_SY_1 = WATER_SYSTEM_NAME,\n WATER_SYST = WATER_SYSTEM_NUMBER,\n BOUNDARY_T = BOUNDARY_TYPE,\n OWNER_TYPE = OWNER_TYPE_CODE,\n REGULATING = REGULATING_AGENCY,\n FEDERAL_CL = FEDERAL_CLASSIFICATION,\n STATE_CLAS = STATE_CLASSIFICATION,\n SERVICE_CO = SERVICE_CONNECTIONS)\n```\n:::\n\n\n### Get County Boundaries {#sec-county-boundaries}\n\nWhen accessing census data using the `tidycensus` R package as shown below (in @sec-census-access), it's often useful (though not strictly required) to know which counties overlap the target dataset (note that, even though the dataset is filtered for systems in Sacramento county, there are some systems whose boundaries extend into neighboring counties). County boundaries may also be useful for making maps in later stages of the analysis. We can get a dataset of county boundaries in California from the [TIGER dataset](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html), which can be accessed with R using the [`tigris`](https://github.com/walkerke/tigris) R package [@tigris].\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncounties_ca <- counties(state = 'CA', \n cb = TRUE) %>% # simplified\n st_transform(crs_projected) # transform to common coordinate system\n```\n:::\n\n\nThen, we can get a list of counties that overlap with the boundaries of the Sacramento area community water systems obtained above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncounties_overlap <- counties_ca %>% \n st_filter(water_systems_sac, \n .predicate = st_overlaps)\n\ncounties_list <- counties_overlap %>% pull(NAME)\n```\n:::\n\n\nThe counties in the `counties_list` variable are: San Joaquin, Yolo, Placer, Sacramento.\n\n### Plot Target Data\n\n@fig-suppliers-counties shows the water systems and county boundaries in an interactive map.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmapview(counties_overlap, \n alpha.regions = 0, \n zcol = 'NAME', \n layer.name = 'County', \n legend = FALSE) + \n mapview(water_systems_sac, \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE)\n```\n\n::: {#fig-suppliers-counties .cell-output-display}\n\n```{=html}\n
\n\n```\n\n\nSelected water systems (with county boundaries for reference).\n:::\n:::\n\n\n## Accessing Census Data {#sec-census-access}\n\nThe following sections demonstrate how to retrieve census data from the Decennial Census and the ACS using the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package.\n\nIn order to use the `tidycensus` R package, you'll need to obtain a personal API key from the US Census Bureau (which is free and available to anyone) by signing up here: . Once you have your API key, you'll need to register it in R by entering the command `census_api_key(key = \"YOUR API KEY\", install = TRUE)` in the console. Note that the `install = TRUE` argument means that the key is saved for all future R sessions, so you'll only need to run that command once on your computer (rather than including it in your scripts). Alternatively, you could save your key to an environment variable and retrieve it using `Sys.getenv()`. Either way will help you avoid the possibility of entering your API key into any scripts that could be shared publicly.\n\n::: callout-caution\nBecause the boundaries of census units (e.g., tracts, block groups, blocks, etc) can change over time, it's important to make sure that the version (year) of the census data you're retrieving matches the version of the census boundary dataset you're using. The methods shown below retrieve the census boundary dataset together with the census demographic data, which ensures that this won't be a potential problem. However, if you use a different workflow that retrieves the geographic boundaries and demographic data via separate processes, you should ensure that the versions are consistent.\n:::\n\n### Decennial Census {#sec-census-access-decennial}\n\nThis section retrieves census data from the Decennial Census, using the `get_decennial` function from the `tidycensus` package. As of this writing, the most recent version of the decennial census data available is from 2020, and we can set that as a variable below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# set year\ndecennial_year <- 2020\n```\n:::\n\n\nNext, we can define the list of demographic variables we'd like to retrieve tabular data for, by saving the census variables we want in the `census_vars_decennial` object (see @sec-census-variables for more information about how to discover variables of interest and find their associated codes). Note that here we're providing descriptive names associated with each variable code, which makes the data easier to work with later, but isn't strictly necessary (i.e., you could just supply the variable codes alone).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# define variables to pull from the decennial census\ncensus_vars_decennial <- c(\n 'population_hispanic_or_latino_count' = 'P2_002N', # Total Hispanic or Latino\n 'population_white_count' = 'P2_005N', # White (Not Hispanic or Latino)\n 'population_black_or_african_american_count' = 'P2_006N', # Black or African American (Not Hispanic or Latino)\n 'population_native_american_or_alaska_native_count' = 'P2_007N', # American Indian and Alaska Native (Not Hispanic or Latino)\n 'population_asian_count' = 'P2_008N', # Asian (Not Hispanic or Latino)\n 'population_pacific_islander_count' = 'P2_009N', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)\n 'population_other_count' = 'P2_010N', # Some other race (Not Hispanic or Latino)\n 'population_multiple_count' = 'P2_011N', # Two or more races (Not Hispanic or Latino)\n 'population_total_count' = 'P2_001N'\n)\n```\n:::\n\n\nThen, we can create an object that we can use to filter our request to the census API so that it will only return census units that overlap with our target areas (the object will be passed to the `filter_by` argument of the `get_decennial` function below). Note that this isn't strictly necessary (you could also apply the filter after making the API request), but may helpful to speed the query and reduce memory usage, especially in the case of large queries.\n\n::: callout-note\nAt the time of this writing, the `filter_by` argument of the tidycensus `get_decennial` function is fairly new, and not yet included in the official documentation.\n\nAlso, the `filter_by` argument is optional, and only appears to accept a simple features (sf) object with a single row / feature (e.g., a single water system), and will not accept an sf object with multiple rows / features. The process below attempts to work around this constraint by joining all of the selected water systems into a single multi-part polygon (i.e., an sf object with a single row). However, if you only want to retrieve data for census units that overlap a single target area (e.g., a single water system), you can skip this step.\n:::\n\n\n::: {.cell}\n\n```{#lst-filter_obj .r .cell-code lst-cap=\"Create object for filtering the API query\"}\nwater_systems_filter <- water_systems_sac %>% \n st_union() %>% \n st_as_sf()\n```\n:::\n\n\nFinally, we can make the data request, using the `get_decennial` function, which accepts several arguments that specify exactly what data to return.\n\nFor this example we're getting data at the 'Block' level (with the `geography = 'block'` argument) for the demographic variables defined above in the `census_vars_decennial` object (which is passed to the `variables` argument). As noted above, block-level data is the most granular level of spatial data available, and should provide the best results when estimating demographics for areas whose boundaries don't align with census unit boundaries. However, depending on the use case, it may require too much time and computational resources to use the most granular spatial data, and may not be necessary to obtain a reasonable estimate. Also, keep in mind that block-level data may not be available for all variables, and some variables may only be available at less granular spatial scales (like block groups or tracts).\n\nIn addition to the tabular data associated with the demographic variables in our list, we'll also get the spatial data -- i.e., the boundaries of the census blocks -- by setting the `geometry = TRUE` argument. When we do this, the tabular demographic data is pre-joined to the spatial data, so the API request returns a single dataset with both the spatial and attribute (demographic) data combined.\n\n::: callout-note\nThe `tidycensus` package generally returns the Census Bureau's [cartographic boundary shapefiles](https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html) by default (as opposed to the [core TIGER/Line shapefiles](https://www.census.gov/geo/maps-data/data/tiger-line.html), which is the default format returned by the `tigris` R package). The default cartographic boundary shapefiles are pre-clipped to the US coastline, and are smaller/faster to process (alternatively you can use `cb = FALSE` to get the core TIGER/Line data) (see [here](https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html#better-cartography-with-spatial-overlay)). So the default spatial data returned by `tidycensus` may be somewhat different than the default spatial data returned by the `tigris` package, but in general I find it's best to use the default `tidycensus` spatial data.\n:::\n\n::: callout-warning\nAt the block level, it appears that `tidycensus` only returns the more detailed core TIGER/Line shapefiles (i.e., they are identical to the default block-level geographic data returned by `tigris`). In some cases, that can create minor inconsistencies when working with both blocks and block groups and using the default geographies.\n:::\n\nWe also narrow down the search parameters geographically by specifying the state (with `state = 'CA'`) and counties (`county = counties_list`) we're seeking data for.\n\n::: callout-note\nSupplying a list of counties may not be strictly necessary, especially in cases where you supply the optional `filter_by` argument. However, especially when working with granular data like blocks, supplying the county argument seems to greatly speed the API request.\n:::\n\nAlso, while by default the `tidycensus` package returns data in long/tidy format, we're getting the data in wide format for this example (by specifying `output = 'wide'`) because it'll be easier to work with for the interpolation method described below to estimate demographics for non-census geographies.\n\n\n::: {.cell}\n\n```{#lst-get_decennial .r .cell-code lst-cap=\"Retrieve decennial census data\"}\n# get census data\ncensus_data_decennial <- get_decennial(geography = 'block', # can be 'block', 'block group', 'tract', 'county', etc.\n state = 'CA', \n county = counties_list,\n filter_by = water_systems_filter,\n year = decennial_year,\n variables = census_vars_decennial,\n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) # convert to common coordinate system\n```\n:::\n\n\nThe output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the population of each racial/ethnic group is reported in a separate column. Here's a view of the contents and structure of the Decennial Census data that's returned:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_decennial)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 17,745\nColumns: 12\n$ GEOID \"060670019003011\", \"…\n$ NAME \"Block 3011, Block G…\n$ population_hispanic_or_latino_count 4, 6, 8, 11, 1, 14, …\n$ population_white_count 20, 4, 167, 70, 86, …\n$ population_black_or_african_american_count 2, 2, 0, 8, 9, 18, 0…\n$ population_native_american_or_alaska_native_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_asian_count 19, 5, 2, 1, 23, 8, …\n$ population_pacific_islander_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_other_count 0, 0, 0, 0, 0, 0, 0,…\n$ population_multiple_count 8, 3, 4, 10, 5, 10, …\n$ population_total_count 53, 20, 181, 100, 12…\n$ geometry POLYGON ((-1…\n```\n\n\n:::\n:::\n\n\n### American Community Survey (ACS) {#sec-census-access-acs}\n\nTo get data from the ACS, you can use the `get_acs()` function, which is very similar to the `get_decennial()` function used above. As of this writing, the most recent version of the 5-year ACS data available is the 2018-2022 ACS, and we can set that as a variable below (which makes it easier to update this document in future years).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# set year\nacs_year <- 2022\n```\n:::\n\n\nHowever, since the ACS data contains data on a much broader set of socio-economic metrics, the requested data includes a greatly expanded list of variables, defined in the `census_vars_acs` object (see @sec-census-variables for more information about how to discover variables of interest and find their associated codes). As above, we can provide descriptive names associated with each variable code, which makes the data easier to work with later, but isn't strictly necessary (i.e., you could just supply the variable codes alone).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# define variables to pull from the ACS\ncensus_vars_acs <- c(\n # --- population variables ---\n 'population_total_count' = 'B01003_001',\n 'population_hispanic_or_latino_count' = 'B03002_012', # Total Hispanic or Latino\n 'population_white_count' = 'B03002_003', # White (Not Hispanic or Latino)\n 'population_black_or_african_american_count' = 'B03002_004', # Black or African American (Not Hispanic or Latino)\n 'population_native_american_or_alaska_native_count' = 'B03002_005', # American Indian and Alaska Native (Not Hispanic or Latino)\n 'population_asian_count' = 'B03002_006', # Asian (Not Hispanic or Latino)\n 'population_pacific_islander_count' = 'B03002_007', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)\n 'population_other_count' = 'B03002_008', # Some other race (Not Hispanic or Latino)\n 'population_multiple_count' = 'B03002_009', # Two or more races (Not Hispanic or Latino)\n \n # --- poverty variables ---\n 'poverty_total_assessed_count' = 'B17021_001', # also available from 'B17020_001' (at the tract level only). Total population for whom poverty status is determined. Poverty status was determined for all people except institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years old. These groups were excluded from the numerator and denominator when calculating poverty rates.\n 'poverty_below_count' = 'B17021_002', # also available from 'B17020_002' (at the tract level only). Population whose income in the past 12 months is below federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.\n 'poverty_above_count' = 'B17021_019', # also available from 'B17020_010' (at the tract level only). Population whose income in the past 12 months is at or above federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.\n \n # --- household variables ---\n 'households_count' = 'B19001_001', # also available from variable 'B19053_001'. A household includes all the people who occupy a housing unit - a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied. People not living in households are classified as living in group quarters.\n 'average_household_size' = 'B25010_001', # A measure obtained by dividing the number of people living in occupied housing units by the total number of occupied housing units. This measure is rounded to the nearest hundredth.\n \n # --- household income variables ---\n 'median_household_income' = 'B19013_001', # also available from 'B19019_001' (at the tract level only). Income in the past 12 months is the sum of wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income.\n 'households_income_below_10k_count' = 'B19001_002', # count of households with income below $10,000 \n 'households_income_10k_15k_count' = 'B19001_003', # count of households with income $10,000 to $15,000 \n 'households_income_15k_20k_count' = 'B19001_004', \n 'households_income_20k_25k_count' = 'B19001_005', \n 'households_income_25k_30k_count' = 'B19001_006', \n 'households_income_30k_35k_count' = 'B19001_007', \n 'households_income_35k_40k_count' = 'B19001_008', \n 'households_income_40k_45k_count' = 'B19001_009', \n 'households_income_45k_50k_count' = 'B19001_010', \n 'households_income_50k_60k_count' = 'B19001_011', \n 'households_income_60k_75k_count' = 'B19001_012', \n 'households_income_75k_100k_count' = 'B19001_013', \n 'households_income_100k_125k_count' = 'B19001_014', \n 'households_income_125k_150k_count' = 'B19001_015', \n 'households_income_150k_200k_count' = 'B19001_016',\n 'households_income_above_200k_count' = 'B19001_017', # count of households with income above $200,000\n\n # --- housing costs variables (% of household income) ---\n # Housing Costs as a Percentage of Household Income in the past 12 months - NOTE: THIS TABLE IS NEW FOR THE 2022 ACS, AND WON'T BE AVAILABLE FOR PREVIOUS YEARS - Table B25140 shows the count of households paying more than 30% of their income towards housing costs broken out by three tenure categories (owned with a mortgage, owned without a mortgage, and rented). The table also shows the number of households paying more than 50% of their income toward housing costs.\n # 'households_count' = 'B25140_001', \n 'households_mortgage_total_count' = 'B25140_002',\n 'households_mortgage_over30pct_count' = 'B25140_003',\n 'households_mortgage_over50pct_count' = 'B25140_004',\n 'households_no_mortgage_total_count' = 'B25140_006',\n 'households_no_mortgage_over30pct_count' = 'B25140_007',\n 'households_no_mortgage_over50pct_count' = 'B25140_008',\n 'households_rent_total_count' = 'B25140_010',\n 'households_rent_over30pct_count' = 'B25140_011',\n 'households_rent_over50pct_count' = 'B25140_012',\n \n # --- other income / economic variables ---\n 'per_capita_income' = 'B19301_001' # note: per capita income by race (at block group level) available in table B19301I\n)\n```\n:::\n\n\nFinally, we can make the data request, using the `get_acs` function, which is very similar to the `get_decennial` function described above ( @sec-census-access-decennial). However, for this example we're getting data at the 'Block Group' level (with the `geography = 'block group'` argument), which is the most granular level of spatial data available for ACS data. But, keep in mind that block group-level data may not be available for all variables, and some variables may only be available at less granular spatial scales (like tracts). Note that the `water_systems_filter` object supplied to the `filter_by` argument was created above in @lst-filter_obj.\n\n\n::: {.cell}\n\n```{#lst-get_acs .r .cell-code lst-cap=\"Retrieve ACS data\"}\n# get census data\ncensus_data_acs <- get_acs(geography = 'block group',\n state = 'CA', \n county = counties_list,\n filter_by = water_systems_filter,\n year = acs_year,\n survey = 'acs5',\n variables = census_vars_acs, \n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) # convert to common coordinate system\n```\n:::\n\n\nAs above, the output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the each demographic variable is reported in a separate column. Here's a view of the contents and structure of the 2022 5-year ACS data that's returned (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_acs[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 1,054\nColumns: 21\n$ GEOID \"060670081451\", \"06…\n$ NAME \"Block Group 1; Cen…\n$ population_total_countE 1768, 1881, 1098, 2…\n$ population_total_countM 520, 585, 395, 583,…\n$ population_hispanic_or_latino_countE 38, 327, 376, 782, …\n$ population_hispanic_or_latino_countM 59, 298, 280, 315, …\n$ population_white_countE 1627, 1337, 293, 18…\n$ population_white_countM 521, 475, 191, 460,…\n$ population_black_or_african_american_countE 0, 1, 272, 26, 351,…\n$ population_black_or_african_american_countM 13, 3, 251, 38, 334…\n$ population_native_american_or_alaska_native_countE 41, 0, 0, 26, 0, 0,…\n$ population_native_american_or_alaska_native_countM 58, 13, 13, 42, 13,…\n$ population_asian_countE 45, 0, 105, 58, 144…\n$ population_asian_countM 71, 13, 116, 66, 18…\n$ population_pacific_islander_countE 0, 98, 0, 0, 27, 13…\n$ population_pacific_islander_countM 13, 98, 13, 13, 50,…\n$ population_other_countE 0, 0, 39, 0, 0, 0, …\n$ population_other_countM 13, 13, 63, 13, 13,…\n$ population_multiple_countE 17, 118, 13, 39, 15…\n$ population_multiple_countM 27, 125, 20, 57, 25…\n$ geometry POLYGON ((-…\n```\n\n\n:::\n:::\n\n\nNote that the dataset that's returned includes fields corresponding to Margin of Error (MOE) for each variable we've requested (these are the fields that end with two digits and an M -- e.g., \"001M\"), since, as noted above in @sec-census-datasets , the ACS is based on a sample of the population and reports estimated values.\n\n::: callout-tip\nIt is possible to calculate MOEs for derived estimates – e.g., when aggregating groups of census units – and in many cases it may be worthwhile to do that to provide extra context to the data. However, it may be difficult to do for more complex aggregations, such as the areal interpolation shown below. For guidance on how calculate MOEs for some types of derived estimates, see [this document](https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch08.pdf).\n\n`tidycensus` also has functions for calculating derives margins of error based on Census-supplied formulas, including [`moe_sum()`](https://walker-data.com/tidycensus/reference/moe_sum.html), [`moe_product()`](https://walker-data.com/tidycensus/reference/moe_product.html), [`moe_ratio()`](https://walker-data.com/tidycensus/reference/moe_ratio.html), and [`moe_prop()`](https://walker-data.com/tidycensus/reference/moe_prop.html).\n:::\n\nBecause we won't be incorporating those MOEs into the analysis below, we can drop them for this example, then clean up the field names.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# drop MOE fields\ncensus_data_acs <- census_data_acs %>% \n select(-matches('M$')) # the $ specifies \"ends with\"\n\n# clean names\nnames(census_data_acs) <- names(census_data_acs) %>% \n str_remove('E$') %>% # remove 'E' (estimate) from field names\n str_replace('NAM', 'NAME') # add 'E' back to NAME field\n```\n:::\n\n\nHere's a view of the contents and structure of the revised 2022 5-year ACS dataset (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(census_data_acs[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 1,054\nColumns: 21\n$ GEOID \"060670081451\", \"060…\n$ NAME \"Block Group 1; Cens…\n$ population_total_count 1768, 1881, 1098, 27…\n$ population_hispanic_or_latino_count 38, 327, 376, 782, 3…\n$ population_white_count 1627, 1337, 293, 181…\n$ population_black_or_african_american_count 0, 1, 272, 26, 351, …\n$ population_native_american_or_alaska_native_count 41, 0, 0, 26, 0, 0, …\n$ population_asian_count 45, 0, 105, 58, 144,…\n$ population_pacific_islander_count 0, 98, 0, 0, 27, 13,…\n$ population_other_count 0, 0, 39, 0, 0, 0, 0…\n$ population_multiple_count 17, 118, 13, 39, 15,…\n$ poverty_total_assessed_count 1768, 1847, 1098, 27…\n$ poverty_below_count 101, 328, 272, 116, …\n$ poverty_above_count 1667, 1519, 826, 263…\n$ households_count 680, 718, 405, 905, …\n$ average_household_size 2.59, 2.62, 2.71, 2.…\n$ median_household_income 123500, 66768, 56216…\n$ households_income_below_10k_count 18, 47, 10, 22, 6, 1…\n$ households_income_10k_15k_count 0, 0, 24, 0, 15, 231…\n$ households_income_15k_20k_count 0, 13, 18, 0, 51, 12…\n$ geometry POLYGON ((-1…\n```\n\n\n:::\n:::\n\n\nFor further analysis, we may want to get the statewide data as a baseline for comparison (this could also be done for other scales, like the county level). We can use a similar process to get that data and clean/format it to match the more detailed data obtained above. Note that in this case we're also using the 5-year ACS (even though the 1-year ACS is also available at the statewide level, and would provide more up-to-date data) so that the statewide data will be directly comparable to the block group level data obtained above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_acs_state <- get_acs(geography = 'state',\n state = 'CA', \n year = acs_year,\n survey = 'acs5',\n variables = census_vars_acs, \n output = 'wide', # can be 'wide' or 'tidy'\n geometry = TRUE,\n cache_table = TRUE) %>% \n st_transform(crs_projected) %>% # convert to common coordinate system\n select(-matches('M$')) %>% # the $ specifies \"ends with\"\n # clean names (note this is a little different than the way we renamed fields above, either works)\n rename_with(.fn = ~ str_remove(., # remove 'E' (estimate) from field names\n pattern = 'E$')) %>% \n rename_with(.fn = ~ str_replace(., # add 'E' back to NAME field\n pattern = 'NAM', \n replacement = 'NAME'))\n```\n:::\n\n\n### Plot Census & Supplier Data {#sec-census-plot}\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsystem_plot <- 'SACRAMENTO SUBURBAN WATER DISTRICT'\n```\n:::\n\n\n@fig-suppliers-census-map shows the 2022 5-year ACS census units that overlap with one of the water systems (Sacramento Suburban Water District) that we'll compute demographics for below (plotting the census units that overlap all systems tends to be slow in this format).\n\n::: {#fig-suppliers-census-map}\n\n::: {.cell}\n\n```{.r .cell-code}\n# label: fig-suppliers-census-map\n# fig-cap: \"Water system (filled polygon) and boundaries of census units (light blue) that will be used to estimate water system demographics.\"\n\nmapview(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot), \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE) +\n mapview(census_data_acs %>% \n st_filter(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot)), \n alpha.regions = 0, \n color = 'cyan', \n lwd = 1.3, label = 'NAME', \n layer.name = 'ACS Data', \n legend = FALSE) # zcol = 'NAME'\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\nWater system Sacramento Suburban Water District (filled polygon) and boundaries of census units (light blue) that will be used to estimate water system demographics.\n:::\n\n## Compute Water System Demographics {#sec-estimate-demographics}\n\nNow we can perform the calculations to estimate demographic characteristics for our *target* areas (water system service boundaries in the Sacramento County area) from our *source* demographic dataset (the census data we obtained above). For this example, we'll use the 2022 5-year ACS data that we retrieved above (which is saved in the `census_data_acs` variable) as our source of demographic data, and we'll estimate the following for each water system's service area:\n\n- Population of each racial/ethnic group (using the racial/ethnic categories defined in the census dataset), and each racial/ethnic group's portion of the total service area population\n- Socio-economic variables like poverty rate, median household income, income distributions, and per capita income\n\nThere are multiple ways this estimation can be done. For this example, we'll employ a three step strategy:\n\n1. Estimate values for count-based variables (typically referred to as 'extensive' data types) -- e.g., total population, popultion by race/ethnicity, population above / below poverty rate, households by income bracket -- for overlapping census unit, using areal interpolation. This is essentially an area weighted average, which estimates how much of each *source* unit's (census unit) count applies to the *target* area (a given water service area), based on the portion of its area that overlaps that target area -- for more information about the process, see [this documentation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html) from the `areal` R package. For example, for a census unit that partially overlaps a service area, only a fraction of its count for a given variable will be applied to that service area; for a census unit that completely overlaps a service area, the full count for that variable will be applied to the service area.\n\n The major simplifying assumption of this approach is that the population or count-based variable of interest are evenly distributed within each unit in the *source* data. For example, in this case we're assuming that population (including the total population and the population of each racial/ethic group), households of each income bracket, populations above / below the poverty rate, etc. are evenly distributed within each census block group.\n\n::: callout-tip\nWhile this section uses the block group-level count data from the 5-year ACS, there may be cases where it could be useful or necessary to use more granular block-level population data from the decennial census to estimate population densities and distributions within larger census units, like block groups and tracts. This could especially be the case when estimating characteristics for small areas in rural environments. See @sec-small-area-estimates and/or @sec-detailed-pop-estimates for more information.\n:::\n\n2. Using the estimated count data (populations, households, etc), compute weighted values for variables that describe those populations, using the associated count data as a weighting factor (e.g., population-weighted values for population based data, or household-weighted values for household-based data) -- these variables are typically referred to as 'intensive' data types.\n\n::: callout-tip\nAlthough it's possible to use areal interpolation to aggregate these variables as well, the multi-step approach described here can be useful because we know (from the population / household count data) that population densities differ between census units. Since we have a reasonable estimate of the count data (population, households, etc) within each census unit, using a population or household weighted average likely will yield more accurate results than a simple area-weighted average for these variables. For example, for per capita income, we can use the estimated population counts to produce a population weighted average per capita income (rather than an area weighted average per capita income, which is likely less meaningful as it over-weights large census areas with lower population densities). Areal interpolation may be more useful for cases where we generally have no other information about how density varies between the source polygons (unless significantly more effort is invested, such as looking at aerial imagery data)\n:::\n\n3. Aggregate interpolated values at the water system level.\n\n### Prepare Census Data\n\nNote that we already transformed the 2022 5-year ACS dataset into the common projected coordinate reference system used for this example immediately after we downloaded the data using the `get_acs()` function (see @lst-get_acs). This allows us to work with the water system data and the census data together in a common coordinate system.\n\nBefore calculating demographics for the *target* areas, we can do a bit of additional transformation to prepare the census data if needed. For example, we can combine the 'other' and 'multiple' racial/ethnic groupings into one 'other or multiple' racial/ethnic group.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## combine other and multiple\ncensus_data_acs <- census_data_acs %>% \n mutate('population_other_or_multiple_count' = population_other_count + population_multiple_count, \n .after = population_pacific_islander_count) %>% \n select(-c(population_other_count, population_multiple_count))\n```\n:::\n\n\nWe can also calculate the poverty rate for each census unit (which may be useful for presenting results later).\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_acs <- census_data_acs %>% \n mutate(poverty_rate_pct_calc_census_unit = case_when(\n poverty_total_assessed_count == 0 ~ 0,\n .default = 100 * poverty_below_count / poverty_total_assessed_count\n ), \n .after = poverty_above_count)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# We can also drop census units with zero population, since they won't contribute anything to our calculations.\n\n## drop census units with zero population\n# census_data_acs <- census_data_acs %>% \n# filter(population_total > 0)\n```\n:::\n\n\n### Interpolation Step 1: Areal Interpolation (for Count Variables) {#sec-areal-interp}\n\nThere are a couple of ways to implement the areal interpolation method. The example below 'manually' implements the process using functions from the `sf` package, for reasons described below. However, note that there are R packages which make it possible to perform areal interpolation with a single function - for example, the `sf` package's [`st_interpolate_aw`](https://r-spatial.github.io/sf/reference/interpolate_aw.html) function and the [`areal`](https://chris-prener.github.io/areal/) package's [`aw_interpolate`](https://chris-prener.github.io/areal/reference/aw_interpolate.html) function. This example uses a more 'manual' approach because this makes it possible to use the multi-step process described above, and also produces useful intermediate calculated data for mapping and visualization. However, we can use the single-function approach to double check our implementation of the areal interpolation approach for the count data (see @sec-check-areal-interp).\n\nFirst, we clip the census data to the water system boundaries:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_clip <- census_data_acs %>% \n mutate(cenus_unit_area = st_area(.)) %>% \n st_intersection(water_systems_sac) %>% \n mutate(clipped_area = st_area(.)) %>% \n mutate(areal_weight_factor = drop_units(clipped_area / cenus_unit_area))\n```\n:::\n\n\n@fig-map-clipped-polygons shows a plot of the census units clipped to the Sacramento Suburban Water District water system, along with the original/complete census units. Note that you can toggle layers on and off (and change their order of appearance) using the layers button in the upper left part of the map (below the zoom buttons).\n\n::: {#fig-map-clipped-polygons}\n\n::: {.cell}\n\n```{.r .cell-code}\n# label: fig-map-clipped-polygons\n# fig-cap: \"Water systems (filled polygons), boundaries of overalpping census units (grey), and clipped portions of census units (light blue) that will be used to estimate water system demographics.\"\n\nmapview(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot), \n zcol = 'WATER_SY_1', \n layer.name = 'Water System', \n legend = FALSE) + \n mapview(census_data_acs %>% \n st_filter(water_systems_sac %>% \n filter(WATER_SY_1 == system_plot)), \n alpha.regions = 0.15, \n col.regions = 'grey', \n color = 'black', \n lwd = 1, \n label = 'NAME', \n layer.name = 'ACS Data Full', \n legend = FALSE) +\n mapview(census_data_clip %>% \n filter(WATER_SY_1 == system_plot),\n alpha.regions = 0, \n color = 'cyan', \n lwd = 1.3, \n label = 'NAME', \n layer.name = 'ACS Data Clipped', \n legend = FALSE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\nWater system Sacramento Suburban Water District (filled polygon), boundaries of overalpping census units (grey), and clipped portions of census units (light blue) that will be used to estimate water system demographics.\n:::\n\nNext, we can compute the area-weighted counts for the portions of census units that overlap each water system boundary:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_interpolate <- census_data_clip %>% \n mutate(\n across(\n .cols = ends_with('_count'),\n .fns = ~ .x * areal_weight_factor #,\n # .names = \"{str_replace(.col, 'population_', 'percent_')}\"\n )) \n```\n:::\n\n\nAs noted above, it's also possible to use pre-built functions from several R packages to perform areal interpolation in a single step. Since we're using a three-step process, which also implements population weighted averaging for some variables, we're not using those functions directly in this example. However, they can be a useful check to validate our computed count data, but only after we aggregate our data at the system level -- see @sec-check-areal-interp for more details.\n\n### Interpolation Step 2: Compute Population Weighted Values (Intensive Variables) {#sec-pop-interp}\n\nCompute population weighted values\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncensus_data_interpolate <- census_data_interpolate %>% \n mutate(average_household_size_weighted = average_household_size * households_count,\n median_household_income_weighted = median_household_income * households_count,\n per_capita_income_weighted = per_capita_income * population_total_count)\n```\n:::\n\n\n::: callout-caution\nTo calculate an aggregated value for a variable like median household income, which depends on the distribution of the underling data, it may be worth considering whether a weighed average value is an appropriate measure. In some cases, it may be more appropriate to use the counts in each income bracket to estimate a median income, and/or present the income distribution rather than a single value.\n\nFor a discussion of the problem and a proposed solution, see [this document](https://www.documentcloud.org/documents/6165014-How-to-Recalculate-a-Median.html#document/p1).\n:::\n\n### Interpolation Step 3: Aggregate by Water System\n\nNext, we need to combine the weighted values calculated above to produce the estimates for each water system, and can also use those combined values to compute some additional metrics for each system (like rates, income distributions, etc.).\n\n#### Combine Results by Water System\n\nFirst, combine the results by summing all of the count-based variables (derived from areal interpolation), and calculating weighted averages for all variables computed in step 2 above. Note that we have to first calculate the denominator for each variable calculated with population weighted interpolation, because some of those variables contain missing values for records where the denominator is present (and if we don't remove the missing values, we get an `NA` for any water system that contains a block group with a missing value for that variable). For example, there are block groups where the median household income is missing, but the total household count is available for that block group – in that case, the weighted average should not include the households in that block group in the denominator; otherwise, the true value will be underestimated.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- census_data_interpolate %>% \n group_by(WATER_SY_1) %>% \n mutate(\n average_household_size_denominator = if_else(is.na(average_household_size), 0, households_count),\n median_household_income_denominator = if_else(is.na(median_household_income), 0, households_count),\n per_capita_income_denominator = if_else(is.na(per_capita_income), 0, population_total_count)\n ) %>% \n summarize(\n across(\n .cols = ends_with('_count'),\n .fns = ~ sum(.x)\n ),\n average_household_size_hh_weighted = \n sum(average_household_size_weighted, na.rm = TRUE) / \n sum(average_household_size_denominator),\n median_household_income_hh_weighted = \n sum(median_household_income_weighted, na.rm = TRUE) /\n sum(median_household_income_denominator),\n per_capita_income_pop_weighted = \n sum(per_capita_income_weighted, na.rm = TRUE) / \n sum(per_capita_income_denominator)\n ) %>% \n ungroup()\n```\n:::\n\n\n#### Check - Count Variables Estimated with Areal Interpolation {#sec-check-areal-interp}\n\nAs noted above, it's also possible to use pre-built functions for areal interpolation. This section demonstrates those functions and uses them as a check of our computed count data.\n\nFrom the `sf` package, we can use the `st_interpolate_aw` function:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# NOTE: it's only necessary to check the estimated values for one variable - \n# this just checks the total estimated population\n\n# sf package\ncheck_sf <- st_interpolate_aw(x = census_data_acs %>% \n select(population_total_count),\n to = water_systems_sac,\n extensive = TRUE) %>% \n bind_cols(water_systems_sac %>% st_drop_geometry)\n\n# check - should be TRUE if results are equivalent\nall(check_sf %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5) ==\n water_system_demographics %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5)\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n:::\n\n\nFrom the `areal` package, we can use the `aw_interpolate` function. Note that there are some settings that you may need to modify in the `aw_interpolate` function depending on the type of analysis you're doing. In particular, for more information about the `weight` argument -- which can be either `sum` or `total` -- see [this section of the documentation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html#calculating-weights-for-extensive-interpolations). For more information about extensive versus intensive interpolations, see [this section of the documenation](https://chris-prener.github.io/areal/articles/areal-weighted-interpolation.html#extensive-and-intensive-interpolations) (as noted above, the method applied here avoids using areal interpolation to calculate intensive variables, because area may not be a good metric for determining how to weight those variables, considering that we can estimate associated counts for populations/households/etc.).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# NOTE: it's only necessary to check the estimated values for one variable - \n# this just checks the total estimated population\n\n# areal package\ncheck_areal <- aw_interpolate(water_systems_sac,\n tid = WATER_SY_1,\n source = census_data_acs,\n sid = GEOID,\n weight = 'total',\n extensive = c('population_total_count'))\n\n# check - should be TRUE if results are equivalent\nall(check_areal %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5) ==\n water_system_demographics %>% \n arrange(WATER_SY_1) %>% \n pull(population_total_count) %>% \n round(5)\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n:::\n\n\n#### Clean & Format Results {#sec-results-clean}\n\nWe could stop here, and save the dataset containing the results to an output file (done below - see @sec-results-save). But, it may be useful to do some additional computations and re-formatting before saving the dataset. For example, in this case it may be useful to calculate the racial/ethnic breakdown of each system's population as percentages of the total population (in addition to the total counts computed above), and calculate other rates / distributions.\n\nFirst we can add fields with each racial/ethnic group's estimated percent of the total population within each water system's service area:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>%\n mutate(\n across(\n .cols = starts_with('population_'),\n .fns = ~ round(.x / population_total_count * 100, 2),\n .names = \"{str_replace(.col, '_count', '_percent')}\"\n ),\n .after = population_other_or_multiple_count) %>% \n select(-population_total_percent) # this always equals 1, not needed\n```\n:::\n\n\nWe can also calculate the estimated poverty rate for each water system's service area.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(poverty_rate_percent = case_when(\n poverty_total_assessed_count == 0 ~ 0,\n .default = 100 * poverty_below_count / poverty_total_assessed_count\n ), \n .after = poverty_above_count)\n```\n:::\n\n\nAnd compute income brackets in 25k increments:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(households_income_0_25k_count = \n households_income_below_10k_count + \n households_income_10k_15k_count + \n households_income_15k_20k_count +\n households_income_20k_25k_count,\n households_income_25k_50k_count =\n households_income_25k_30k_count + \n households_income_30k_35k_count +\n households_income_35k_40k_count +\n households_income_40k_45k_count +\n households_income_45k_50k_count,\n households_income_50k_75k_count =\n households_income_50k_60k_count +\n households_income_60k_75k_count,\n .after = households_income_above_200k_count\n ) # note - above 75k is already in 25k increments\n```\n:::\n\n\nAnd compute income brackets in 50k increments:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n mutate(households_income_0_50k_count = \n households_income_0_25k_count + \n households_income_25k_50k_count,\n households_income_50k_100k_count =\n households_income_50k_75k_count +\n households_income_75k_100k_count,\n households_income_100k_150k_count =\n households_income_100k_125k_count +\n households_income_125k_150k_count,\n .after = households_income_50k_75k_count\n ) # above 150k is already in 50k increments\n```\n:::\n\n\nAnd compute grouped median household income:\n\n\n::: {.cell}\n\n:::\n\n\nAnd compute \\# and % of households below income thresholds:\n\n\n::: {.cell}\n\n:::\n\n\nAnd, compute the portion of households paying more than 30% / 50% of their income toward housing costs:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>%\n mutate(households_housing_costs_over30pct_percent = \n 100 * (households_mortgage_over30pct_count + \n households_no_mortgage_over30pct_count +\n households_rent_over30pct_count) / \n households_count) %>% \n mutate(households_housing_costs_over50pct_percent = \n 100 * (households_mortgage_over50pct_count + \n households_no_mortgage_over50pct_count +\n households_rent_over50pct_count) / \n households_count)\n```\n:::\n\n\nFinally, we can round the estimated values to appropriate levels of precision:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>%\n mutate(\n across(\n .cols = ends_with('_count'),\n .fns = ~ round(.x, 0)\n )) %>%\n mutate(\n across(\n .cols = ends_with('_percent'),\n .fns = ~ round(.x, 2)\n ))\n```\n:::\n\n\nWe've now got a dataset with the selected census data estimated for each of the *target* geographic features (water system service areas). Here's a view of the contents and structure of the re-formatted dataset (only the first few fields are shown):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_system_demographics[,1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 21\n$ WATER_SY_1 \"B & W RESORT MARI…\n$ population_total_count 0, 22603, 33120, 1…\n$ population_hispanic_or_latino_count 0, 10939, 5245, 34…\n$ population_white_count 0, 3504, 19456, 23…\n$ population_black_or_african_american_count 0, 2663, 3199, 197…\n$ population_native_american_or_alaska_native_count 0, 121, 113, 70, 0…\n$ population_asian_count 0, 4075, 2947, 108…\n$ population_pacific_islander_count 0, 240, 77, 59, 0,…\n$ population_other_or_multiple_count 0, 1060, 2082, 110…\n$ population_hispanic_or_latino_percent 41.43, 48.40, 15.8…\n$ population_white_percent 52.47, 15.50, 58.7…\n$ population_black_or_african_american_percent 0.00, 11.78, 9.66,…\n$ population_native_american_or_alaska_native_percent 0.00, 0.54, 0.34, …\n$ population_asian_percent 4.55, 18.03, 8.90,…\n$ population_pacific_islander_percent 0.00, 1.06, 0.23, …\n$ population_other_or_multiple_percent 1.56, 4.69, 6.29, …\n$ poverty_total_assessed_count 0, 22556, 33034, 1…\n$ poverty_below_count 0, 6010, 3389, 313…\n$ poverty_above_count 0, 16546, 29645, 6…\n$ poverty_rate_percent 22.60, 26.64, 10.2…\n$ geometry POLYGON ((…\n```\n\n\n:::\n:::\n\n\n@tbl-water-sys-demographics-rev provides a complete view of the cleaned and re-formatted dataset. These results are saved locally in tabular and spatial format in @sec-results-save.\n\n\n::: {#tbl-water-sys-demographics-rev .cell .tbl-cap-location-top tbl-cap='Water System Demographics'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n kable(caption = 'A Caption') %>%\n scroll_box(height = \"400px\")\n```\n\n::: {.cell-output-display}\n`````{=html}\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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A Caption
WATER_SY_1 population_total_count population_hispanic_or_latino_count population_white_count population_black_or_african_american_count population_native_american_or_alaska_native_count population_asian_count population_pacific_islander_count population_other_or_multiple_count population_hispanic_or_latino_percent population_white_percent population_black_or_african_american_percent population_native_american_or_alaska_native_percent population_asian_percent population_pacific_islander_percent population_other_or_multiple_percent poverty_total_assessed_count poverty_below_count poverty_above_count poverty_rate_percent households_count households_income_below_10k_count households_income_10k_15k_count households_income_15k_20k_count households_income_20k_25k_count households_income_25k_30k_count households_income_30k_35k_count households_income_35k_40k_count households_income_40k_45k_count households_income_45k_50k_count households_income_50k_60k_count households_income_60k_75k_count households_income_75k_100k_count households_income_100k_125k_count households_income_125k_150k_count households_income_150k_200k_count households_income_above_200k_count households_income_0_25k_count households_income_25k_50k_count households_income_50k_75k_count households_income_0_50k_count households_income_50k_100k_count households_income_100k_150k_count households_mortgage_total_count households_mortgage_over30pct_count households_mortgage_over50pct_count households_no_mortgage_total_count households_no_mortgage_over30pct_count households_no_mortgage_over50pct_count households_rent_total_count households_rent_over30pct_count households_rent_over50pct_count average_household_size_hh_weighted median_household_income_hh_weighted per_capita_income_pop_weighted geometry households_housing_costs_over30pct_percent households_housing_costs_over50pct_percent
B & W RESORT MARINA 0 0 0 0 0 0 0 0 41.43 52.47 0.00 0.00 4.55 0.00 1.56 0 0 0 22.60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.030000 51977.00 40522.00 POLYGON ((-138282.2 13643.2... 40.53 21.84
CAL AM FRUITRIDGE VISTA 22603 10939 3504 2663 121 4075 240 1060 48.40 15.50 11.78 0.54 18.03 1.06 4.69 22556 6010 16546 26.64 6900 354 339 521 263 367 302 359 355 565 692 876 784 459 235 287 141 1477 1948 1569 3425 2352 694 1620 745 345 1236 95 58 4044 2131 1059 3.257806 53040.44 20519.57 POLYGON ((-127001.4 54266.8... 43.06 21.18
CALAM - ANTELOPE 33120 5245 19456 3199 113 2947 77 2082 15.84 58.74 9.66 0.34 8.90 0.23 6.29 33034 3389 29645 10.26 10529 315 184 101 122 116 469 248 368 449 737 1077 1669 1501 1077 1158 937 723 1650 1814 2373 3483 2578 5544 1861 621 1747 184 106 3238 1678 649 3.134530 93741.55 34660.44 POLYGON ((-120906.3 77326.5... 35.36 13.07
CALAM - ARDEN 10112 3433 2392 1977 70 1082 59 1100 33.95 23.65 19.55 0.69 10.70 0.58 10.87 10034 3130 6904 31.19 3823 201 259 239 167 319 190 142 236 207 440 394 535 228 148 62 58 866 1093 834 1959 1368 376 265 84 46 133 8 3 3426 2124 1170 2.623643 49624.62 22770.82 POLYGON ((-123052 64046.06,... 57.97 31.87
CALAM - ISLETON 34 14 17 0 0 2 0 1 42.06 51.14 0.00 0.00 4.55 0.00 2.25 34 7 27 20.89 16 1 1 0 1 1 0 1 1 0 2 1 1 3 1 0 1 4 3 3 6 4 4 6 4 1 7 2 2 4 1 1 2.078994 57361.76 40672.21 POLYGON ((-138730.9 17272.8... 39.45 20.68
CALAM - LINCOLN OAKS 42916 9056 26529 1486 143 2706 288 2708 21.10 61.82 3.46 0.33 6.31 0.67 6.31 42823 4074 38749 9.51 15621 740 375 308 622 488 616 585 629 645 1035 1641 2442 1889 1272 1555 778 2046 2964 2675 5010 5118 3161 7390 2671 919 3332 503 298 4900 2523 1302 2.730281 82035.52 33728.94 POLYGON ((-117495.2 73240.4... 36.46 16.13
CALAM - PARKWAY 58635 18665 8921 6965 21 19228 1386 3449 31.83 15.21 11.88 0.04 32.79 2.36 5.88 58434 9804 48630 16.78 17667 1081 753 514 713 694 640 713 700 727 1145 1918 2490 1634 1532 1546 865 3061 3475 3064 6536 5554 3166 7163 2719 1049 3418 647 383 7086 3517 1917 3.284608 72938.51 26938.14 POLYGON ((-124522.5 52428.5... 38.96 18.96
CALAM - SUBURBAN ROSEMONT 57897 13791 25062 7725 91 6905 380 3942 23.82 43.29 13.34 0.16 11.93 0.66 6.81 57661 8374 49287 14.52 21045 1156 612 472 744 653 568 582 874 628 1289 2508 3438 2595 1594 1671 1661 2985 3305 3797 6290 7235 4189 8262 2262 730 3425 439 271 9358 4521 2320 2.726937 81229.87 34497.37 POLYGON ((-119360.4 58937.6... 34.31 15.78
CALAM - WALNUT GROVE 12 5 5 0 0 1 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 12 2 10 15.75 5 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 1 2 2 2 0 2 0 0 1 0 0 2 1 0 2.490000 68248.00 38950.00 POLYGON ((-131705.3 26403.6... 24.49 14.65
CALIFORNIA STATE FAIR 532 78 262 91 0 48 0 52 14.68 49.25 17.13 0.00 9.10 0.00 9.85 526 152 374 28.89 285 65 13 8 5 9 14 2 0 23 29 30 35 21 11 17 3 91 48 59 140 93 32 0 0 0 0 0 0 285 177 95 1.820000 52886.00 33141.00 POLYGON ((-125611.2 65287.3... 62.11 33.45
CARMICHAEL WATER DISTRICT 39253 6192 25026 2230 68 3326 295 2116 15.78 63.76 5.68 0.17 8.47 0.75 5.39 38700 5000 33700 12.92 15937 570 534 513 472 398 607 522 684 541 996 1595 1782 1724 1200 1678 2122 2088 2751 2591 4839 4373 2924 5256 1399 669 3147 358 177 7534 4056 2068 2.405914 96967.64 46901.80 POLYGON ((-117711 65208.06,... 36.48 18.29
CITRUS HEIGHTS WATER DISTRICT 68912 12380 48148 2092 162 2875 71 3186 17.96 69.87 3.04 0.23 4.17 0.10 4.62 68581 6961 61620 10.15 25633 1012 569 446 769 665 867 841 723 1165 1875 3057 3954 2744 2332 2533 2080 2796 4261 4932 7057 8886 5075 10344 3553 1380 4293 554 286 10996 5759 2620 2.653808 82960.78 37323.17 POLYGON ((-114405.5 72735.6... 38.49 16.72
CITY OF SACRAMENTO MAIN 516189 151211 159508 62060 1249 98585 9242 34334 29.29 30.90 12.02 0.24 19.10 1.79 6.65 508800 77003 431797 15.13 194000 9540 9401 6217 6407 5804 6255 6278 6139 6729 13349 17396 26982 20453 15080 17439 20531 31564 31205 30745 62769 57728 35533 67435 21769 8217 29857 3476 1805 96708 47510 24524 2.609594 84694.02 39105.61 POLYGON ((-133314 51929.51,... 37.50 17.81
DEL PASO MANOR COUNTY WATER DI 5592 687 3967 390 15 119 31 382 12.28 70.95 6.97 0.26 2.13 0.56 6.84 5592 621 4971 11.10 2222 170 45 54 66 21 51 66 237 40 158 278 166 171 120 347 231 336 416 436 752 601 291 922 326 189 572 112 68 729 509 114 2.516895 90374.38 40254.83 POLYGON ((-120068.3 65980.9... 42.59 16.67
DELTA CROSSING MHP 0 0 0 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 0 0 0 17.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 POLYGON ((-132498.4 40410.2... 45.66 25.57
EAST WALNUT GROVE [SWS] 3 2 2 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 3 1 3 15.75 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 2.490000 68248.00 38950.00 POLYGON ((-132506.3 25966.4... 24.49 14.65
EDGEWATER MOBILE HOME PARK 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-153562.3 7972.28... 28.02 23.19
EL DORADO MOBILE HOME PARK 139 84 11 15 0 19 0 11 60.26 7.80 10.48 0.00 13.27 0.00 8.19 139 60 79 43.12 48 6 10 0 4 6 1 0 8 1 7 0 1 0 4 0 1 19 15 8 34 9 4 3 0 0 10 5 5 35 17 10 2.710000 29468.00 17394.00 POLYGON ((-124341.2 53660.1... 46.70 31.09
EL DORADO WEST MHP 148 89 12 16 0 20 0 12 60.26 7.80 10.48 0.00 13.27 0.00 8.19 147 63 84 43.12 51 6 10 0 4 6 1 0 8 2 8 0 1 0 5 0 1 20 16 8 37 9 5 3 0 0 10 6 6 38 18 10 2.710000 29468.00 17394.00 POLYGON ((-124532.3 53662.9... 46.70 31.09
ELEVEN OAKS MOBILE HOME COMMUNITY 233 45 94 56 0 37 0 1 19.27 40.19 24.01 0.00 15.91 0.00 0.62 233 87 146 37.48 71 7 2 3 6 10 2 1 1 3 1 13 17 3 0 3 0 17 17 15 34 32 3 8 3 1 21 1 1 42 29 23 3.280000 60521.00 18213.00 POLYGON ((-119819.8 71950.9... 46.85 35.36
ELK GROVE WATER SERVICE 42647 7656 19550 3209 70 8939 388 2835 17.95 45.84 7.53 0.16 20.96 0.91 6.65 42258 3264 38994 7.72 13239 430 202 253 224 328 102 345 292 245 667 1117 1441 1470 1386 1907 2832 1108 1311 1784 2420 3225 2856 7552 1903 628 2861 283 113 2826 1595 864 3.179068 122771.00 43429.03 POLYGON ((-118730.1 42496.7... 28.55 12.12
FAIR OAKS WATER DISTRICT 36003 4655 27050 708 94 1372 12 2113 12.93 75.13 1.97 0.26 3.81 0.03 5.87 35775 2852 32923 7.97 14233 546 332 113 229 208 391 206 469 293 804 1064 2214 1447 1568 1875 2474 1220 1568 1868 2788 4082 3016 7090 1872 845 3092 261 108 4051 1844 768 2.480217 107985.74 54435.01 POLYGON ((-112317.5 69577.6... 27.94 12.09
FLORIN COUNTY WATER DISTRICT 9951 2963 1548 1394 7 2743 866 430 29.78 15.56 14.01 0.07 27.56 8.70 4.32 9835 1285 8550 13.06 2755 84 125 53 154 103 46 86 176 224 258 223 432 297 215 143 137 417 635 481 1051 913 512 981 426 90 675 49 28 1100 476 260 3.573005 67048.12 24517.64 POLYGON ((-122791.9 52602.2... 34.48 13.70
FOLSOM STATE PRISON 3536 1257 652 1390 57 70 34 77 35.55 18.43 39.31 1.60 1.97 0.96 2.17 29 1 28 2.20 23 0 0 0 0 0 0 0 0 0 0 0 0 4 4 12 1 0 0 0 0 1 8 3 1 0 0 0 0 19 0 0 2.726311 161047.22 2271.22 POLYGON ((-99838.11 75350.0... 4.67 0.53
FOLSOM, CITY OF - ASHLAND 3845 318 2934 43 1 125 1 423 8.26 76.32 1.12 0.03 3.26 0.02 10.99 3780 143 3637 3.79 1800 44 17 104 43 34 209 103 74 43 43 158 248 132 80 123 345 208 463 201 670 449 212 594 164 90 847 368 82 358 196 74 2.087286 76810.17 56773.97 POLYGON ((-102605.9 74922.1... 40.42 13.70
FOLSOM, CITY OF - MAIN 62462 8433 35222 1693 105 12934 177 3897 13.50 56.39 2.71 0.17 20.71 0.28 6.24 62115 3405 58710 5.48 22409 807 218 390 477 418 283 329 373 451 670 1181 2255 2382 1747 4083 6344 1892 1855 1851 3747 4106 4129 11491 2728 1179 3590 237 146 7328 3010 1321 2.769356 141856.37 58469.35 POLYGON ((-101870.6 66094.5... 26.66 11.81
FREEPORT MARINA 3 2 1 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 3 1 3 17.42 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 POLYGON ((-130970.7 50553.3... 45.66 25.57
GALT, CITY OF 21490 9314 9952 520 22 872 20 789 43.34 46.31 2.42 0.10 4.06 0.09 3.67 21341 1404 19937 6.58 6988 139 168 243 210 141 342 161 347 152 550 687 807 1096 504 789 650 761 1143 1237 1904 2044 1601 3724 907 523 1454 109 44 1809 906 414 3.048249 90632.93 33685.54 MULTIPOLYGON (((-113921.6 2... 27.52 14.05
GOLDEN STATE WATER CO - ARDEN WATER SERV 6556 1706 2887 322 0 888 11 742 26.02 44.04 4.91 0.00 13.54 0.16 11.32 6453 1626 4828 25.19 2173 19 82 19 141 53 173 34 179 37 139 351 319 132 172 141 183 262 476 490 738 809 303 728 239 123 131 0 0 1315 599 335 2.897716 66579.36 30417.36 POLYGON ((-121143.9 63698.4... 38.56 21.09
GOLDEN STATE WATER CO. - CORDOVA 48115 9009 26042 3982 229 6050 188 2615 18.72 54.13 8.28 0.48 12.57 0.39 5.43 47835 4408 43427 9.21 18022 509 482 310 496 480 437 389 469 598 1276 1692 2653 2565 1671 1948 2047 1796 2374 2968 4170 5621 4236 7380 2174 836 3506 364 201 7137 2744 1410 2.650717 96697.06 42695.41 POLYGON ((-112985.4 62375.3... 29.31 13.58
HAPPY HARBOR (SWS) 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-139842 11256.85,... 28.02 23.19
HOLIDAY MOBILE VILLAGE 46 18 7 3 0 15 0 3 38.66 15.12 7.10 0.00 32.49 0.00 6.64 46 10 36 22.33 16 2 1 0 1 0 1 5 1 0 0 2 2 1 0 0 0 4 7 2 11 4 1 2 0 0 2 1 1 12 6 4 2.860000 38491.00 16707.00 POLYGON ((-123874.7 52485.3... 44.88 28.55
HOOD WATER MAINTENCE DIST [SWS] 1 1 0 0 0 0 0 0 69.19 28.71 0.00 0.00 0.00 0.00 2.10 1 0 1 17.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.550000 56250.00 23510.00 MULTIPOLYGON (((-132506 403... 45.66 25.57
IMPERIAL MANOR MOBILEHOME COMMUNITY 209 52 129 1 0 6 0 21 24.93 61.63 0.45 0.00 2.93 0.00 10.05 209 45 164 21.48 124 4 26 18 3 0 16 7 5 6 1 4 29 0 0 0 6 51 34 5 84 34 0 9 0 0 89 37 34 27 27 22 1.680363 31831.84 32878.17 POLYGON ((-115390.2 74250.3... 50.97 45.07
KORTHS PIRATES LAIR 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-137314.9 10213.1... 28.02 23.19
LAGUNA DEL SOL INC 24 5 18 0 0 0 0 0 21.55 75.20 0.00 0.67 1.46 0.00 1.12 24 2 22 6.40 9 0 1 1 0 0 0 0 0 0 0 0 2 0 0 1 2 2 1 0 3 2 1 5 2 2 3 0 0 2 0 0 2.640000 95227.00 50793.00 POLYGON ((-104662.2 49197.3... 23.37 23.37
LAGUNA VILLAGE RV PARK 20 3 2 1 0 11 2 2 12.79 8.48 7.28 0.00 52.62 8.38 10.45 20 2 18 11.79 7 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 2 2 1 3 1 0 1 0 0 3 1 0 3.030000 84332.00 32668.00 POLYGON ((-122461.8 48066.6... 32.52 12.26
LINCOLN CHAN-HOME RANCH 4 2 2 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 4 1 3 15.75 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 2.490000 68248.00 38950.00 POLYGON ((-136788.6 36526.1... 24.49 14.65
LOCKE WATER WORKS CO [SWS] 1 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 1 0 1 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-131952.8 27176.6... 24.49 14.65
MAGNOLIA MUTUAL WATER 1 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 1 0 1 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-137022.9 36118.9... 24.49 14.65
MC CLELLAN MHP 269 52 108 65 0 43 0 2 19.27 40.19 24.01 0.00 15.91 0.00 0.62 269 101 168 37.48 82 8 2 3 7 11 2 2 1 3 1 15 20 3 0 3 0 20 19 17 39 36 3 9 4 2 25 1 1 48 34 27 3.280000 60521.00 18213.00 POLYGON ((-119814.9 72169.0... 46.85 35.36
OLYMPIA MOBILODGE 290 70 81 18 0 101 16 3 24.12 28.03 6.30 0.00 34.95 5.53 1.08 290 68 222 23.43 114 11 0 6 10 9 3 13 0 0 10 19 8 3 12 5 5 28 25 29 53 36 14 31 22 10 51 12 10 33 9 7 2.510000 53786.00 29451.00 POLYGON ((-123342.4 53061.6... 37.35 23.74
ORANGE VALE WATER COMPANY 17387 2658 12308 241 181 633 86 1281 15.28 70.79 1.39 1.04 3.64 0.49 7.37 17288 1904 15384 11.01 6595 389 111 61 94 226 58 274 120 181 372 752 990 901 626 678 766 655 858 1123 1512 2113 1526 3246 1021 453 1686 315 185 1663 693 305 2.608348 92693.71 42509.89 POLYGON ((-108131.3 74330.4... 30.77 14.29
PLANTATION MOBILE HOME PARK 10 4 1 1 0 3 0 1 38.66 15.12 7.10 0.00 32.49 0.00 6.64 10 2 7 22.33 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 2 1 0 1 0 0 0 0 0 2 1 1 2.860000 38491.00 16707.00 POLYGON ((-124180.4 53321.5... 44.88 28.55
RANCHO MARINA 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-138041.4 11320.9... 28.02 23.19
RANCHO MURIETA COMMUNITY SERVI 3239 661 2157 120 7 188 0 106 20.42 66.59 3.71 0.21 5.80 0.00 3.26 3239 199 3040 6.13 1402 59 42 0 6 5 18 74 27 75 44 81 88 118 204 241 319 108 199 125 307 213 323 1029 205 103 270 63 57 103 41 40 2.307704 144993.81 66451.34 POLYGON ((-92457.85 52674.7... 22.02 14.30
RIO COSUMNES CORRECTIONAL CENTER [SWS] 22 6 8 4 1 1 0 2 25.74 37.49 16.82 2.97 4.50 1.81 10.66 4 0 4 0.00 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 3.450000 115897.00 11095.00 POLYGON ((-124032.5 32206.2... 23.75 0.00
RIO LINDA/ELVERTA COMMUNITY WATER DIST 11831 2585 7595 337 17 765 21 512 21.85 64.19 2.85 0.14 6.46 0.18 4.33 11829 1619 10210 13.69 3762 177 156 67 169 56 113 116 114 118 173 297 607 492 431 416 259 569 518 470 1087 1077 922 1918 573 157 773 114 47 1070 519 340 3.123012 83603.04 33734.49 POLYGON ((-126609.8 73568.2... 32.07 14.49
RIVER'S EDGE MARINA & RESORT 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-141102.2 11867.3... 28.02 23.19
SAC CITY MOBILE HOME COMMUNITY LP 229 82 17 7 0 123 0 0 35.66 7.50 3.27 0.00 53.57 0.00 0.00 229 110 119 48.14 89 11 16 9 10 8 0 0 4 2 7 1 13 4 4 0 0 46 14 8 60 21 8 4 2 2 15 2 0 71 41 30 2.530000 22380.00 16689.00 POLYGON ((-124544.3 56147.0... 48.95 35.43
SACRAMENTO SUBURBAN WATER DISTRICT 193126 43047 97872 17684 834 20602 624 12464 22.29 50.68 9.16 0.43 10.67 0.32 6.45 190984 33399 157585 17.49 72505 3817 3001 3069 2884 3205 3100 3337 2893 2342 5541 6792 10037 6480 4342 5488 6177 12771 14878 12333 27649 22370 10822 23467 7204 2837 12037 2087 1160 37001 21072 10274 2.635471 73746.51 35321.18 MULTIPOLYGON (((-122206.9 6... 41.88 19.68
SAN JUAN WATER DISTRICT 30122 3409 21349 831 287 2762 17 1467 11.32 70.87 2.76 0.95 9.17 0.06 4.87 30014 1718 28297 5.72 10750 389 168 100 275 128 160 111 133 127 472 684 984 854 876 1032 4256 932 658 1156 1591 2141 1730 6210 1754 724 2883 528 357 1658 726 339 2.783858 160696.10 72978.42 POLYGON ((-104526.8 73044.7... 27.98 13.21
SCWA - ARDEN PARK VISTA 8086 990 6016 270 12 396 8 395 12.24 74.40 3.33 0.15 4.90 0.10 4.88 8038 523 7515 6.51 3303 79 36 48 77 65 38 18 49 162 139 187 253 465 208 416 1065 241 330 326 571 579 673 1823 520 112 673 76 23 807 384 225 2.424845 139081.65 84548.46 POLYGON ((-120985.4 62883.8... 29.69 10.90
SCWA - LAGUNA/VINEYARD 145495 27502 38496 16568 246 50411 2220 10052 18.90 26.46 11.39 0.17 34.65 1.53 6.91 145198 14710 130489 10.13 45137 1692 666 742 878 839 1336 850 788 752 2363 3198 6037 5323 5057 6578 8038 3978 4565 5561 8543 11598 10380 24581 7232 2916 7878 861 471 12677 6368 3337 3.207447 114494.03 41415.71 MULTIPOLYGON (((-126550 404... 32.04 14.90
SCWA MATHER-SUNRISE 18249 2708 8114 1553 23 4507 164 1180 14.84 44.47 8.51 0.12 24.70 0.90 6.47 18211 1005 17206 5.52 5503 228 35 97 57 68 39 12 20 36 189 320 533 645 755 1003 1469 416 174 509 590 1042 1399 3756 881 266 855 60 43 893 318 167 3.296327 147818.01 47448.37 MULTIPOLYGON (((-112526.7 5... 22.89 8.66
SEQUOIA WATER ASSOC 0 0 0 0 0 0 0 0 44.60 45.84 0.00 0.00 5.93 0.00 3.63 0 0 0 15.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.490000 68248.00 38950.00 POLYGON ((-136929.5 36128.1... 24.49 14.65
SOUTHWEST TRACT W M D [SWS] 174 29 42 24 3 75 1 0 16.58 24.48 13.69 1.55 43.11 0.60 0.00 174 38 136 21.83 57 1 2 7 0 7 0 0 10 12 3 2 5 0 1 2 4 10 29 6 39 10 1 3 1 0 8 0 0 45 29 7 3.040000 45671.00 36348.00 MULTIPOLYGON (((-125843.6 5... 52.53 12.40
SPINDRIFT MARINA 0 0 0 0 0 0 0 0 3.90 89.23 3.23 0.00 0.00 0.00 3.63 0 0 0 35.94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.790000 38125.00 33103.00 POLYGON ((-139920.3 11468.3... 28.02 23.19
TOKAY PARK WATER CO 652 214 134 37 0 239 0 28 32.80 20.55 5.61 0.00 36.69 0.00 4.35 652 113 539 17.29 173 2 2 3 21 0 0 13 13 10 18 27 36 14 4 10 0 27 36 45 64 81 18 81 38 11 44 0 0 48 32 12 3.757973 62802.24 19400.05 POLYGON ((-122824.8 54197.9... 40.57 13.58
TUNNEL TRAILER PARK 0 0 0 0 0 0 0 0 49.74 34.94 0.00 0.00 4.65 0.00 10.67 0 0 0 0.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.950000 153092.00 42507.00 POLYGON ((-136160.9 24171.2... 20.30 0.00
VIEIRA'S RESORT, INC 4 2 2 0 0 0 0 0 41.43 52.47 0.00 0.00 4.55 0.00 1.56 4 1 3 22.60 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 2.030000 51977.00 40522.00 POLYGON ((-143780.4 18567.4... 40.53 21.84
WESTERNER MOBILE HOME PARK 32 6 6 9 0 10 0 1 17.59 17.62 28.31 0.55 31.36 0.00 4.57 31 7 24 23.76 10 1 0 0 0 1 0 0 1 0 2 1 1 2 0 1 0 1 2 3 3 4 2 4 2 1 1 0 0 5 3 2 3.160000 59296.00 23437.00 POLYGON ((-122657.2 48977.8... 56.87 29.49
\n\n`````\n:::\n:::\n\n\n
\n\n#### Transform Results to Long Format {#sec-results-transform-long}\n\nFor further analysis and exploration / visualization of the results, it will help to convert the results from wide to long format, and edit the group names so that they can be used as titles.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# pivot from wide to long format\nwater_system_demographics_long <- water_system_demographics %>% \n # select(WATER_SY_1, starts_with('percent_')) %>% # select only the fields with percentages, and the water system name/id\n # convert to long format\n # st_drop_geometry() %>% \n pivot_longer(cols = !c(WATER_SY_1, geometry), \n names_to = 'variable', \n values_to = 'value')\n\n# clean variable names and add grouping fields (type, group_type)\nwater_system_demographics_long <- water_system_demographics_long %>% \n mutate(variable = variable %>% \n # str_remove_all(pattern = 'percent_') %>% \n str_replace_all(pattern = '_', replacement = ' ') %>% \n str_replace_all(pattern = ' or ', replacement = ' / ') %>% \n str_to_title(.) %>% \n str_remove_all(pattern = ' / Alaska Native')) %>% \n mutate(type = case_when(\n str_detect(variable, pattern = 'Count') ~ 'Count',\n str_detect(variable, pattern = 'Percent') ~ 'Percent',\n str_detect(variable, pattern = 'Pop Weighted') ~ 'Pop Weighted',\n str_detect(variable, pattern = 'Hh Weighted') ~ 'Hh Weighted',\n .default = NA), \n .after = variable) %>% \n mutate(group_type = case_when(\n str_detect(variable, pattern ='Population') ~ 'Population',\n str_detect(variable, pattern = 'Households') ~ 'Households',\n str_detect(variable, pattern = 'Average Household Size Hh Weighted') ~ 'Household Weighted', \n str_detect(variable, pattern = 'Median Household Income Hh Weighted') ~ 'Household Weighted',\n str_detect(variable, pattern = 'Per Capita Income Pop Weighted') ~ 'Population Weighted',\n str_detect(variable, pattern = 'Poverty') ~ 'Population'),\n .after = type) %>% \n mutate(variable = case_when(\n str_detect(variable, pattern = 'Households Count') ~ 'Households Total',\n .default = str_remove_all(variable, pattern = 'Households'))) %>% \n mutate(variable = case_when(\n str_detect(variable, 'Population Total Count') ~ 'Population Total',\n .default = str_remove_all(variable, 'Population'))) %>%\n mutate(variable = str_remove_all(variable, \n pattern = 'Count')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = 'Percent')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = ' Hh Weighted')) %>% \n mutate(variable = str_remove_all(variable, \n pattern = ' Pop Weighted')) %>% \n mutate(variable = str_replace_all(variable, \n pattern = 'Over30pct', \n replacement = 'Over 30% Income')) %>% \n mutate(variable = str_replace_all(variable, \n pattern = 'Over50pct', \n replacement = 'Over 50% Income')) %>% \n mutate(variable = str_trim(variable)) %>%\n mutate(variable = str_replace_all(variable,\n pattern = 'k ',\n replacement = 'k-')) %>%\n mutate(variable = str_replace_all(variable,\n pattern = '0 ',\n replacement = '0-'))\n```\n:::\n\n\nHere's a view of the structure of the reformatted data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(water_system_demographics_long)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 3,472\nColumns: 6\n$ WATER_SY_1 \"B & W RESORT MARINA\", \"B & W RESORT MARINA\", \"B & W RESORT…\n$ geometry POLYGON ((-138282.2 13643.2..., POLYGON ((-138282.2…\n$ variable \"Population Total\", \"Hispanic / Latino\", \"White\", \"Black-/ …\n$ type \"Count\", \"Count\", \"Count\", \"Count\", \"Count\", \"Count\", \"Coun…\n$ group_type \"Population\", \"Population\", \"Population\", \"Population\", \"Po…\n$ value 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 41.43, 52.4…\n```\n\n\n:::\n:::\n\n\n### Save Results {#sec-results-save}\n\nOnce we've finished the computations and verified the outputs look reasonable, we can save the results to output files so they can be re-used and shared. The results can be saved in tabular (e.g., csv, excel) and/or spatial (e.g., shapefile, geopackage) formats, which may be helpful for different use cases. Note that you may need to think about exactly what variables to include in the output file(s) and how to format the output datasets (e.g., wide versus long format).\n\nThe chunk of code below (which is hidden by default), just tests to see whether any of the datasets to be saved have been changed since the previous version was saved. In general this is probably not needed for a typical workflow and can be ignored for most use cases -- it is just used here to make rendering of this document a little more efficient.\n\n\n::: {.cell}\n\n```{.r .cell-code code-fold=\"true\"}\n# compute hash for datasets to be saved (i.e., a unique identifier for each dataset), and compare against previous versions\n\n## define file that stores hash (unique identifier for dataset)\nhash_file <- here('03_data_results',\n 'dataset_hash.csv')\n\n## compute hashes (unique identifier for datasets)\nhash_current <- digest(object = water_system_demographics,\n algo = 'md5')\nhash_current_long <- digest(object = water_system_demographics_long,\n algo = 'md5')\nhash_table_current <- tibble(dataset = c('water_system_demographics', 'water_system_demographics_long'),\n hash = c(hash_current, hash_current_long))\n\n## get the previous hashes from file (if it exists), else create a new file to store the hashes\nif (file.exists(hash_file)) {\n hash_table_previous <- read_csv(file = hash_file)\n} else {\n file.create(hash_file)\n hash_table_previous <- tibble(dataset = c('water_system_demographics', 'water_system_demographics_long'),\n hash = c('missing', 'missing'))\n}\n\n## if new hash is different from previous hash, set flag to update the output file (i.e., write a new version of the file)\nfile_update <- !identical(hash_table_current %>% \n filter(dataset == 'water_system_demographics') %>% \n pull(hash),\n hash_table_previous %>% \n filter(dataset == 'water_system_demographics') %>% \n pull(hash))\nfile_update_long <- !identical(hash_table_current %>% \n filter(dataset == 'water_system_demographics_long') %>% \n pull(hash),\n hash_table_previous %>% \n filter(dataset == 'water_system_demographics_long') %>% \n pull(hash))\n\n## write current hashes to file (for comparison with future versions)\nwrite_csv(x = hash_table_current,\n file = hash_file,\n append = FALSE)\n```\n:::\n\n\n#### Tabular Dataset {#sec-results-save-tabular}\n\nThe code below saves the tabular results to a csv file -- note that this dataset is in the 'wide' format we originally produced the results in:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update == TRUE) {\n write_csv(water_system_demographics %>%\n st_drop_geometry(), # drop the spatial data since this is a tabular format\n file = here('03_data_results',\n 'water_system_demographics_sac.csv'))\n}\n```\n:::\n\n\nWe can also save the data in the long/tidy format we developed above:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update_long == TRUE) {\n write_csv(water_system_demographics_long %>%\n st_drop_geometry(), # drop the spatial data since this is a tabular format\n file = here('03_data_results',\n 'water_system_demographics_sac_long.csv'))\n}\n```\n:::\n\n\n#### Spatial Dataset {#sec-results-save-spatial}\n\nTo save the output in a geospatial format, it may be best to save the data in a wide format, so that all of the attribute data for each *target* area (water system) is in a single row along with its spatial data (i.e. the system boundary information) (saving in long format may create a very large file). The code below saves the results -- in wide format -- to a geopackage file, which is a spatial file format that is similar to a shapefile.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nif (file_update == TRUE) {\n st_write(water_system_demographics,\n here('03_data_results',\n 'water_system_demographics_sac.gpkg'),\n append = FALSE)\n}\n```\n:::\n\n\n## Explore and Visualize Results {#sec-results-explore}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nFor simplicity, this section will focus on presenting estimated demographics for some of the largest water suppliers in the Sacramento county region (results for small water systems may not be very accurate and should be used with some caution - see @sec-check-pop-estimated-reported and @sec-small-area-estimates for more investigation of the results for small systems).\n\n\\[TO DO: add visualizations\\]\n\nPrepare interpolation data\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# census_data_interpolate census_data_clip\ncensus_data_interpolate <- census_data_interpolate %>% \n mutate(households_housing_costs_over30pct_percent = \n 100 * (households_mortgage_over30pct_count + \n households_no_mortgage_over30pct_count +\n households_rent_over30pct_count) / \n households_count) %>% \n mutate(households_housing_costs_over30pct_percent = \n 100 * (households_mortgage_over50pct_count + \n households_no_mortgage_over50pct_count +\n households_rent_over50pct_count) / \n households_count)\n```\n:::\n\n\nSelect systems to plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn_systems <- 20\nsystems_top_n <- water_system_demographics %>% \n slice_max(population_total_count, n = n_systems) %>% \n pull(WATER_SY_1)\n```\n:::\n\n\n### Bivariate Map\n\nThis section uses the [@biscale] R package to create bivariate choropleth maps that show how two variables vary together spatially.\n\n@fig-bivariate-all shows the relationship between relative housing costs and income – using the estimated data – for the top 20 systems by estimated population in Sacramento.\n\n\n::: {.cell}\n\n```{.r .cell-code code-fold=\"true\"}\n# Table B25140 - Housing Costs as a Percentage of Household Income in the past 12 months.\n# Shows the count of households paying more than 30% or 50% of their income towards housing costs broken out by three tenure categories (owned with a mortgage, owned without a mortgage, and rented).\n\n# set defaults\nbiscale_pal <- 'BlueOr' # 'GrPink' # 'DkViolet2'\nbiscale_dim <- 3\n\n# create classes\nbiscale_data <- bi_class(water_system_demographics %>% \n filter(WATER_SY_1 %in% systems_top_n) %>% \n filter(!is.na(median_household_income_hh_weighted)), \n x = households_housing_costs_over30pct_percent, \n y = median_household_income_hh_weighted, \n style = \"quantile\", \n dim = biscale_dim)\n\n# create map\nbiscale_map <- ggplot() +\n geom_sf(data = biscale_data, \n mapping = aes(fill = bi_class), \n color = \"white\", \n size = 0.1, \n show.legend = FALSE) +\n bi_scale_fill(pal = biscale_pal, \n dim = biscale_dim) + \n labs(\n title = \"Estimated % of Households Paying More Than 30% of Income Towards Housing Costs \\nand Estimated Median Household Income in Sacramento Water Systems\",\n subtitle = glue(\"Top {n_systems} systems by population\"),\n caption = glue(\"Data estimated from {acs_year} 5-year ACS Block Groups\")\n # title = \"Estimated Housing Cost as % of Household Income and \\nEstimated Median Household Income in Sacramento Water Systems\", \n # caption = \"% Housing cost shows the percent of households paying more than 30% of their income towards housing costs \\nIncome shows median household income (yellow = missing)\"\n ) +\n # labs(\n # title = \"Housing Cost1 and Income2 in Sacramento Water Systems\",\n # caption = \"1% of households paying more than 30% of their income towards housing costs
2Median household income (yellow = missing)\",\n # subtitle = glue(\"Top {n_systems} systems by population\")\n # ) +\n # add missing polygons back in\n geom_sf(data = water_system_demographics %>% \n filter(WATER_SY_1 %in% systems_top_n) %>% \n filter(is.na(median_household_income_hh_weighted)),\n color = \"white\",\n fill = 'gold'\n ) +\n geom_sf(data = counties_ca %>% filter(NAME == 'Sacramento'), \n color = 'grey',\n fill = NA) +\n bi_theme() + \n theme(plot.title = element_text(size=12), # element_markdown(size=12)\n plot.subtitle = element_text(size=10),\n plot.caption = element_text(size=8, hjust = 1)) # element_markdown(size=8, hjust = 1))\n\n# create legend\nbiscale_legend <- bi_legend(pal = biscale_pal,\n dim = biscale_dim,\n xlab = \"% Housing Costs \",\n ylab = \"Income \",\n size = 8)\n\n# construct map\nbiscale_plot <- ggdraw() +\n draw_plot(biscale_map, 0, 0, 1, 1) +\n draw_plot(biscale_legend, 0.1, .65, 0.2, 0.2)\n\nbiscale_plot\n```\n\n::: {.cell-output-display}\n![](example_census_race_ethnicity_calculation_files/figure-html/fig-bivariate-all-1.png){#fig-bivariate-all width=768}\n:::\n:::\n\n\n@fig-bivariate-system shows the same variables (relative housing costs and income) for the portions block groups overlapping Sacramento Suburban Water District – this illustrates the data underlying the interpolation process.\n\n\n::: {.cell}\n\n```{.r .cell-code code-fold=\"true\"}\n# set defaults\nbiscale_pal_system <- 'BlueOr' # 'GrPink' # 'DkViolet2'\nbiscale_dim_system <- 3\n\n# create classes\nbiscale_data_system <- bi_class(census_data_interpolate %>% \n filter(WATER_SY_1 == system_plot) %>% \n filter(!is.na(median_household_income)), \n x = households_housing_costs_over30pct_percent, \n y = median_household_income, \n style = \"quantile\", \n dim = biscale_dim_system)\n# create map\nbiscale_map_system <- ggplot() +\n geom_sf(data = biscale_data_system , \n mapping = aes(fill = bi_class), \n color = \"white\", \n size = 0.1, \n show.legend = FALSE) +\n bi_scale_fill(pal = biscale_pal_system, \n dim = biscale_dim_system) + \n labs(\n title = glue(\"Estimated % of Households Paying More Than 30% of Income Towards Housing Costs \\nand Estimated Median Household Income in {str_to_title(system_plot)}\"),\n # subtitle = glue(\"\"),\n caption = glue(\"Data from {acs_year} 5-year ACS Block Groups (Yellow = Missing Data)\")#,\n # title = glue(\"Housing Cost and Income \\nin {str_to_title(system_plot)}\"), \n # caption = \"% Housing cost shows the percent of households paying more than 30% of their income towards housing costs \\nIncome shows median household income (yellow = missing)\"#,\n ) +\n # add the missing polygons back in\n geom_sf(data = census_data_interpolate %>% \n filter(WATER_SY_1 == system_plot) %>% \n filter(is.na(median_household_income)),\n color = \"white\",\n fill = 'gold'\n ) +\n bi_theme() + \n theme(plot.title = element_text(size=12), # element_markdown(size=12)\n plot.subtitle = element_text(size=10),\n plot.caption = element_text(size=8, hjust = 1)) # element_markdown(size=8, hjust = 1))\n\n# create legend\nbiscale_legend <- bi_legend(pal = biscale_pal_system,\n dim = biscale_dim_system,\n xlab = \"% Housing Costs \",\n ylab = \"Income \",\n size = 8)\n\n# construct map\nbiscale_plot_system <- ggdraw() +\n draw_plot(biscale_map_system, 0, 0, 1, 1) +\n draw_plot(biscale_legend, 0.1, .55, 0.2, 0.2)\n\nbiscale_plot_system\n```\n\n::: {.cell-output-display}\n![](example_census_race_ethnicity_calculation_files/figure-html/fig-bivariate-system-1.png){#fig-bivariate-system width=768}\n:::\n:::\n\n\n## Check - Estimated vs Reported Population Estimates {#sec-check-pop-estimated-reported}\n\n\\[TO DO: Create map\\]\n\nBased on the map above, it's apparent that it will be difficult to obtain reasonable estimates for some suppliers, such as the suppliers with very small service areas in the southern portion of the county where the block groups are very large (and the supplier's service are is only a small fraction of the total area of the block group). These issues are explored further in @sec-small-area-estimates.\n\nNote that there are a number of reasons why the estimated population values are likely to differ from the population numbers in the water system dataset (e.g., the depicted boundaries may not be correct or exact, the supplier may have used different methods to count/estimate the population they serve, the time frames for the estimates may be different, etc.). But, there may also be some cases where the numbers differ significantly -- depending on the actual analysis being performed, this may mean that further work is needed for certain areas, or could mean that this method may not be sufficient and different methods are needed.\n\nAs a check, we can add a column to the interpolated dataset (which we'll call `population_percent_difference`) to compute the difference between the estimated total population (in the `population_total` field) and the total population listed in the `POPULATION` field (the reported value from the water system dataset).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwater_system_demographics <- water_system_demographics %>% \n left_join(water_systems_sac %>% \n st_drop_geometry() %>% \n select(WATER_SY_1, POPULATION),\n by = 'WATER_SY_1')\n\nwater_system_demographics <- water_system_demographics %>%\n mutate(population_percent_difference =\n round(100 * (population_total_count - POPULATION) / POPULATION, \n 2), \n .after = POPULATION)\n```\n:::\n\n\nFor water systems with a small population and/or service area, the estimated demographics may not match the reported population numbers in the water system dataset very well. You can see this in @tbl-pop-est-small by comparing the `population_reported` field, which contains the total population values from the water supplier dataset, with the `population_estimated` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. This probably indicates that, for small areas, some adjustments and/or further analysis may be needed, and the preliminary estimated values should be treated with some caution/skepticism.\n\nNote: See @sec-small-area-estimates below for some more investigation into water systems whose estimated population is at or near zero.\n\n\n::: {#tbl-pop-est-small .cell tbl-cap='10 Smallest Water Systems by Population'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n arrange(POPULATION) %>%\n slice(1:10) %>%\n select(WATER_SY_1, population_reported = POPULATION, \n population_estimated = population_total_count, \n population_percent_difference) %>%\n st_drop_geometry() %>%\n kable()\n```\n\n::: {.cell-output-display}\n\n\n|WATER_SY_1 | population_reported| population_estimated| population_percent_difference|\n|:---------------------------|-------------------:|--------------------:|-----------------------------:|\n|DELTA CROSSING MHP | 30| 0| -100.00|\n|LAGUNA VILLAGE RV PARK | 32| 20| -37.50|\n|LINCOLN CHAN-HOME RANCH | 33| 4| -87.88|\n|EDGEWATER MOBILE HOME PARK | 40| 0| -100.00|\n|MAGNOLIA MUTUAL WATER | 40| 1| -97.50|\n|FREEPORT MARINA | 42| 3| -92.86|\n|PLANTATION MOBILE HOME PARK | 44| 10| -77.27|\n|TUNNEL TRAILER PARK | 44| 0| -100.00|\n|SEQUOIA WATER ASSOC | 54| 0| -100.00|\n|HAPPY HARBOR (SWS) | 60| 0| -100.00|\n\n\n:::\n:::\n\n\nBut for larger water systems, the estimated population values seem to be more in line with the population numbers in the original dataset. You can see this in @tbl-pop-est-large by, as above, comparing the `population_reported` field, which contains the total population values from the water supplier dataset, with the `population_estimated` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field.\n\n\n::: {#tbl-pop-est-large .cell tbl-cap='10 Largest Water Systems by Population'}\n\n```{.r .cell-code}\nwater_system_demographics %>%\n arrange(desc(POPULATION)) %>%\n slice(1:10) %>%\n select(WATER_SY_1, population_reported = POPULATION, \n population_estimated = population_total_count, \n population_percent_difference) %>%\n st_drop_geometry() %>%\n kable()\n```\n\n::: {.cell-output-display}\n\n\n|WATER_SY_1 | population_reported| population_estimated| population_percent_difference|\n|:----------------------------------|-------------------:|--------------------:|-----------------------------:|\n|CITY OF SACRAMENTO MAIN | 510931| 516189| 1.03|\n|SACRAMENTO SUBURBAN WATER DISTRICT | 184385| 193126| 4.74|\n|SCWA - LAGUNA/VINEYARD | 172666| 145495| -15.74|\n|FOLSOM, CITY OF - MAIN | 68122| 62462| -8.31|\n|CITRUS HEIGHTS WATER DISTRICT | 65911| 68912| 4.55|\n|CALAM - SUBURBAN ROSEMONT | 53563| 57897| 8.09|\n|CALAM - PARKWAY | 48738| 58635| 20.31|\n|CALAM - LINCOLN OAKS | 47487| 42916| -9.63|\n|GOLDEN STATE WATER CO. - CORDOVA | 44928| 48115| 7.09|\n|ELK GROVE WATER SERVICE | 42540| 42647| 0.25|\n\n\n:::\n:::\n\n\n## Considerations for Detailed Population Estimates {#sec-detailed-pop-estimates}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nIf you're primarily only interested in population estimates (possibly including population by race/ethnicity, age, gender, etc.) and need an estimate that's as geographically accurate as possible, it may make more sense to use the block-level population data from the decennial census rather than block group level population data from the ACS. However, since the decennial census only occurs once every 10 years, those estimates won't reflect recent population changes (and will get especially less accurate as we get farther from the last decennial census). But keep in mind that even the 5-year ACS is an average that encompasses previous years' estimates, so it's not necessarily temporally precise either.\n\nIt's also possible to use the block-level decennial population data as a weighing factor for ACS population data (to allocate the population within block-group level ACS data).\n\n\\[TO DO: add example\\]\n\n## Considerations for Small / Rural Area Estimates {#sec-small-area-estimates}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nFor some water systems, the estimated population using the areal interpolation above (@sec-areal-interp) was at or near zero, and it may be useful to look at an example to see what's going on with one of those cases.\n\n(because the water system may encompass only a small portion of one or a few census units, and the entire census unit(s) may not be representative of the small portion(s)), especially those in rural environments (where population densities are lower, population centers tend to be spread out, and census units tend to be larger).\n\n\\[TO DO: insert map\\]\n\nFrom the map above \\[TO DO: insert map\\], you can see that the service area reported for some systems are very small, only covering a small fraction of a single census unit, resulting in a population estimate that is very low. In these cases, it could be that the system area was drawn incorrectly (i.e., maybe it doesn't really depict the entire service area), in which case the reported service area should be revised. Or, it's possible that the population within the given census unit is very un-evenly distributed and instead there's a relatively high density population cluster in the depicted service area, in which case a more sophisticated method than an area-weighted average should be used (e.g., maybe consider the density of buildings, roads, and/or other features associated with inhabited areas).\n\n## Alternative Computation Methods {#sec-alternative-methods}\n\n::: callout-warning\nThis section is in progress...\n:::\n\n### Population Weighted Interpolation {#sec-alternative-interpolate_pw}\n\nThe `tidycensus` package also has a function for population weighted interpolation, [`interpolate_pw`](https://walker-data.com/tidycensus/reference/interpolate_pw.html), but it uses a somewhat different methodology than the population weighted interpolation procedure applied above in @sec-pop-interp.\n\nNote that some water systems may not get an estimated value using this method, even if `NA` values are removed from the source data first (TO DO: check whether this depends on which type of boundary dataset is used - i.e., tigris with cb = FALSE or TRUE).\n\nUsing original `census_data_acs` variable gives multiple `NA`s - it looks like those are small areas:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresults_interpolate_pw <- interpolate_pw(from = census_data_acs %>%\n filter(!is.na(population_total_count)) %>% # population_total_count median_household_income\n select(population_total_count),\n to = water_systems_sac,\n to_id = 'WATER_SY_1',\n extensive = TRUE, # use FALSE for median_household_income\n weights = census_data_decennial,\n # weight_placement = 'surface',\n weight_column = 'population_total_count') %>%\n # rename(median_household_income_interpolate_pw = median_household_income) # rename results field\n rename(population_total_count_interpolate_pw = population_total_count) %>% \n mutate(population_total_count_interpolate_pw = round(population_total_count_interpolate_pw, 0))\n\n# sum(is.na(results_interpolate_pw$median_household_income_interpolate_pw))\nsum(is.na(results_interpolate_pw$population_total_count_interpolate_pw))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 17\n```\n\n\n:::\n:::\n\n\nUsing detailed block group geometry - looks like the same results?:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# # get detailed block group geometry\n# block_groups_detailed <- block_groups(state = 'CA',\n# county = 'Sacramento',\n# cb = FALSE,\n# year = acs_year) %>%\n# st_transform(crs_projected)\n# block_groups_detailed <- block_groups_detailed %>%\n# st_filter(water_systems_sac) %>%\n# select(GEOID)\n# \n# block_groups_detailed <- block_groups_detailed %>%\n# left_join(census_data_acs %>%\n# st_drop_geometry() %>%\n# select(GEOID, population_total_count, median_household_income),\n# by = 'GEOID')\n# \n# # interpolate\n# results_interpolate_pw_detailed <- interpolate_pw(from = block_groups_detailed %>%\n# filter(!is.na(population_total_count)) %>% # population_total_count median_household_income\n# select(population_total_count),\n# to = water_systems_sac,\n# to_id = 'WATER_SY_1',\n# extensive = TRUE, # use FALSE for median_household_income\n# weights = census_data_decennial,\n# weight_placement = 'surface',\n# weight_column = 'population_total_count') %>%\n# # rename(median_household_income_interpolate_pw = median_household_income) # rename results field\n# rename(population_total_count_interpolate_pw = population_total_count)\n# \n# # sum(is.na(results_interpolate_pw_detailed$median_household_income_interpolate_pw))\n# sum(is.na(results_interpolate_pw_detailed$population_total_count_interpolate_pw))\n```\n:::\n\n\nCompare results using `interpolate_pw` to reported population counts:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresults_interpolate_pw <- results_interpolate_pw %>%\n left_join(water_systems_sac %>%\n st_drop_geometry() %>%\n select(SERVICE_CO, POPULATION, WATER_SY_1),\n by = 'WATER_SY_1') %>% \n relocate(POPULATION, .after = population_total_count_interpolate_pw)\n```\n:::\n\n\nView results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglimpse(results_interpolate_pw)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 62\nColumns: 5\n$ WATER_SY_1 \"HOOD WATER MAINTENCE DIST [SWS]…\n$ population_total_count_interpolate_pw 74, 412, 96, NA, 1031, NA, NA, 1…\n$ POPULATION 100, 700, 40, 150, 256, 150, 32,…\n$ SERVICE_CO 82, 199, 34, 64, 128, 83, 28, 50…\n$ geometry MULTIPOLYGON (((-13…\n```\n\n\n:::\n:::\n\n\n## Working with Other Source Datasets {#sec-other-sources}\n\n::: callout-warning\nThis section is in progress...\n:::\n\nIn addition to using census data, it's possible to use other types of *source* datasets to compute characteristics of custom *target* areas like water systems. The process is generally likely to be similar to the processes shown above using census data, but each source dataset may require unique considerations (e.g., to handle missing values, uncertain boundaries, etc.).\n\n### CalEnviroScreen {#sec-calenviroscreen}\n\n\\[TO DO: compute population weighted average scores, using interpolated population estimates - (1) first compute interpolated populations by clipping ACS tracts to CES boundaries, then (2) clip those boundaries to water systems and compute populations, then (3) calculate population weighted averages (using areal-weighted populations from step 2)?\\]\n\nNotes to consider:\n\n- Some census tracts are missing CES scores (overall and/or for certain indicators), and have to deal with those missing values somehow\n\n- CES 4.0 is tract-level data, and uses 2010 census boundaries (so boundaries won't match current ACS boundaries)\n\n
\n", + "supporting": [ + "example_census_race_ethnicity_calculation_files" + ], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-all-1.png b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-all-1.png new file mode 100644 index 0000000..f888b25 Binary files /dev/null and b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-all-1.png differ diff --git a/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-system-1.png b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-system-1.png new file mode 100644 index 0000000..4c388ad Binary files /dev/null and b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/fig-bivariate-system-1.png differ diff --git a/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-49-1.png b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-49-1.png new file mode 100644 index 0000000..f888b25 Binary files /dev/null and b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-49-1.png differ diff --git a/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-50-1.png b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-50-1.png new file mode 100644 index 0000000..d563a25 Binary files /dev/null and b/01_document/_freeze/example_census_race_ethnicity_calculation/figure-html/unnamed-chunk-50-1.png differ diff --git a/01_document/example_census_race_ethnicity_calculation.qmd b/01_document/example_census_race_ethnicity_calculation.qmd index 07009e6..b87ace2 100644 --- a/01_document/example_census_race_ethnicity_calculation.qmd +++ b/01_document/example_census_race_ethnicity_calculation.qmd @@ -30,7 +30,7 @@ This document is a work in progress, and may change significantly. This document provides an example of how to use tools available from the [R programming language](https://www.R-project.org/) [@R] to estimate characteristics of any given *target* spatial area(s) (e.g., neighborhoods, project boundaries, water supplier service areas, etc.) based on data from a *source* dataset containing the characteristic data of interest (e.g., census data, CalEnvrioScreen scores, etc.), especially when the boundaries of the *source* and *target* areas overlap but don't necessarily align with each other. It also provides some brief background on the various types of data available from the U.S Census Bureau, and links to a few places to find more in-depth information. -This particular example estimates demographic characteristics of community water systems in the Sacramento County area (the *target* dataset). It uses the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package [@tidycensus] to access selected demographic data from the U.S. Census Bureau (the *source* dataset) for census units whose spatial extent covers those water systems' service areas, then uses the [`sf`](https://r-spatial.github.io/sf/) package [@sf] package (for working with spatial data) and the [`tidyverse`](https://www.tidyverse.org/) collection of packages (for general data cleaning and transformation) to estimate some demographic characteristics of each water system based on that census data. It also uses the [`areal`](https://chris-prener.github.io/areal/) R package [@areal] to check some of the results, and as general guidance on the principles and techniques for implementing areal interpolation. +This particular example estimates demographic characteristics of community water systems in the Sacramento County area (the *target* dataset). It uses the [`tidycensus`](https://walker-data.com/tidycensus/index.html) R package [@tidycensus] to access selected demographic data from the U.S. Census Bureau (the *source* dataset) for census units whose spatial extent covers those water systems' service areas, then uses the [`sf`](https://r-spatial.github.io/sf/) package [@sf] package (for working with spatial data) and the [`tidyverse`](https://www.tidyverse.org/) collection of packages [@tidyverse] (for general data cleaning and transformation) to estimate some demographic characteristics of each water system based on that census data. It also uses the [`areal`](https://chris-prener.github.io/areal/) R package [@areal] to check some of the results, and as general guidance on the principles and techniques for implementing areal interpolation. This example is just intended to be a simplified demonstration of a possible workflow. For a real analysis, additional steps and considerations -- that may not be covered here -- may be needed to deal with data inconsistencies (e.g., missing or incomplete data), required level of precision and acceptable assumptions (e.g. more fine-grained datasets or more sophisticated techniques could be used to estimate/model population distributions), or other project-specific issues that might arise. @@ -59,6 +59,10 @@ library(patchwork) library(scales) library(digest) library(mapview) +library(biscale) +library(cowplot) +library(glue) +library(ggtext) # conflicts ---- library(conflicted) @@ -593,8 +597,11 @@ We can also calculate the poverty rate for each census unit (which may be useful ```{r} census_data_acs <- census_data_acs %>% - mutate(poverty_rate_pct_calc_census_unit = 100 * poverty_below_count / poverty_total_assessed_count, - .after = poverty_above_count) + mutate(poverty_rate_pct_calc_census_unit = case_when( + poverty_total_assessed_count == 0 ~ 0, + .default = 100 * poverty_below_count / poverty_total_assessed_count + ), + .after = poverty_above_count) ``` ```{r} @@ -694,24 +701,35 @@ Next, we need to combine the weighted values calculated above to produce the est #### Combine Results by Water System -First, combine the results by summing all of the count-based variables (derived from areal interpolation), and calculating weighted averages for all variables computed in step 2 above. +First, combine the results by summing all of the count-based variables (derived from areal interpolation), and calculating weighted averages for all variables computed in step 2 above. Note that we have to first calculate the denominator for each variable calculated with population weighted interpolation, because some of those variables contain missing values for records where the denominator is present (and if we don't remove the missing values, we get an `NA` for any water system that contains a block group with a missing value for that variable). For example, there are block groups where the median household income is missing, but the total household count is available for that block group – in that case, the weighted average should not include the households in that block group in the denominator; otherwise, the true value will be underestimated. ```{r} water_system_demographics <- census_data_interpolate %>% group_by(WATER_SY_1) %>% + mutate( + average_household_size_denominator = if_else(is.na(average_household_size), 0, households_count), + median_household_income_denominator = if_else(is.na(median_household_income), 0, households_count), + per_capita_income_denominator = if_else(is.na(per_capita_income), 0, population_total_count) + ) %>% summarize( across( .cols = ends_with('_count'), .fns = ~ sum(.x) ), - average_household_size_hh_weighted = sum(average_household_size_weighted) / sum(households_count), - median_household_income_hh_weighted = sum(median_household_income_weighted) / sum(households_count), - per_capita_income_pop_weighted = sum(per_capita_income_weighted) / sum(population_total_count) + average_household_size_hh_weighted = + sum(average_household_size_weighted, na.rm = TRUE) / + sum(average_household_size_denominator), + median_household_income_hh_weighted = + sum(median_household_income_weighted, na.rm = TRUE) / + sum(median_household_income_denominator), + per_capita_income_pop_weighted = + sum(per_capita_income_weighted, na.rm = TRUE) / + sum(per_capita_income_denominator) ) %>% ungroup() ``` -#### Check - Variables Estimated with Areal Interpolation {#sec-check-areal-interp} +#### Check - Count Variables Estimated with Areal Interpolation {#sec-check-areal-interp} As noted above, it's also possible to use pre-built functions for areal interpolation. This section demonstrates those functions and uses them as a check of our computed count data. @@ -792,8 +810,11 @@ We can also calculate the estimated poverty rate for each water system's service ```{r} water_system_demographics <- water_system_demographics %>% - mutate(poverty_rate_percent = 100 * poverty_below_count / poverty_total_assessed_count, - .after = poverty_above_count) + mutate(poverty_rate_percent = case_when( + poverty_total_assessed_count == 0 ~ 0, + .default = 100 * poverty_below_count / poverty_total_assessed_count + ), + .after = poverty_above_count) ``` And compute income brackets in 25k increments: @@ -847,10 +868,20 @@ And compute \# and % of households below income thresholds: ``` -And, compute other variables (% households by % housing cost, ...) +And, compute the portion of households paying more than 30% / 50% of their income toward housing costs: ```{r} - +water_system_demographics <- water_system_demographics %>% + mutate(households_housing_costs_over30pct_percent = + 100 * (households_mortgage_over30pct_count + + households_no_mortgage_over30pct_count + + households_rent_over30pct_count) / + households_count) %>% + mutate(households_housing_costs_over50pct_percent = + 100 * (households_mortgage_over50pct_count + + households_no_mortgage_over50pct_count + + households_rent_over50pct_count) / + households_count) ``` Finally, we can round the estimated values to appropriate levels of precision: @@ -1072,6 +1103,178 @@ For simplicity, this section will focus on presenting estimated demographics for \[TO DO: add visualizations\] +Prepare interpolation data + +```{r} +# census_data_interpolate census_data_clip +census_data_interpolate <- census_data_interpolate %>% + mutate(households_housing_costs_over30pct_percent = + 100 * (households_mortgage_over30pct_count + + households_no_mortgage_over30pct_count + + households_rent_over30pct_count) / + households_count) %>% + mutate(households_housing_costs_over30pct_percent = + 100 * (households_mortgage_over50pct_count + + households_no_mortgage_over50pct_count + + households_rent_over50pct_count) / + households_count) +``` + +Select systems to plot + +```{r} +n_systems <- 20 +systems_top_n <- water_system_demographics %>% + slice_max(population_total_count, n = n_systems) %>% + pull(WATER_SY_1) +``` + +### Bivariate Map + +This section uses the [@biscale] R package to create bivariate choropleth maps that show how two variables vary together spatially. + +@fig-bivariate-all shows the relationship between relative housing costs and income – using the estimated data – for the top `{r} n_systems` systems by estimated population in Sacramento. + +```{r} +#| message: false +#| warning: false +#| fig-width: 8 +#| fig-height: 6 +#| label: fig-bivariate-all +#| code-fold: true + +# Table B25140 - Housing Costs as a Percentage of Household Income in the past 12 months. +# Shows the count of households paying more than 30% or 50% of their income towards housing costs broken out by three tenure categories (owned with a mortgage, owned without a mortgage, and rented). + +# set defaults +biscale_pal <- 'BlueOr' # 'GrPink' # 'DkViolet2' +biscale_dim <- 3 + +# create classes +biscale_data <- bi_class(water_system_demographics %>% + filter(WATER_SY_1 %in% systems_top_n) %>% + filter(!is.na(median_household_income_hh_weighted)), + x = households_housing_costs_over30pct_percent, + y = median_household_income_hh_weighted, + style = "quantile", + dim = biscale_dim) + +# create map +biscale_map <- ggplot() + + geom_sf(data = biscale_data, + mapping = aes(fill = bi_class), + color = "white", + size = 0.1, + show.legend = FALSE) + + bi_scale_fill(pal = biscale_pal, + dim = biscale_dim) + + labs( + title = "Estimated % of Households Paying More Than 30% of Income Towards Housing Costs \nand Estimated Median Household Income in Sacramento Water Systems", + subtitle = glue("Top {n_systems} systems by population"), + caption = glue("Data estimated from {acs_year} 5-year ACS Block Groups") + # title = "Estimated Housing Cost as % of Household Income and \nEstimated Median Household Income in Sacramento Water Systems", + # caption = "% Housing cost shows the percent of households paying more than 30% of their income towards housing costs \nIncome shows median household income (yellow = missing)" + ) + + # labs( + # title = "Housing Cost1 and Income2 in Sacramento Water Systems", + # caption = "1% of households paying more than 30% of their income towards housing costs
2Median household income (yellow = missing)", + # subtitle = glue("Top {n_systems} systems by population") + # ) + + # add missing polygons back in + geom_sf(data = water_system_demographics %>% + filter(WATER_SY_1 %in% systems_top_n) %>% + filter(is.na(median_household_income_hh_weighted)), + color = "white", + fill = 'gold' + ) + + geom_sf(data = counties_ca %>% filter(NAME == 'Sacramento'), + color = 'grey', + fill = NA) + + bi_theme() + + theme(plot.title = element_text(size=12), # element_markdown(size=12) + plot.subtitle = element_text(size=10), + plot.caption = element_text(size=8, hjust = 1)) # element_markdown(size=8, hjust = 1)) + +# create legend +biscale_legend <- bi_legend(pal = biscale_pal, + dim = biscale_dim, + xlab = "% Housing Costs ", + ylab = "Income ", + size = 8) + +# construct map +biscale_plot <- ggdraw() + + draw_plot(biscale_map, 0, 0, 1, 1) + + draw_plot(biscale_legend, 0.1, .65, 0.2, 0.2) + +biscale_plot +``` + +@fig-bivariate-system shows the same variables (relative housing costs and income) for the portions block groups overlapping `{r} str_to_title(system_plot)` – this illustrates the data underlying the interpolation process. + +```{r} +#| message: false +#| warning: false +#| fig-width: 8 +#| fig-height: 6 +#| label: fig-bivariate-system +#| code-fold: true + +# set defaults +biscale_pal_system <- 'BlueOr' # 'GrPink' # 'DkViolet2' +biscale_dim_system <- 3 + +# create classes +biscale_data_system <- bi_class(census_data_interpolate %>% + filter(WATER_SY_1 == system_plot) %>% + filter(!is.na(median_household_income)), + x = households_housing_costs_over30pct_percent, + y = median_household_income, + style = "quantile", + dim = biscale_dim_system) +# create map +biscale_map_system <- ggplot() + + geom_sf(data = biscale_data_system , + mapping = aes(fill = bi_class), + color = "white", + size = 0.1, + show.legend = FALSE) + + bi_scale_fill(pal = biscale_pal_system, + dim = biscale_dim_system) + + labs( + title = glue("Estimated % of Households Paying More Than 30% of Income Towards Housing Costs \nand Estimated Median Household Income in {str_to_title(system_plot)}"), + # subtitle = glue(""), + caption = glue("Data from {acs_year} 5-year ACS Block Groups (Yellow = Missing Data)")#, + # title = glue("Housing Cost and Income \nin {str_to_title(system_plot)}"), + # caption = "% Housing cost shows the percent of households paying more than 30% of their income towards housing costs \nIncome shows median household income (yellow = missing)"#, + ) + + # add the missing polygons back in + geom_sf(data = census_data_interpolate %>% + filter(WATER_SY_1 == system_plot) %>% + filter(is.na(median_household_income)), + color = "white", + fill = 'gold' + ) + + bi_theme() + + theme(plot.title = element_text(size=12), # element_markdown(size=12) + plot.subtitle = element_text(size=10), + plot.caption = element_text(size=8, hjust = 1)) # element_markdown(size=8, hjust = 1)) + +# create legend +biscale_legend <- bi_legend(pal = biscale_pal_system, + dim = biscale_dim_system, + xlab = "% Housing Costs ", + ylab = "Income ", + size = 8) + +# construct map +biscale_plot_system <- ggdraw() + + draw_plot(biscale_map_system, 0, 0, 1, 1) + + draw_plot(biscale_legend, 0.1, .55, 0.2, 0.2) + +biscale_plot_system +``` + ## Check - Estimated vs Reported Population Estimates {#sec-check-pop-estimated-reported} \[TO DO: Create map\] @@ -1097,7 +1300,7 @@ water_system_demographics <- water_system_demographics %>% .after = POPULATION) ``` -For water systems with a small population and/or service area, the estimated demographics may not match the population numbers in the original water system dataset very well. You can see this in @tbl-pop-est-small by comparing the `POPULATION` field, which contains the total population values from the water supplier dataset, with the `population_total` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. This probably indicates that, for small areas, some adjustments and/or further analysis may be needed, and the preliminary estimated values should be treated with some caution/skepticism. +For water systems with a small population and/or service area, the estimated demographics may not match the reported population numbers in the water system dataset very well. You can see this in @tbl-pop-est-small by comparing the `population_reported` field, which contains the total population values from the water supplier dataset, with the `population_estimated` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. This probably indicates that, for small areas, some adjustments and/or further analysis may be needed, and the preliminary estimated values should be treated with some caution/skepticism. Note: See @sec-small-area-estimates below for some more investigation into water systems whose estimated population is at or near zero. @@ -1108,12 +1311,14 @@ Note: See @sec-small-area-estimates below for some more investigation into water water_system_demographics %>% arrange(POPULATION) %>% slice(1:10) %>% - select(WATER_SY_1, POPULATION, population_total_count, population_percent_difference) %>% + select(WATER_SY_1, population_reported = POPULATION, + population_estimated = population_total_count, + population_percent_difference) %>% st_drop_geometry() %>% kable() ``` -But for larger water systems, the estimated population values seem to be more in line with the population numbers in the original dataset. You can see this in @tbl-pop-est-large by, as above, comparing the `POPULATION` field, which contains the total population values from the water supplier dataset, with the `population_total` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. +But for larger water systems, the estimated population values seem to be more in line with the population numbers in the original dataset. You can see this in @tbl-pop-est-large by, as above, comparing the `population_reported` field, which contains the total population values from the water supplier dataset, with the `population_estimated` field, which contains the total population estimated from the census data; the difference between the two is summarized in the `population_percent_difference` field. ```{r} #| label: tbl-pop-est-large @@ -1122,7 +1327,9 @@ But for larger water systems, the estimated population values seem to be more in water_system_demographics %>% arrange(desc(POPULATION)) %>% slice(1:10) %>% - select(WATER_SY_1, POPULATION, population_total_count, population_percent_difference) %>% + select(WATER_SY_1, population_reported = POPULATION, + population_estimated = population_total_count, + population_percent_difference) %>% st_drop_geometry() %>% kable() ``` @@ -1246,7 +1453,7 @@ glimpse(results_interpolate_pw) This section is in progress... ::: -In addition to using census data, it's possible to use other datasets to compute characteristics of custom *target* areas like water systems. The process is generally likely to be similar to the processes shown above using census data, but each dataset may require unique considerations (e.g., to handle missing values, uncertain boundaries, etc.). +In addition to using census data, it's possible to use other types of *source* datasets to compute characteristics of custom *target* areas like water systems. The process is generally likely to be similar to the processes shown above using census data, but each source dataset may require unique considerations (e.g., to handle missing values, uncertain boundaries, etc.). ### CalEnviroScreen {#sec-calenviroscreen} @@ -1254,7 +1461,7 @@ In addition to using census data, it's possible to use other datasets to compute Notes to consider: -- Some census tracts are missing CES scores (overall and/or for certain indicators), and have to deal with those missing values somehow. +- Some census tracts are missing CES scores (overall and/or for certain indicators), and have to deal with those missing values somehow - CES 4.0 is tract-level data, and uses 2010 census boundaries (so boundaries won't match current ACS boundaries) diff --git a/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-manual-pop-1.png b/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-manual-pop-1.png deleted file mode 100644 index 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a/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-suppliers-census-map-1.png b/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-suppliers-census-map-1.png deleted file mode 100644 index f915f1b..0000000 Binary files a/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-suppliers-census-map-1.png and /dev/null differ diff --git a/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-sys-bounds-1.png b/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-sys-bounds-1.png deleted file mode 100644 index d59e8a9..0000000 Binary files a/01_document/example_census_race_ethnicity_calculation_files/figure-html/fig-sys-bounds-1.png and /dev/null differ diff --git a/01_document/references.bib b/01_document/references.bib index d88d9a4..4d3f7b1 100644 --- a/01_document/references.bib +++ b/01_document/references.bib @@ -70,3 +70,11 @@ @article{tigris date = {2023}, url = {https://CRAN.R-project.org/package=tigris} } + +@article{biscale, + title = {biscale: Tools and Palettes for Bivariate Thematic Mapping}, + author = {Prener, Christopher and Grossenbacher, Timo and Zehr, Angelo}, + year = {2022}, + date = {2022}, + url = {https://CRAN.R-project.org/package=biscale} +} diff --git a/03_data_results/water_system_demographics_sac.csv b/03_data_results/water_system_demographics_sac.csv index 65c0994..4f7fae9 100644 --- a/03_data_results/water_system_demographics_sac.csv +++ b/03_data_results/water_system_demographics_sac.csv @@ -1,63 +1,63 @@ -WATER_SY_1,population_total_count,population_hispanic_or_latino_count,population_white_count,population_black_or_african_american_count,population_native_american_or_alaska_native_count,population_asian_count,population_pacific_islander_count,population_other_or_multiple_count,population_hispanic_or_latino_percent,population_white_percent,population_black_or_african_american_percent,population_native_american_or_alaska_native_percent,population_asian_percent,population_pacific_islander_percent,population_other_or_multiple_percent,poverty_total_assessed_count,poverty_below_count,poverty_above_count,poverty_rate_percent,households_count,households_income_below_10k_count,households_income_10k_15k_count,households_income_15k_20k_count,households_income_20k_25k_count,households_income_25k_30k_count,households_income_30k_35k_count,households_income_35k_40k_count,households_income_40k_45k_count,households_income_45k_50k_count,households_income_50k_60k_count,households_income_60k_75k_count,households_income_75k_100k_count,households_income_100k_125k_count,households_income_125k_150k_count,households_income_150k_200k_count,households_income_above_200k_count,households_income_0_25k_count,households_income_25k_50k_count,households_income_50k_75k_count,households_income_0_50k_count,households_income_50k_100k_count,households_income_100k_150k_count,households_mortgage_total_count,households_mortgage_over30pct_count,households_mortgage_over50pct_count,households_no_mortgage_total_count,households_no_mortgage_over30pct_count,households_no_mortgage_over50pct_count,households_rent_total_count,households_rent_over30pct_count,households_rent_over50pct_count,average_household_size_hh_weighted,median_household_income_hh_weighted,per_capita_income_pop_weighted -B & W RESORT MARINA,0,0,0,0,0,0,0,0,41.43,52.47,0,0,4.55,0,1.56,0,0,0,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.03,51977,40522 -CAL AM FRUITRIDGE VISTA,22603,10939,3504,2663,121,4075,240,1060,48.4,15.5,11.78,0.54,18.03,1.06,4.69,22556,6010,16546,26.64,6900,354,339,521,263,367,302,359,355,565,692,876,784,459,235,287,141,1477,1948,1569,3425,2352,694,1620,745,345,1236,95,58,4044,2131,1059,3.2578059416417346,53040.44113156888,20519.569076441432 -CALAM - ANTELOPE,33120,5245,19456,3199,113,2947,77,2082,15.84,58.74,9.66,0.34,8.9,0.23,6.29,33034,3389,29645,10.26,10529,315,184,101,122,116,469,248,368,449,737,1077,1669,1501,1077,1158,937,723,1650,1814,2373,3483,2578,5544,1861,621,1747,184,106,3238,1678,649,3.1345302087117366,93741.54894285185,34660.43709201826 -CALAM - ARDEN,10112,3433,2392,1977,70,1082,59,1100,33.95,23.65,19.55,0.69,10.7,0.58,10.87,10034,3130,6904,31.19,3823,201,259,239,167,319,190,142,236,207,440,394,535,228,148,62,58,866,1093,834,1959,1368,376,265,84,46,133,8,3,3426,2124,1170,2.6236426838601012,49624.62015306522,22770.82157980776 -CALAM - ISLETON,34,14,17,0,0,2,0,1,42.06,51.14,0,0,4.55,0,2.25,34,7,27,20.89,16,1,1,0,1,1,0,1,1,0,2,1,1,3,1,0,1,4,3,3,6,4,4,6,4,1,7,2,2,4,1,1,2.0789934965188213,57361.758044022434,40672.21234441078 -CALAM - LINCOLN OAKS,42916,9056,26529,1486,143,2706,288,2708,21.1,61.82,3.46,0.33,6.31,0.67,6.31,42823,4074,38749,9.51,15621,740,375,308,622,488,616,585,629,645,1035,1641,2442,1889,1272,1555,778,2046,2964,2675,5010,5118,3161,7390,2671,919,3332,503,298,4900,2523,1302,2.7302804909283616,NA,33728.94235778291 -CALAM - PARKWAY,58635,18665,8921,6965,21,19228,1386,3449,31.83,15.21,11.88,0.04,32.79,2.36,5.88,58434,9804,48630,16.78,17667,1081,753,514,713,694,640,713,700,727,1145,1918,2490,1634,1532,1546,865,3061,3475,3064,6536,5554,3166,7163,2719,1049,3418,647,383,7086,3517,1917,3.284607556891153,NA,26938.13941161509 -CALAM - SUBURBAN ROSEMONT,57897,13791,25062,7725,91,6905,380,3942,23.82,43.29,13.34,0.16,11.93,0.66,6.81,57661,8374,49287,14.52,21045,1156,612,472,744,653,568,582,874,628,1289,2508,3438,2595,1594,1671,1661,2985,3305,3797,6290,7235,4189,8262,2262,730,3425,439,271,9358,4521,2320,2.7269365676013217,NA,34497.37344907046 -CALAM - WALNUT GROVE,12,5,5,0,0,1,0,0,44.6,45.84,0,0,5.93,0,3.63,12,2,10,15.75,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,1,2,2,2,0,2,0,0,1,0,0,2,1,0,2.49,68248,38950 -CALIFORNIA STATE FAIR,532,78,262,91,0,48,0,52,14.68,49.25,17.13,0,9.1,0,9.85,526,152,374,28.89,285,65,13,8,5,9,14,2,0,23,29,30,35,21,11,17,3,91,48,59,140,93,32,0,0,0,0,0,0,285,177,95,1.82,52886,33141 -CARMICHAEL WATER DISTRICT,39253,6192,25026,2230,68,3326,295,2116,15.78,63.76,5.68,0.17,8.47,0.75,5.39,38700,5000,33700,12.92,15937,570,534,513,472,398,607,522,684,541,996,1595,1782,1724,1200,1678,2122,2088,2751,2591,4839,4373,2924,5256,1399,669,3147,358,177,7534,4056,2068,2.405914184458731,NA,46901.802443947105 -CITRUS HEIGHTS WATER DISTRICT,68912,12380,48148,2092,162,2875,71,3186,17.96,69.87,3.04,0.23,4.17,0.1,4.62,68581,6961,61620,10.15,25633,1012,569,446,769,665,867,841,723,1165,1875,3057,3954,2744,2332,2533,2080,2796,4261,4932,7057,8886,5075,10344,3553,1380,4293,554,286,10996,5759,2620,2.653808184173017,82960.7829472826,37323.17430154909 -CITY OF SACRAMENTO MAIN,516189,151211,159508,62060,1249,98585,9242,34334,29.29,30.9,12.02,0.24,19.1,1.79,6.65,508800,77003,431797,15.13,194000,9540,9401,6217,6407,5804,6255,6278,6139,6729,13349,17396,26982,20453,15080,17439,20531,31564,31205,30745,62769,57728,35533,67435,21769,8217,29857,3476,1805,96708,47510,24524,2.6095944008555163,NA,39105.608740826385 -DEL PASO MANOR COUNTY WATER DI,5592,687,3967,390,15,119,31,382,12.28,70.95,6.97,0.26,2.13,0.56,6.84,5592,621,4971,11.1,2222,170,45,54,66,21,51,66,237,40,158,278,166,171,120,347,231,336,416,436,752,601,291,922,326,189,572,112,68,729,509,114,2.5168950080102928,NA,40254.832685705536 -DELTA CROSSING MHP,0,0,0,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,0,0,0,17.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510 -EAST WALNUT GROVE [SWS],3,2,2,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,3,1,3,15.75,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,2.49,68248,38950 -EDGEWATER MOBILE HOME PARK,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103 -EL DORADO MOBILE HOME PARK,139,84,11,15,0,19,0,11,60.26,7.8,10.48,0,13.27,0,8.19,139,60,79,43.12,48,6,10,0,4,6,1,0,8,1,7,0,1,0,4,0,1,19,15,8,34,9,4,3,0,0,10,5,5,35,17,10,2.71,29468,17394 -EL DORADO WEST MHP,148,89,12,16,0,20,0,12,60.26,7.8,10.48,0,13.27,0,8.19,147,63,84,43.12,51,6,10,0,4,6,1,0,8,2,8,0,1,0,5,0,1,20,16,8,37,9,5,3,0,0,10,6,6,38,18,10,2.7099999999999995,29468.000000000004,17394 -ELEVEN OAKS MOBILE HOME COMMUNITY,233,45,94,56,0,37,0,1,19.27,40.19,24.01,0,15.91,0,0.62,233,87,146,37.48,71,7,2,3,6,10,2,1,1,3,1,13,17,3,0,3,0,17,17,15,34,32,3,8,3,1,21,1,1,42,29,23,3.28,60521,18213 -ELK GROVE WATER SERVICE,42647,7656,19550,3209,70,8939,388,2835,17.95,45.84,7.53,0.16,20.96,0.91,6.65,42258,3264,38994,7.72,13239,430,202,253,224,328,102,345,292,245,667,1117,1441,1470,1386,1907,2832,1108,1311,1784,2420,3225,2856,7552,1903,628,2861,283,113,2826,1595,864,3.179068135170295,122770.99741351404,43429.03313732531 -FAIR OAKS WATER DISTRICT,36003,4655,27050,708,94,1372,12,2113,12.93,75.13,1.97,0.26,3.81,0.03,5.87,35775,2852,32923,7.97,14233,546,332,113,229,208,391,206,469,293,804,1064,2214,1447,1568,1875,2474,1220,1568,1868,2788,4082,3016,7090,1872,845,3092,261,108,4051,1844,768,2.4802167837955067,NA,54435.00970487184 -FLORIN COUNTY WATER DISTRICT,9951,2963,1548,1394,7,2743,866,430,29.78,15.56,14.01,0.07,27.56,8.7,4.32,9835,1285,8550,13.06,2755,84,125,53,154,103,46,86,176,224,258,223,432,297,215,143,137,417,635,481,1051,913,512,981,426,90,675,49,28,1100,476,260,3.573005180660505,67048.12268615587,24517.639859299343 -FOLSOM STATE PRISON,3536,1257,652,1390,57,70,34,77,35.55,18.43,39.31,1.6,1.97,0.96,2.17,29,1,28,2.2,23,0,0,0,0,0,0,0,0,0,0,0,0,4,4,12,1,0,0,0,0,1,8,3,1,0,0,0,0,19,0,0,NA,161047.2164545734,2271.2201161818602 -"FOLSOM, CITY OF - ASHLAND",3845,318,2934,43,1,125,1,423,8.26,76.32,1.12,0.03,3.26,0.02,10.99,3780,143,3637,3.79,1800,44,17,104,43,34,209,103,74,43,43,158,248,132,80,123,345,208,463,201,670,449,212,594,164,90,847,368,82,358,196,74,NA,NA,56773.973812166274 -"FOLSOM, CITY OF - MAIN",62462,8433,35222,1693,105,12934,177,3897,13.5,56.39,2.71,0.17,20.71,0.28,6.24,62115,3405,58710,5.48,22409,807,218,390,477,418,283,329,373,451,670,1181,2255,2382,1747,4083,6344,1892,1855,1851,3747,4106,4129,11491,2728,1179,3590,237,146,7328,3010,1321,NA,NA,NA -FREEPORT MARINA,3,2,1,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,3,1,3,17.42,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510 -"GALT, CITY OF",21490,9314,9952,520,22,872,20,789,43.34,46.31,2.42,0.1,4.06,0.09,3.67,21341,1404,19937,6.58,6988,139,168,243,210,141,342,161,347,152,550,687,807,1096,504,789,650,761,1143,1237,1904,2044,1601,3724,907,523,1454,109,44,1809,906,414,3.048248495417214,NA,33685.541351256594 -GOLDEN STATE WATER CO - ARDEN WATER SERV,6556,1706,2887,322,0,888,11,742,26.02,44.04,4.91,0,13.54,0.16,11.32,6453,1626,4828,25.19,2173,19,82,19,141,53,173,34,179,37,139,351,319,132,172,141,183,262,476,490,738,809,303,728,239,123,131,0,0,1315,599,335,2.8977160737987506,NA,30417.362367453836 -GOLDEN STATE WATER CO. - CORDOVA,48115,9009,26042,3982,229,6050,188,2615,18.72,54.13,8.28,0.48,12.57,0.39,5.43,47835,4408,43427,9.21,18022,509,482,310,496,480,437,389,469,598,1276,1692,2653,2565,1671,1948,2047,1796,2374,2968,4170,5621,4236,7380,2174,836,3506,364,201,7137,2744,1410,NA,NA,NA -HAPPY HARBOR (SWS),0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103 -HOLIDAY MOBILE VILLAGE,46,18,7,3,0,15,0,3,38.66,15.12,7.1,0,32.49,0,6.64,46,10,36,22.33,16,2,1,0,1,0,1,5,1,0,0,2,2,1,0,0,0,4,7,2,11,4,1,2,0,0,2,1,1,12,6,4,2.86,38491,16707 -HOOD WATER MAINTENCE DIST [SWS],1,1,0,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,1,0,1,17.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510 -IMPERIAL MANOR MOBILEHOME COMMUNITY,209,52,129,1,0,6,0,21,24.93,61.63,0.45,0,2.93,0,10.05,209,45,164,21.48,124,4,26,18,3,0,16,7,5,6,1,4,29,0,0,0,6,51,34,5,84,34,0,9,0,0,89,37,34,27,27,22,1.6803625908618791,31831.837612603995,32878.16681958172 -KORTHS PIRATES LAIR,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103 -LAGUNA DEL SOL INC,24,5,18,0,0,0,0,0,21.55,75.2,0,0.67,1.46,0,1.12,24,2,22,6.4,9,0,1,1,0,0,0,0,0,0,0,0,2,0,0,1,2,2,1,0,3,2,1,5,2,2,3,0,0,2,0,0,2.64,95227,50793 -LAGUNA VILLAGE RV PARK,20,3,2,1,0,11,2,2,12.79,8.48,7.28,0,52.62,8.38,10.45,20,2,18,11.79,7,1,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,2,2,1,3,1,0,1,0,0,3,1,0,3.03,84332,32668 -LINCOLN CHAN-HOME RANCH,4,2,2,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,4,1,3,15.75,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,0,0,2.49,68248,38950 -LOCKE WATER WORKS CO [SWS],1,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,1,0,1,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950 -MAGNOLIA MUTUAL WATER,1,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,1,0,1,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950 -MC CLELLAN MHP,269,52,108,65,0,43,0,2,19.27,40.19,24.01,0,15.91,0,0.62,269,101,168,37.48,82,8,2,3,7,11,2,2,1,3,1,15,20,3,0,3,0,20,19,17,39,36,3,9,4,2,25,1,1,48,34,27,3.28,60521,18213 -OLYMPIA MOBILODGE,290,70,81,18,0,101,16,3,24.12,28.03,6.3,0,34.95,5.53,1.08,290,68,222,23.43,114,11,0,6,10,9,3,13,0,0,10,19,8,3,12,5,5,28,25,29,53,36,14,31,22,10,51,12,10,33,9,7,2.5100000000000002,53786,29451 -ORANGE VALE WATER COMPANY,17387,2658,12308,241,181,633,86,1281,15.28,70.79,1.39,1.04,3.64,0.49,7.37,17288,1904,15384,11.01,6595,389,111,61,94,226,58,274,120,181,372,752,990,901,626,678,766,655,858,1123,1512,2113,1526,3246,1021,453,1686,315,185,1663,693,305,2.608348457768683,92693.71491876646,42509.89363050402 -PLANTATION MOBILE HOME PARK,10,4,1,1,0,3,0,1,38.66,15.12,7.1,0,32.49,0,6.64,10,2,7,22.33,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,2,1,0,1,0,0,0,0,0,2,1,1,2.86,38491,16707 -RANCHO MARINA,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103 -RANCHO MURIETA COMMUNITY SERVI,3239,661,2157,120,7,188,0,106,20.42,66.59,3.71,0.21,5.8,0,3.26,3239,199,3040,6.13,1402,59,42,0,6,5,18,74,27,75,44,81,88,118,204,241,319,108,199,125,307,213,323,1029,205,103,270,63,57,103,41,40,2.307704065813508,144993.80707581018,66451.34059033732 -RIO COSUMNES CORRECTIONAL CENTER [SWS],22,6,8,4,1,1,0,2,25.74,37.49,16.82,2.97,4.5,1.81,10.66,4,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3.45,115897.00000000001,11095 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,11831,2585,7595,337,17,765,21,512,21.85,64.19,2.85,0.14,6.46,0.18,4.33,11829,1619,10210,13.69,3762,177,156,67,169,56,113,116,114,118,173,297,607,492,431,416,259,569,518,470,1087,1077,922,1918,573,157,773,114,47,1070,519,340,3.1230123203938827,NA,33734.48719704626 -RIVER'S EDGE MARINA & RESORT,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103 -SAC CITY MOBILE HOME COMMUNITY LP,229,82,17,7,0,123,0,0,35.66,7.5,3.27,0,53.57,0,0,229,110,119,48.14,89,11,16,9,10,8,0,0,4,2,7,1,13,4,4,0,0,46,14,8,60,21,8,4,2,2,15,2,0,71,41,30,2.53,22380,16689 -SACRAMENTO SUBURBAN WATER DISTRICT,193126,43047,97872,17684,834,20602,624,12464,22.29,50.68,9.16,0.43,10.67,0.32,6.45,190984,33399,157585,17.49,72505,3817,3001,3069,2884,3205,3100,3337,2893,2342,5541,6792,10037,6480,4342,5488,6177,12771,14878,12333,27649,22370,10822,23467,7204,2837,12037,2087,1160,37001,21072,10274,2.635470822506937,NA,35321.17943972356 -SAN JUAN WATER DISTRICT,30122,3409,21349,831,287,2762,17,1467,11.32,70.87,2.76,0.95,9.17,0.06,4.87,30014,1718,28297,5.72,10750,389,168,100,275,128,160,111,133,127,472,684,984,854,876,1032,4256,932,658,1156,1591,2141,1730,6210,1754,724,2883,528,357,1658,726,339,2.7838582261197615,NA,72978.42336271124 -SCWA - ARDEN PARK VISTA,8086,990,6016,270,12,396,8,395,12.24,74.4,3.33,0.15,4.9,0.1,4.88,8038,523,7515,6.51,3303,79,36,48,77,65,38,18,49,162,139,187,253,465,208,416,1065,241,330,326,571,579,673,1823,520,112,673,76,23,807,384,225,2.424845516612799,NA,84548.46138496802 -SCWA - LAGUNA/VINEYARD,145495,27502,38496,16568,246,50411,2220,10052,18.9,26.46,11.39,0.17,34.65,1.53,6.91,145198,14710,130489,10.13,45137,1692,666,742,878,839,1336,850,788,752,2363,3198,6037,5323,5057,6578,8038,3978,4565,5561,8543,11598,10380,24581,7232,2916,7878,861,471,12677,6368,3337,3.2074469367308294,NA,41415.71495309661 -SCWA MATHER-SUNRISE,18249,2708,8114,1553,23,4507,164,1180,14.84,44.47,8.51,0.12,24.7,0.9,6.47,18211,1005,17206,5.52,5503,228,35,97,57,68,39,12,20,36,189,320,533,645,755,1003,1469,416,174,509,590,1042,1399,3756,881,266,855,60,43,893,318,167,NA,NA,NA -SEQUOIA WATER ASSOC,0,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,0,0,0,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950 -SOUTHWEST TRACT W M D [SWS],174,29,42,24,3,75,1,0,16.58,24.48,13.69,1.55,43.11,0.6,0,174,38,136,21.83,57,1,2,7,0,7,0,0,10,12,3,2,5,0,1,2,4,10,29,6,39,10,1,3,1,0,8,0,0,45,29,7,3.04,45671,36348 -SPINDRIFT MARINA,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.7899999999999998,38125,33103 -TOKAY PARK WATER CO,652,214,134,37,0,239,0,28,32.8,20.55,5.61,0,36.69,0,4.35,652,113,539,17.29,173,2,2,3,21,0,0,13,13,10,18,27,36,14,4,10,0,27,36,45,64,81,18,81,38,11,44,0,0,48,32,12,3.7579731008260437,62802.23785121953,19400.04804878149 -TUNNEL TRAILER PARK,0,0,0,0,0,0,0,0,49.74,34.94,0,0,4.65,0,10.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.95,153092,42507 -"VIEIRA'S RESORT, INC",4,2,2,0,0,0,0,0,41.43,52.47,0,0,4.55,0,1.56,4,1,3,22.6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0,0,0,0,2.03,51977,40522 -WESTERNER MOBILE HOME PARK,32,6,6,9,0,10,0,1,17.59,17.62,28.31,0.55,31.36,0,4.57,31,7,24,23.76,10,1,0,0,0,1,0,0,1,0,2,1,1,2,0,1,0,1,2,3,3,4,2,4,2,1,1,0,0,5,3,2,3.16,59296.00000000001,23437 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+B & W RESORT MARINA,0,0,0,0,0,0,0,0,41.43,52.47,0,0,4.55,0,1.56,0,0,0,22.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.03,51977,40522,40.53,21.84 +CAL AM FRUITRIDGE VISTA,22603,10939,3504,2663,121,4075,240,1060,48.4,15.5,11.78,0.54,18.03,1.06,4.69,22556,6010,16546,26.64,6900,354,339,521,263,367,302,359,355,565,692,876,784,459,235,287,141,1477,1948,1569,3425,2352,694,1620,745,345,1236,95,58,4044,2131,1059,3.2578059416417346,53040.44113156888,20519.569076441432,43.06,21.18 +CALAM - ANTELOPE,33120,5245,19456,3199,113,2947,77,2082,15.84,58.74,9.66,0.34,8.9,0.23,6.29,33034,3389,29645,10.26,10529,315,184,101,122,116,469,248,368,449,737,1077,1669,1501,1077,1158,937,723,1650,1814,2373,3483,2578,5544,1861,621,1747,184,106,3238,1678,649,3.1345302087117366,93741.54894285185,34660.43709201826,35.36,13.07 +CALAM - ARDEN,10112,3433,2392,1977,70,1082,59,1100,33.95,23.65,19.55,0.69,10.7,0.58,10.87,10034,3130,6904,31.19,3823,201,259,239,167,319,190,142,236,207,440,394,535,228,148,62,58,866,1093,834,1959,1368,376,265,84,46,133,8,3,3426,2124,1170,2.6236426838601012,49624.62015306522,22770.82157980776,57.97,31.87 +CALAM - ISLETON,34,14,17,0,0,2,0,1,42.06,51.14,0,0,4.55,0,2.25,34,7,27,20.89,16,1,1,0,1,1,0,1,1,0,2,1,1,3,1,0,1,4,3,3,6,4,4,6,4,1,7,2,2,4,1,1,2.0789934965188213,57361.758044022434,40672.21234441078,39.45,20.68 +CALAM - LINCOLN OAKS,42916,9056,26529,1486,143,2706,288,2708,21.1,61.82,3.46,0.33,6.31,0.67,6.31,42823,4074,38749,9.51,15621,740,375,308,622,488,616,585,629,645,1035,1641,2442,1889,1272,1555,778,2046,2964,2675,5010,5118,3161,7390,2671,919,3332,503,298,4900,2523,1302,2.7302804909283616,82035.52088760637,33728.94235778291,36.46,16.13 +CALAM - PARKWAY,58635,18665,8921,6965,21,19228,1386,3449,31.83,15.21,11.88,0.04,32.79,2.36,5.88,58434,9804,48630,16.78,17667,1081,753,514,713,694,640,713,700,727,1145,1918,2490,1634,1532,1546,865,3061,3475,3064,6536,5554,3166,7163,2719,1049,3418,647,383,7086,3517,1917,3.284607556891153,72938.51439696936,26938.13941161509,38.96,18.96 +CALAM - SUBURBAN ROSEMONT,57897,13791,25062,7725,91,6905,380,3942,23.82,43.29,13.34,0.16,11.93,0.66,6.81,57661,8374,49287,14.52,21045,1156,612,472,744,653,568,582,874,628,1289,2508,3438,2595,1594,1671,1661,2985,3305,3797,6290,7235,4189,8262,2262,730,3425,439,271,9358,4521,2320,2.7269365676013217,81229.87090779487,34497.37344907046,34.31,15.78 +CALAM - WALNUT GROVE,12,5,5,0,0,1,0,0,44.6,45.84,0,0,5.93,0,3.63,12,2,10,15.75,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,1,2,2,2,0,2,0,0,1,0,0,2,1,0,2.49,68248,38950,24.49,14.65 +CALIFORNIA STATE FAIR,532,78,262,91,0,48,0,52,14.68,49.25,17.13,0,9.1,0,9.85,526,152,374,28.89,285,65,13,8,5,9,14,2,0,23,29,30,35,21,11,17,3,91,48,59,140,93,32,0,0,0,0,0,0,285,177,95,1.82,52886,33141,62.11,33.45 +CARMICHAEL WATER DISTRICT,39253,6192,25026,2230,68,3326,295,2116,15.78,63.76,5.68,0.17,8.47,0.75,5.39,38700,5000,33700,12.92,15937,570,534,513,472,398,607,522,684,541,996,1595,1782,1724,1200,1678,2122,2088,2751,2591,4839,4373,2924,5256,1399,669,3147,358,177,7534,4056,2068,2.405914184458731,96967.64494126133,46901.802443947105,36.48,18.29 +CITRUS HEIGHTS WATER DISTRICT,68912,12380,48148,2092,162,2875,71,3186,17.96,69.87,3.04,0.23,4.17,0.1,4.62,68581,6961,61620,10.15,25633,1012,569,446,769,665,867,841,723,1165,1875,3057,3954,2744,2332,2533,2080,2796,4261,4932,7057,8886,5075,10344,3553,1380,4293,554,286,10996,5759,2620,2.653808184173017,82960.7829472826,37323.17430154909,38.49,16.72 +CITY OF SACRAMENTO MAIN,516189,151211,159508,62060,1249,98585,9242,34334,29.29,30.9,12.02,0.24,19.1,1.79,6.65,508800,77003,431797,15.13,194000,9540,9401,6217,6407,5804,6255,6278,6139,6729,13349,17396,26982,20453,15080,17439,20531,31564,31205,30745,62769,57728,35533,67435,21769,8217,29857,3476,1805,96708,47510,24524,2.6095944008555163,84694.01855912723,39105.608740826385,37.5,17.81 +DEL PASO MANOR COUNTY WATER DI,5592,687,3967,390,15,119,31,382,12.28,70.95,6.97,0.26,2.13,0.56,6.84,5592,621,4971,11.1,2222,170,45,54,66,21,51,66,237,40,158,278,166,171,120,347,231,336,416,436,752,601,291,922,326,189,572,112,68,729,509,114,2.5168950080102928,90374.38264756008,40254.832685705536,42.59,16.67 +DELTA CROSSING MHP,0,0,0,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,0,0,0,17.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510,45.66,25.57 +EAST WALNUT GROVE [SWS],3,2,2,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,3,1,3,15.75,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,2.49,68248,38950,24.49,14.65 +EDGEWATER MOBILE HOME PARK,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103,28.02,23.19 +EL DORADO MOBILE HOME PARK,139,84,11,15,0,19,0,11,60.26,7.8,10.48,0,13.27,0,8.19,139,60,79,43.12,48,6,10,0,4,6,1,0,8,1,7,0,1,0,4,0,1,19,15,8,34,9,4,3,0,0,10,5,5,35,17,10,2.71,29468,17394,46.7,31.09 +EL DORADO WEST MHP,148,89,12,16,0,20,0,12,60.26,7.8,10.48,0,13.27,0,8.19,147,63,84,43.12,51,6,10,0,4,6,1,0,8,2,8,0,1,0,5,0,1,20,16,8,37,9,5,3,0,0,10,6,6,38,18,10,2.7099999999999995,29468.000000000004,17394,46.7,31.09 +ELEVEN OAKS MOBILE HOME COMMUNITY,233,45,94,56,0,37,0,1,19.27,40.19,24.01,0,15.91,0,0.62,233,87,146,37.48,71,7,2,3,6,10,2,1,1,3,1,13,17,3,0,3,0,17,17,15,34,32,3,8,3,1,21,1,1,42,29,23,3.28,60521,18213,46.85,35.36 +ELK GROVE WATER SERVICE,42647,7656,19550,3209,70,8939,388,2835,17.95,45.84,7.53,0.16,20.96,0.91,6.65,42258,3264,38994,7.72,13239,430,202,253,224,328,102,345,292,245,667,1117,1441,1470,1386,1907,2832,1108,1311,1784,2420,3225,2856,7552,1903,628,2861,283,113,2826,1595,864,3.179068135170295,122770.99741351404,43429.03313732531,28.55,12.12 +FAIR OAKS WATER DISTRICT,36003,4655,27050,708,94,1372,12,2113,12.93,75.13,1.97,0.26,3.81,0.03,5.87,35775,2852,32923,7.97,14233,546,332,113,229,208,391,206,469,293,804,1064,2214,1447,1568,1875,2474,1220,1568,1868,2788,4082,3016,7090,1872,845,3092,261,108,4051,1844,768,2.4802167837955067,107985.74325851546,54435.00970487184,27.94,12.09 +FLORIN COUNTY WATER DISTRICT,9951,2963,1548,1394,7,2743,866,430,29.78,15.56,14.01,0.07,27.56,8.7,4.32,9835,1285,8550,13.06,2755,84,125,53,154,103,46,86,176,224,258,223,432,297,215,143,137,417,635,481,1051,913,512,981,426,90,675,49,28,1100,476,260,3.573005180660505,67048.12268615587,24517.639859299343,34.48,13.7 +FOLSOM STATE PRISON,3536,1257,652,1390,57,70,34,77,35.55,18.43,39.31,1.6,1.97,0.96,2.17,29,1,28,2.2,23,0,0,0,0,0,0,0,0,0,0,0,0,4,4,12,1,0,0,0,0,1,8,3,1,0,0,0,0,19,0,0,2.726311489407616,161047.2164545734,2271.2201161818602,4.67,0.53 +"FOLSOM, CITY OF - ASHLAND",3845,318,2934,43,1,125,1,423,8.26,76.32,1.12,0.03,3.26,0.02,10.99,3780,143,3637,3.79,1800,44,17,104,43,34,209,103,74,43,43,158,248,132,80,123,345,208,463,201,670,449,212,594,164,90,847,368,82,358,196,74,2.087285536960886,76810.17111631615,56773.973812166274,40.42,13.7 +"FOLSOM, CITY OF - MAIN",62462,8433,35222,1693,105,12934,177,3897,13.5,56.39,2.71,0.17,20.71,0.28,6.24,62115,3405,58710,5.48,22409,807,218,390,477,418,283,329,373,451,670,1181,2255,2382,1747,4083,6344,1892,1855,1851,3747,4106,4129,11491,2728,1179,3590,237,146,7328,3010,1321,2.7693559569400805,141856.37152396925,58469.34569630441,26.66,11.81 +FREEPORT MARINA,3,2,1,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,3,1,3,17.42,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510,45.66,25.57 +"GALT, CITY OF",21490,9314,9952,520,22,872,20,789,43.34,46.31,2.42,0.1,4.06,0.09,3.67,21341,1404,19937,6.58,6988,139,168,243,210,141,342,161,347,152,550,687,807,1096,504,789,650,761,1143,1237,1904,2044,1601,3724,907,523,1454,109,44,1809,906,414,3.048248495417214,90632.9331221346,33685.541351256594,27.52,14.05 +GOLDEN STATE WATER CO - ARDEN WATER SERV,6556,1706,2887,322,0,888,11,742,26.02,44.04,4.91,0,13.54,0.16,11.32,6453,1626,4828,25.19,2173,19,82,19,141,53,173,34,179,37,139,351,319,132,172,141,183,262,476,490,738,809,303,728,239,123,131,0,0,1315,599,335,2.8977160737987506,66579.356425836,30417.362367453836,38.56,21.09 +GOLDEN STATE WATER CO. - CORDOVA,48115,9009,26042,3982,229,6050,188,2615,18.72,54.13,8.28,0.48,12.57,0.39,5.43,47835,4408,43427,9.21,18022,509,482,310,496,480,437,389,469,598,1276,1692,2653,2565,1671,1948,2047,1796,2374,2968,4170,5621,4236,7380,2174,836,3506,364,201,7137,2744,1410,2.6507166775240423,96697.06109071148,42695.412199827406,29.31,13.58 +HAPPY HARBOR (SWS),0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103,28.02,23.19 +HOLIDAY MOBILE VILLAGE,46,18,7,3,0,15,0,3,38.66,15.12,7.1,0,32.49,0,6.64,46,10,36,22.33,16,2,1,0,1,0,1,5,1,0,0,2,2,1,0,0,0,4,7,2,11,4,1,2,0,0,2,1,1,12,6,4,2.86,38491,16707,44.88,28.55 +HOOD WATER MAINTENCE DIST [SWS],1,1,0,0,0,0,0,0,69.19,28.71,0,0,0,0,2.1,1,0,1,17.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.55,56250,23510,45.66,25.57 +IMPERIAL MANOR MOBILEHOME COMMUNITY,209,52,129,1,0,6,0,21,24.93,61.63,0.45,0,2.93,0,10.05,209,45,164,21.48,124,4,26,18,3,0,16,7,5,6,1,4,29,0,0,0,6,51,34,5,84,34,0,9,0,0,89,37,34,27,27,22,1.6803625908618791,31831.837612603995,32878.16681958172,50.97,45.07 +KORTHS PIRATES LAIR,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103,28.02,23.19 +LAGUNA DEL SOL INC,24,5,18,0,0,0,0,0,21.55,75.2,0,0.67,1.46,0,1.12,24,2,22,6.4,9,0,1,1,0,0,0,0,0,0,0,0,2,0,0,1,2,2,1,0,3,2,1,5,2,2,3,0,0,2,0,0,2.64,95227,50793,23.37,23.37 +LAGUNA VILLAGE RV PARK,20,3,2,1,0,11,2,2,12.79,8.48,7.28,0,52.62,8.38,10.45,20,2,18,11.79,7,1,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,2,2,1,3,1,0,1,0,0,3,1,0,3.03,84332,32668,32.52,12.26 +LINCOLN CHAN-HOME RANCH,4,2,2,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,4,1,3,15.75,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,0,0,2.49,68248,38950,24.49,14.65 +LOCKE WATER WORKS CO [SWS],1,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,1,0,1,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950,24.49,14.65 +MAGNOLIA MUTUAL WATER,1,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,1,0,1,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950,24.49,14.65 +MC CLELLAN MHP,269,52,108,65,0,43,0,2,19.27,40.19,24.01,0,15.91,0,0.62,269,101,168,37.48,82,8,2,3,7,11,2,2,1,3,1,15,20,3,0,3,0,20,19,17,39,36,3,9,4,2,25,1,1,48,34,27,3.28,60521,18213,46.85,35.36 +OLYMPIA MOBILODGE,290,70,81,18,0,101,16,3,24.12,28.03,6.3,0,34.95,5.53,1.08,290,68,222,23.43,114,11,0,6,10,9,3,13,0,0,10,19,8,3,12,5,5,28,25,29,53,36,14,31,22,10,51,12,10,33,9,7,2.5100000000000002,53786,29451,37.35,23.74 +ORANGE VALE WATER COMPANY,17387,2658,12308,241,181,633,86,1281,15.28,70.79,1.39,1.04,3.64,0.49,7.37,17288,1904,15384,11.01,6595,389,111,61,94,226,58,274,120,181,372,752,990,901,626,678,766,655,858,1123,1512,2113,1526,3246,1021,453,1686,315,185,1663,693,305,2.608348457768683,92693.71491876646,42509.89363050402,30.77,14.29 +PLANTATION MOBILE HOME PARK,10,4,1,1,0,3,0,1,38.66,15.12,7.1,0,32.49,0,6.64,10,2,7,22.33,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,2,1,0,1,0,0,0,0,0,2,1,1,2.86,38491,16707,44.88,28.55 +RANCHO MARINA,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103,28.02,23.19 +RANCHO MURIETA COMMUNITY SERVI,3239,661,2157,120,7,188,0,106,20.42,66.59,3.71,0.21,5.8,0,3.26,3239,199,3040,6.13,1402,59,42,0,6,5,18,74,27,75,44,81,88,118,204,241,319,108,199,125,307,213,323,1029,205,103,270,63,57,103,41,40,2.307704065813508,144993.80707581018,66451.34059033732,22.02,14.3 +RIO COSUMNES CORRECTIONAL CENTER [SWS],22,6,8,4,1,1,0,2,25.74,37.49,16.82,2.97,4.5,1.81,10.66,4,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3.45,115897.00000000001,11095,23.75,0 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,11831,2585,7595,337,17,765,21,512,21.85,64.19,2.85,0.14,6.46,0.18,4.33,11829,1619,10210,13.69,3762,177,156,67,169,56,113,116,114,118,173,297,607,492,431,416,259,569,518,470,1087,1077,922,1918,573,157,773,114,47,1070,519,340,3.1230123203938827,83603.04124462775,33734.48719704626,32.07,14.49 +RIVER'S EDGE MARINA & RESORT,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,38125,33103,28.02,23.19 +SAC CITY MOBILE HOME COMMUNITY LP,229,82,17,7,0,123,0,0,35.66,7.5,3.27,0,53.57,0,0,229,110,119,48.14,89,11,16,9,10,8,0,0,4,2,7,1,13,4,4,0,0,46,14,8,60,21,8,4,2,2,15,2,0,71,41,30,2.53,22380,16689,48.95,35.43 +SACRAMENTO SUBURBAN WATER DISTRICT,193126,43047,97872,17684,834,20602,624,12464,22.29,50.68,9.16,0.43,10.67,0.32,6.45,190984,33399,157585,17.49,72505,3817,3001,3069,2884,3205,3100,3337,2893,2342,5541,6792,10037,6480,4342,5488,6177,12771,14878,12333,27649,22370,10822,23467,7204,2837,12037,2087,1160,37001,21072,10274,2.635470822506937,73746.51448559026,35321.17943972356,41.88,19.68 +SAN JUAN WATER DISTRICT,30122,3409,21349,831,287,2762,17,1467,11.32,70.87,2.76,0.95,9.17,0.06,4.87,30014,1718,28297,5.72,10750,389,168,100,275,128,160,111,133,127,472,684,984,854,876,1032,4256,932,658,1156,1591,2141,1730,6210,1754,724,2883,528,357,1658,726,339,2.7838582261197615,160696.10105741228,72978.42336271124,27.98,13.21 +SCWA - ARDEN PARK VISTA,8086,990,6016,270,12,396,8,395,12.24,74.4,3.33,0.15,4.9,0.1,4.88,8038,523,7515,6.51,3303,79,36,48,77,65,38,18,49,162,139,187,253,465,208,416,1065,241,330,326,571,579,673,1823,520,112,673,76,23,807,384,225,2.424845516612799,139081.65196802403,84548.46138496802,29.69,10.9 +SCWA - LAGUNA/VINEYARD,145495,27502,38496,16568,246,50411,2220,10052,18.9,26.46,11.39,0.17,34.65,1.53,6.91,145198,14710,130489,10.13,45137,1692,666,742,878,839,1336,850,788,752,2363,3198,6037,5323,5057,6578,8038,3978,4565,5561,8543,11598,10380,24581,7232,2916,7878,861,471,12677,6368,3337,3.2074469367308294,114494.03008793297,41415.71495309661,32.04,14.9 +SCWA MATHER-SUNRISE,18249,2708,8114,1553,23,4507,164,1180,14.84,44.47,8.51,0.12,24.7,0.9,6.47,18211,1005,17206,5.52,5503,228,35,97,57,68,39,12,20,36,189,320,533,645,755,1003,1469,416,174,509,590,1042,1399,3756,881,266,855,60,43,893,318,167,3.296327128817464,147818.00762610507,47448.3691116171,22.89,8.66 +SEQUOIA WATER ASSOC,0,0,0,0,0,0,0,0,44.6,45.84,0,0,5.93,0,3.63,0,0,0,15.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.49,68248,38950,24.49,14.65 +SOUTHWEST TRACT W M D [SWS],174,29,42,24,3,75,1,0,16.58,24.48,13.69,1.55,43.11,0.6,0,174,38,136,21.83,57,1,2,7,0,7,0,0,10,12,3,2,5,0,1,2,4,10,29,6,39,10,1,3,1,0,8,0,0,45,29,7,3.04,45671,36348,52.53,12.4 +SPINDRIFT MARINA,0,0,0,0,0,0,0,0,3.9,89.23,3.23,0,0,0,3.63,0,0,0,35.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.7899999999999998,38125,33103,28.02,23.19 +TOKAY PARK WATER CO,652,214,134,37,0,239,0,28,32.8,20.55,5.61,0,36.69,0,4.35,652,113,539,17.29,173,2,2,3,21,0,0,13,13,10,18,27,36,14,4,10,0,27,36,45,64,81,18,81,38,11,44,0,0,48,32,12,3.7579731008260437,62802.23785121953,19400.04804878149,40.57,13.58 +TUNNEL TRAILER PARK,0,0,0,0,0,0,0,0,49.74,34.94,0,0,4.65,0,10.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.95,153092,42507,20.3,0 +"VIEIRA'S RESORT, INC",4,2,2,0,0,0,0,0,41.43,52.47,0,0,4.55,0,1.56,4,1,3,22.6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0,0,0,0,2.03,51977,40522,40.53,21.84 +WESTERNER MOBILE HOME PARK,32,6,6,9,0,10,0,1,17.59,17.62,28.31,0.55,31.36,0,4.57,31,7,24,23.76,10,1,0,0,0,1,0,0,1,0,2,1,1,2,0,1,0,1,2,3,3,4,2,4,2,1,1,0,0,5,3,2,3.16,59296.00000000001,23437,56.87,29.49 diff --git a/03_data_results/water_system_demographics_sac.gpkg b/03_data_results/water_system_demographics_sac.gpkg index 244bd20..39d2166 100644 Binary files a/03_data_results/water_system_demographics_sac.gpkg and b/03_data_results/water_system_demographics_sac.gpkg differ diff --git a/03_data_results/water_system_demographics_sac_long.csv b/03_data_results/water_system_demographics_sac_long.csv index a448fa1..6f36e36 100644 --- a/03_data_results/water_system_demographics_sac_long.csv +++ b/03_data_results/water_system_demographics_sac_long.csv @@ -1,435 +1,3473 @@ -WATER_SY_1,metric,value -HOOD WATER MAINTENCE DIST [SWS],Asian,0 -HOOD WATER MAINTENCE DIST [SWS],Black / African American,0 -HOOD WATER MAINTENCE DIST [SWS],Hispanic / Latino,69.19 -HOOD WATER MAINTENCE DIST [SWS],Native American,0 -HOOD WATER MAINTENCE DIST [SWS],Other / Multiple,2.1 -HOOD WATER MAINTENCE DIST [SWS],Pacific Islander,0 -HOOD WATER MAINTENCE DIST [SWS],White,28.71 -MC CLELLAN MHP,Asian,15.91 -MC CLELLAN MHP,Black / African American,24.01 -MC CLELLAN MHP,Hispanic / Latino,19.27 -MC CLELLAN MHP,Native American,0 -MC CLELLAN MHP,Other / Multiple,0.62 -MC CLELLAN MHP,Pacific Islander,0 -MC CLELLAN MHP,White,40.19 -MAGNOLIA MUTUAL WATER,Asian,5.93 -MAGNOLIA MUTUAL WATER,Black / African American,0 -MAGNOLIA MUTUAL WATER,Hispanic / Latino,44.6 -MAGNOLIA MUTUAL WATER,Native American,0 -MAGNOLIA MUTUAL WATER,Other / Multiple,3.63 -MAGNOLIA MUTUAL WATER,Pacific Islander,0 -MAGNOLIA MUTUAL WATER,White,45.84 -KORTHS PIRATES LAIR,Asian,0 -KORTHS PIRATES LAIR,Black / African American,3.23 -KORTHS PIRATES LAIR,Hispanic / Latino,3.9 -KORTHS PIRATES LAIR,Native American,0 -KORTHS PIRATES LAIR,Other / Multiple,3.63 -KORTHS PIRATES LAIR,Pacific Islander,0 -KORTHS PIRATES LAIR,White,89.23 -EL DORADO MOBILE HOME PARK,Asian,13.27 -EL DORADO MOBILE HOME PARK,Black / African American,10.48 -EL DORADO MOBILE HOME PARK,Hispanic / Latino,60.26 -EL DORADO MOBILE HOME PARK,Native American,0 -EL DORADO MOBILE HOME PARK,Other / Multiple,8.19 -EL DORADO MOBILE HOME PARK,Pacific Islander,0 -EL DORADO MOBILE HOME PARK,White,7.8 -RIVER'S EDGE MARINA & RESORT,Asian,0 -RIVER'S EDGE MARINA & RESORT,Black / African American,3.23 -RIVER'S EDGE MARINA & RESORT,Hispanic / Latino,3.9 -RIVER'S EDGE MARINA & RESORT,Native American,0 -RIVER'S EDGE MARINA & RESORT,Other / Multiple,3.63 -RIVER'S EDGE MARINA & RESORT,Pacific Islander,0 -RIVER'S EDGE MARINA & RESORT,White,89.23 -LAGUNA VILLAGE RV PARK,Asian,52.62 -LAGUNA VILLAGE RV PARK,Black / African American,7.28 -LAGUNA VILLAGE RV PARK,Hispanic / Latino,12.79 -LAGUNA VILLAGE RV PARK,Native American,0 -LAGUNA VILLAGE RV PARK,Other / Multiple,10.45 -LAGUNA VILLAGE RV PARK,Pacific Islander,8.38 -LAGUNA VILLAGE RV PARK,White,8.48 -SPINDRIFT MARINA,Asian,0 -SPINDRIFT MARINA,Black / African American,3.23 -SPINDRIFT MARINA,Hispanic / Latino,3.9 -SPINDRIFT MARINA,Native American,0 -SPINDRIFT MARINA,Other / Multiple,3.63 -SPINDRIFT MARINA,Pacific Islander,0 -SPINDRIFT MARINA,White,89.23 -SAC CITY MOBILE HOME COMMUNITY LP,Asian,53.57 -SAC CITY MOBILE HOME COMMUNITY LP,Black / African American,3.27 -SAC CITY MOBILE HOME COMMUNITY LP,Hispanic / Latino,35.66 -SAC CITY MOBILE HOME COMMUNITY LP,Native American,0 -SAC CITY MOBILE HOME COMMUNITY LP,Other / Multiple,0 -SAC CITY MOBILE HOME COMMUNITY LP,Pacific Islander,0 -SAC CITY MOBILE HOME COMMUNITY LP,White,7.5 -ORANGE VALE WATER COMPANY,Asian,3.64 -ORANGE VALE WATER COMPANY,Black / African American,1.39 -ORANGE VALE WATER COMPANY,Hispanic / Latino,15.28 -ORANGE VALE WATER COMPANY,Native American,1.04 -ORANGE VALE WATER COMPANY,Other / Multiple,7.37 -ORANGE VALE WATER COMPANY,Pacific Islander,0.49 -ORANGE VALE WATER COMPANY,White,70.79 -GOLDEN STATE WATER CO. - CORDOVA,Asian,12.57 -GOLDEN STATE WATER CO. - CORDOVA,Black / African American,8.28 -GOLDEN STATE WATER CO. - CORDOVA,Hispanic / Latino,18.72 -GOLDEN STATE WATER CO. - CORDOVA,Native American,0.48 -GOLDEN STATE WATER CO. - CORDOVA,Other / Multiple,5.43 -GOLDEN STATE WATER CO. - CORDOVA,Pacific Islander,0.39 -GOLDEN STATE WATER CO. - CORDOVA,White,54.13 -HOLIDAY MOBILE VILLAGE,Asian,32.49 -HOLIDAY MOBILE VILLAGE,Black / African American,7.1 -HOLIDAY MOBILE VILLAGE,Hispanic / Latino,38.66 -HOLIDAY MOBILE VILLAGE,Native American,0 -HOLIDAY MOBILE VILLAGE,Other / Multiple,6.64 -HOLIDAY MOBILE VILLAGE,Pacific Islander,0 -HOLIDAY MOBILE VILLAGE,White,15.12 -SOUTHWEST TRACT W M D [SWS],Asian,43.11 -SOUTHWEST TRACT W M D [SWS],Black / African American,13.69 -SOUTHWEST TRACT W M D [SWS],Hispanic / Latino,16.58 -SOUTHWEST TRACT W M D [SWS],Native American,1.55 -SOUTHWEST TRACT W M D [SWS],Other / Multiple,0 -SOUTHWEST TRACT W M D [SWS],Pacific Islander,0.6 -SOUTHWEST TRACT W M D [SWS],White,24.48 -CARMICHAEL WATER DISTRICT,Asian,8.47 -CARMICHAEL WATER DISTRICT,Black / African American,5.68 -CARMICHAEL WATER DISTRICT,Hispanic / Latino,15.78 -CARMICHAEL WATER DISTRICT,Native American,0.17 -CARMICHAEL WATER DISTRICT,Other / Multiple,5.39 -CARMICHAEL WATER DISTRICT,Pacific Islander,0.75 -CARMICHAEL WATER DISTRICT,White,63.76 -SCWA - ARDEN PARK VISTA,Asian,4.9 -SCWA - ARDEN PARK VISTA,Black / African American,3.33 -SCWA - ARDEN PARK VISTA,Hispanic / Latino,12.24 -SCWA - ARDEN PARK VISTA,Native American,0.15 -SCWA - ARDEN PARK VISTA,Other / Multiple,4.88 -SCWA - ARDEN PARK VISTA,Pacific Islander,0.1 -SCWA - ARDEN PARK VISTA,White,74.4 -SCWA - LAGUNA/VINEYARD,Asian,34.65 -SCWA - LAGUNA/VINEYARD,Black / African American,11.39 -SCWA - LAGUNA/VINEYARD,Hispanic / Latino,18.9 -SCWA - LAGUNA/VINEYARD,Native American,0.17 -SCWA - LAGUNA/VINEYARD,Other / Multiple,6.91 -SCWA - LAGUNA/VINEYARD,Pacific Islander,1.53 -SCWA - LAGUNA/VINEYARD,White,26.46 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Asian,4.5 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Black / African American,16.82 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Hispanic / Latino,25.74 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Native American,2.97 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Other / Multiple,10.66 -RIO COSUMNES CORRECTIONAL CENTER [SWS],Pacific Islander,1.81 -RIO COSUMNES CORRECTIONAL CENTER [SWS],White,37.49 -SCWA MATHER-SUNRISE,Asian,24.7 -SCWA MATHER-SUNRISE,Black / African American,8.51 -SCWA MATHER-SUNRISE,Hispanic / Latino,14.84 -SCWA MATHER-SUNRISE,Native American,0.12 -SCWA MATHER-SUNRISE,Other / Multiple,6.47 -SCWA MATHER-SUNRISE,Pacific Islander,0.9 -SCWA MATHER-SUNRISE,White,44.47 -TUNNEL TRAILER PARK,Asian,4.65 -TUNNEL TRAILER PARK,Black / African American,0 -TUNNEL TRAILER PARK,Hispanic / Latino,49.74 -TUNNEL TRAILER PARK,Native American,0 -TUNNEL TRAILER PARK,Other / Multiple,10.67 -TUNNEL TRAILER PARK,Pacific Islander,0 -TUNNEL TRAILER PARK,White,34.94 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Asian,2.93 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Black / African American,0.45 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Hispanic / Latino,24.93 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Native American,0 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Other / Multiple,10.05 -IMPERIAL MANOR MOBILEHOME COMMUNITY,Pacific Islander,0 -IMPERIAL MANOR MOBILEHOME COMMUNITY,White,61.63 -CALAM - ISLETON,Asian,4.55 -CALAM - ISLETON,Black / African American,0 -CALAM - ISLETON,Hispanic / Latino,42.06 -CALAM - ISLETON,Native American,0 -CALAM - ISLETON,Other / Multiple,2.25 -CALAM - ISLETON,Pacific Islander,0 -CALAM - ISLETON,White,51.14 -"FOLSOM, CITY OF - ASHLAND",Asian,3.26 -"FOLSOM, CITY OF - ASHLAND",Black / African American,1.12 -"FOLSOM, CITY OF - ASHLAND",Hispanic / Latino,8.26 -"FOLSOM, CITY OF - ASHLAND",Native American,0.03 -"FOLSOM, CITY OF - ASHLAND",Other / Multiple,10.99 -"FOLSOM, CITY OF - ASHLAND",Pacific Islander,0.02 -"FOLSOM, CITY OF - ASHLAND",White,76.32 -LOCKE WATER WORKS CO [SWS],Asian,5.93 -LOCKE WATER WORKS CO [SWS],Black / African American,0 -LOCKE WATER WORKS CO [SWS],Hispanic / Latino,44.6 -LOCKE WATER WORKS CO [SWS],Native American,0 -LOCKE WATER WORKS CO [SWS],Other / Multiple,3.63 -LOCKE WATER WORKS CO [SWS],Pacific Islander,0 -LOCKE WATER WORKS CO [SWS],White,45.84 -DEL PASO MANOR COUNTY WATER DI,Asian,2.13 -DEL PASO MANOR COUNTY WATER DI,Black / African American,6.97 -DEL PASO MANOR COUNTY WATER DI,Hispanic / Latino,12.28 -DEL PASO MANOR COUNTY WATER DI,Native American,0.26 -DEL PASO MANOR COUNTY WATER DI,Other / Multiple,6.84 -DEL PASO MANOR COUNTY WATER DI,Pacific Islander,0.56 -DEL PASO MANOR COUNTY WATER DI,White,70.95 -EAST WALNUT GROVE [SWS],Asian,5.93 -EAST WALNUT GROVE [SWS],Black / African American,0 -EAST WALNUT GROVE [SWS],Hispanic / Latino,44.6 -EAST WALNUT GROVE [SWS],Native American,0 -EAST WALNUT GROVE [SWS],Other / Multiple,3.63 -EAST WALNUT GROVE [SWS],Pacific Islander,0 -EAST WALNUT GROVE [SWS],White,45.84 -FOLSOM STATE PRISON,Asian,1.97 -FOLSOM STATE PRISON,Black / African American,39.31 -FOLSOM STATE PRISON,Hispanic / Latino,35.55 -FOLSOM STATE PRISON,Native American,1.6 -FOLSOM STATE PRISON,Other / Multiple,2.17 -FOLSOM STATE PRISON,Pacific Islander,0.96 -FOLSOM STATE PRISON,White,18.43 -CALAM - ARDEN,Asian,10.7 -CALAM - ARDEN,Black / African American,19.55 -CALAM - ARDEN,Hispanic / Latino,33.95 -CALAM - ARDEN,Native American,0.69 -CALAM - ARDEN,Other / Multiple,10.87 -CALAM - ARDEN,Pacific Islander,0.58 -CALAM - ARDEN,White,23.65 -EDGEWATER MOBILE HOME PARK,Asian,0 -EDGEWATER MOBILE HOME PARK,Black / African American,3.23 -EDGEWATER MOBILE HOME PARK,Hispanic / Latino,3.9 -EDGEWATER MOBILE HOME PARK,Native American,0 -EDGEWATER MOBILE HOME PARK,Other / Multiple,3.63 -EDGEWATER MOBILE HOME PARK,Pacific Islander,0 -EDGEWATER MOBILE HOME PARK,White,89.23 -CALAM - LINCOLN OAKS,Asian,6.31 -CALAM - LINCOLN OAKS,Black / African American,3.46 -CALAM - LINCOLN OAKS,Hispanic / Latino,21.1 -CALAM - LINCOLN OAKS,Native American,0.33 -CALAM - LINCOLN OAKS,Other / Multiple,6.31 -CALAM - LINCOLN OAKS,Pacific Islander,0.67 -CALAM - LINCOLN OAKS,White,61.82 -"VIEIRA'S RESORT, INC",Asian,4.55 -"VIEIRA'S RESORT, INC",Black / African American,0 -"VIEIRA'S RESORT, INC",Hispanic / Latino,41.43 -"VIEIRA'S RESORT, INC",Native American,0 -"VIEIRA'S RESORT, INC",Other / Multiple,1.56 -"VIEIRA'S RESORT, INC",Pacific Islander,0 -"VIEIRA'S RESORT, INC",White,52.47 -FLORIN COUNTY WATER DISTRICT,Asian,27.56 -FLORIN COUNTY WATER DISTRICT,Black / African American,14.01 -FLORIN COUNTY WATER DISTRICT,Hispanic / Latino,29.78 -FLORIN COUNTY WATER DISTRICT,Native American,0.07 -FLORIN COUNTY WATER DISTRICT,Other / Multiple,4.32 -FLORIN COUNTY WATER DISTRICT,Pacific Islander,8.7 -FLORIN COUNTY WATER DISTRICT,White,15.56 -WESTERNER MOBILE HOME PARK,Asian,31.36 -WESTERNER MOBILE HOME PARK,Black / African American,28.31 -WESTERNER MOBILE HOME PARK,Hispanic / Latino,17.59 -WESTERNER MOBILE HOME PARK,Native American,0.55 -WESTERNER MOBILE HOME PARK,Other / Multiple,4.57 -WESTERNER MOBILE HOME PARK,Pacific Islander,0 -WESTERNER MOBILE HOME PARK,White,17.62 -EL DORADO WEST MHP,Asian,13.27 -EL DORADO WEST MHP,Black / African American,10.48 -EL DORADO WEST MHP,Hispanic / Latino,60.26 -EL DORADO WEST MHP,Native American,0 -EL DORADO WEST MHP,Other / Multiple,8.19 -EL DORADO WEST MHP,Pacific Islander,0 -EL DORADO WEST MHP,White,7.8 -TOKAY PARK WATER CO,Asian,36.69 -TOKAY PARK WATER CO,Black / African American,5.61 -TOKAY PARK WATER CO,Hispanic / Latino,32.8 -TOKAY PARK WATER CO,Native American,0 -TOKAY PARK WATER CO,Other / Multiple,4.35 -TOKAY PARK WATER CO,Pacific Islander,0 -TOKAY PARK WATER CO,White,20.55 -LAGUNA DEL SOL INC,Asian,1.46 -LAGUNA DEL SOL INC,Black / African American,0 -LAGUNA DEL SOL INC,Hispanic / Latino,21.55 -LAGUNA DEL SOL INC,Native American,0.67 -LAGUNA DEL SOL INC,Other / Multiple,1.12 -LAGUNA DEL SOL INC,Pacific Islander,0 -LAGUNA DEL SOL INC,White,75.2 -OLYMPIA MOBILODGE,Asian,34.95 -OLYMPIA MOBILODGE,Black / African American,6.3 -OLYMPIA MOBILODGE,Hispanic / Latino,24.12 -OLYMPIA MOBILODGE,Native American,0 -OLYMPIA MOBILODGE,Other / Multiple,1.08 -OLYMPIA MOBILODGE,Pacific Islander,5.53 -OLYMPIA MOBILODGE,White,28.03 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Asian,13.54 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Black / African American,4.91 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Hispanic / Latino,26.02 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Native American,0 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Other / Multiple,11.32 -GOLDEN STATE WATER CO - ARDEN WATER SERV,Pacific Islander,0.16 -GOLDEN STATE WATER CO - ARDEN WATER SERV,White,44.04 -ELEVEN OAKS MOBILE HOME COMMUNITY,Asian,15.91 -ELEVEN OAKS MOBILE HOME COMMUNITY,Black / African American,24.01 -ELEVEN OAKS MOBILE HOME COMMUNITY,Hispanic / Latino,19.27 -ELEVEN OAKS MOBILE HOME COMMUNITY,Native American,0 -ELEVEN OAKS MOBILE HOME COMMUNITY,Other / Multiple,0.62 -ELEVEN OAKS MOBILE HOME COMMUNITY,Pacific Islander,0 -ELEVEN OAKS MOBILE HOME COMMUNITY,White,40.19 -CALAM - ANTELOPE,Asian,8.9 -CALAM - ANTELOPE,Black / African American,9.66 -CALAM - ANTELOPE,Hispanic / Latino,15.84 -CALAM - ANTELOPE,Native American,0.34 -CALAM - ANTELOPE,Other / Multiple,6.29 -CALAM - ANTELOPE,Pacific Islander,0.23 -CALAM - ANTELOPE,White,58.74 -CALIFORNIA STATE FAIR,Asian,9.1 -CALIFORNIA STATE FAIR,Black / African American,17.13 -CALIFORNIA STATE FAIR,Hispanic / Latino,14.68 -CALIFORNIA STATE FAIR,Native American,0 -CALIFORNIA STATE FAIR,Other / Multiple,9.85 -CALIFORNIA STATE FAIR,Pacific Islander,0 -CALIFORNIA STATE FAIR,White,49.25 -PLANTATION MOBILE HOME PARK,Asian,32.49 -PLANTATION MOBILE HOME PARK,Black / African American,7.1 -PLANTATION MOBILE HOME PARK,Hispanic / Latino,38.66 -PLANTATION MOBILE HOME PARK,Native American,0 -PLANTATION MOBILE HOME PARK,Other / Multiple,6.64 -PLANTATION MOBILE HOME PARK,Pacific Islander,0 -PLANTATION MOBILE HOME PARK,White,15.12 -CALAM - PARKWAY,Asian,32.79 -CALAM - PARKWAY,Black / African American,11.88 -CALAM - PARKWAY,Hispanic / Latino,31.83 -CALAM - PARKWAY,Native American,0.04 -CALAM - PARKWAY,Other / Multiple,5.88 -CALAM - PARKWAY,Pacific Islander,2.36 -CALAM - PARKWAY,White,15.21 -CITRUS HEIGHTS WATER DISTRICT,Asian,4.17 -CITRUS HEIGHTS WATER DISTRICT,Black / African American,3.04 -CITRUS HEIGHTS WATER DISTRICT,Hispanic / Latino,17.96 -CITRUS HEIGHTS WATER DISTRICT,Native American,0.23 -CITRUS HEIGHTS WATER DISTRICT,Other / Multiple,4.62 -CITRUS HEIGHTS WATER DISTRICT,Pacific Islander,0.1 -CITRUS HEIGHTS WATER DISTRICT,White,69.87 -SEQUOIA WATER ASSOC,Asian,5.93 -SEQUOIA WATER ASSOC,Black / African American,0 -SEQUOIA WATER ASSOC,Hispanic / Latino,44.6 -SEQUOIA WATER ASSOC,Native American,0 -SEQUOIA WATER ASSOC,Other / Multiple,3.63 -SEQUOIA WATER ASSOC,Pacific Islander,0 -SEQUOIA WATER ASSOC,White,45.84 -FAIR OAKS WATER DISTRICT,Asian,3.81 -FAIR OAKS WATER DISTRICT,Black / African American,1.97 -FAIR OAKS WATER DISTRICT,Hispanic / Latino,12.93 -FAIR OAKS WATER DISTRICT,Native American,0.26 -FAIR OAKS WATER DISTRICT,Other / Multiple,5.87 -FAIR OAKS WATER DISTRICT,Pacific Islander,0.03 -FAIR OAKS WATER DISTRICT,White,75.13 -RANCHO MARINA,Asian,0 -RANCHO MARINA,Black / African American,3.23 -RANCHO MARINA,Hispanic / Latino,3.9 -RANCHO MARINA,Native American,0 -RANCHO MARINA,Other / Multiple,3.63 -RANCHO MARINA,Pacific Islander,0 -RANCHO MARINA,White,89.23 -FREEPORT MARINA,Asian,0 -FREEPORT MARINA,Black / African American,0 -FREEPORT MARINA,Hispanic / Latino,69.19 -FREEPORT MARINA,Native American,0 -FREEPORT MARINA,Other / Multiple,2.1 -FREEPORT MARINA,Pacific Islander,0 -FREEPORT MARINA,White,28.71 -HAPPY HARBOR (SWS),Asian,0 -HAPPY HARBOR (SWS),Black / African American,3.23 -HAPPY HARBOR (SWS),Hispanic / Latino,3.9 -HAPPY HARBOR (SWS),Native American,0 -HAPPY HARBOR (SWS),Other / Multiple,3.63 -HAPPY HARBOR (SWS),Pacific Islander,0 -HAPPY HARBOR (SWS),White,89.23 -"FOLSOM, CITY OF - MAIN",Asian,20.71 -"FOLSOM, CITY OF - MAIN",Black / African American,2.71 -"FOLSOM, CITY OF - MAIN",Hispanic / Latino,13.5 -"FOLSOM, CITY OF - MAIN",Native American,0.17 -"FOLSOM, CITY OF - MAIN",Other / Multiple,6.24 -"FOLSOM, CITY OF - MAIN",Pacific Islander,0.28 -"FOLSOM, CITY OF - MAIN",White,56.39 -RANCHO MURIETA COMMUNITY SERVI,Asian,5.8 -RANCHO MURIETA COMMUNITY SERVI,Black / African American,3.71 -RANCHO MURIETA COMMUNITY SERVI,Hispanic / Latino,20.42 -RANCHO MURIETA COMMUNITY SERVI,Native American,0.21 -RANCHO MURIETA COMMUNITY SERVI,Other / Multiple,3.26 -RANCHO MURIETA COMMUNITY SERVI,Pacific Islander,0 -RANCHO MURIETA COMMUNITY SERVI,White,66.59 -CAL AM FRUITRIDGE VISTA,Asian,18.03 -CAL AM FRUITRIDGE VISTA,Black / African American,11.78 -CAL AM FRUITRIDGE VISTA,Hispanic / Latino,48.4 -CAL AM FRUITRIDGE VISTA,Native American,0.54 -CAL AM FRUITRIDGE VISTA,Other / Multiple,4.69 -CAL AM FRUITRIDGE VISTA,Pacific Islander,1.06 -CAL AM FRUITRIDGE VISTA,White,15.5 -B & W RESORT MARINA,Asian,4.55 -B & W RESORT MARINA,Black / African American,0 -B & W RESORT MARINA,Hispanic / Latino,41.43 -B & W RESORT MARINA,Native American,0 -B & W RESORT MARINA,Other / Multiple,1.56 -B & W RESORT MARINA,Pacific Islander,0 -B & W RESORT MARINA,White,52.47 -CALAM - SUBURBAN ROSEMONT,Asian,11.93 -CALAM - SUBURBAN ROSEMONT,Black / African American,13.34 -CALAM - SUBURBAN ROSEMONT,Hispanic / Latino,23.82 -CALAM - SUBURBAN ROSEMONT,Native American,0.16 -CALAM - SUBURBAN ROSEMONT,Other / Multiple,6.81 -CALAM - SUBURBAN ROSEMONT,Pacific Islander,0.66 -CALAM - SUBURBAN ROSEMONT,White,43.29 -SAN JUAN WATER DISTRICT,Asian,9.17 -SAN JUAN WATER DISTRICT,Black / African American,2.76 -SAN JUAN WATER DISTRICT,Hispanic / Latino,11.32 -SAN JUAN WATER DISTRICT,Native American,0.95 -SAN JUAN WATER DISTRICT,Other / Multiple,4.87 -SAN JUAN WATER DISTRICT,Pacific Islander,0.06 -SAN JUAN WATER DISTRICT,White,70.87 -ELK GROVE WATER SERVICE,Asian,20.96 -ELK GROVE WATER SERVICE,Black / African American,7.53 -ELK GROVE WATER SERVICE,Hispanic / Latino,17.95 -ELK GROVE WATER SERVICE,Native American,0.16 -ELK GROVE WATER SERVICE,Other / Multiple,6.65 -ELK GROVE WATER SERVICE,Pacific Islander,0.91 -ELK GROVE WATER SERVICE,White,45.84 -DELTA CROSSING MHP,Asian,0 -DELTA CROSSING MHP,Black / African American,0 -DELTA CROSSING MHP,Hispanic / Latino,69.19 -DELTA CROSSING MHP,Native American,0 -DELTA CROSSING MHP,Other / Multiple,2.1 -DELTA CROSSING MHP,Pacific Islander,0 -DELTA CROSSING MHP,White,28.71 -"GALT, CITY OF",Asian,4.06 -"GALT, CITY OF",Black / African American,2.42 -"GALT, CITY OF",Hispanic / Latino,43.34 -"GALT, CITY OF",Native American,0.1 -"GALT, CITY OF",Other / Multiple,3.67 -"GALT, CITY OF",Pacific Islander,0.09 -"GALT, CITY OF",White,46.31 -LINCOLN CHAN-HOME RANCH,Asian,5.93 -LINCOLN CHAN-HOME RANCH,Black / African American,0 -LINCOLN CHAN-HOME RANCH,Hispanic / Latino,44.6 -LINCOLN CHAN-HOME RANCH,Native American,0 -LINCOLN CHAN-HOME RANCH,Other / Multiple,3.63 -LINCOLN CHAN-HOME RANCH,Pacific Islander,0 -LINCOLN CHAN-HOME RANCH,White,45.84 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Asian,6.46 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Black / African American,2.85 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Hispanic / Latino,21.85 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Native American,0.14 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Other / Multiple,4.33 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,Pacific Islander,0.18 -RIO LINDA/ELVERTA COMMUNITY WATER DIST,White,64.19 -SACRAMENTO SUBURBAN WATER DISTRICT,Asian,10.67 -SACRAMENTO SUBURBAN WATER DISTRICT,Black / African American,9.16 -SACRAMENTO SUBURBAN WATER DISTRICT,Hispanic / Latino,22.29 -SACRAMENTO SUBURBAN WATER DISTRICT,Native American,0.43 -SACRAMENTO SUBURBAN WATER DISTRICT,Other / Multiple,6.45 -SACRAMENTO SUBURBAN WATER DISTRICT,Pacific Islander,0.32 -SACRAMENTO SUBURBAN WATER DISTRICT,White,50.68 -CALAM - WALNUT GROVE,Asian,5.93 -CALAM - WALNUT GROVE,Black / African American,0 -CALAM - WALNUT GROVE,Hispanic / Latino,44.6 -CALAM - WALNUT GROVE,Native American,0 -CALAM - WALNUT GROVE,Other / Multiple,3.63 -CALAM - WALNUT GROVE,Pacific Islander,0 -CALAM - WALNUT GROVE,White,45.84 -CITY OF SACRAMENTO MAIN,Asian,19.1 -CITY OF SACRAMENTO MAIN,Black / African American,12.02 -CITY OF SACRAMENTO MAIN,Hispanic / Latino,29.29 -CITY OF SACRAMENTO MAIN,Native American,0.24 -CITY OF SACRAMENTO MAIN,Other / Multiple,6.65 -CITY OF SACRAMENTO MAIN,Pacific Islander,1.79 -CITY OF SACRAMENTO MAIN,White,30.9 +WATER_SY_1,variable,type,group_type,value +B & W RESORT MARINA,Population Total,Count,Population,0 +B & W RESORT MARINA,Hispanic / Latino,Count,Population,0 +B & W RESORT MARINA,White,Count,Population,0 +B & W RESORT MARINA,Black-/ African American,Count,Population,0 +B & W RESORT MARINA,Native American,Count,Population,0 +B & W RESORT MARINA,Asian,Count,Population,0 +B & W RESORT MARINA,Pacific Islander,Count,Population,0 +B & W RESORT MARINA,Other / Multiple,Count,Population,0 +B & W RESORT MARINA,Hispanic / Latino,Percent,Population,41.43 +B & W RESORT MARINA,White,Percent,Population,52.47 +B & W RESORT MARINA,Black-/ African American,Percent,Population,0 +B & W RESORT MARINA,Native American,Percent,Population,0 +B & W RESORT MARINA,Asian,Percent,Population,4.55 +B & W RESORT MARINA,Pacific Islander,Percent,Population,0 +B & W RESORT MARINA,Other / Multiple,Percent,Population,1.56 +B & W RESORT MARINA,Poverty Total Assessed,Count,Population,0 +B & W RESORT MARINA,Poverty Below,Count,Population,0 +B & W RESORT MARINA,Poverty Above,Count,Population,0 +B & W RESORT MARINA,Poverty Rate,Percent,Population,22.6 +B & W RESORT MARINA,Households Total,Count,Households,0 +B & W RESORT MARINA,Income Below 10k,Count,Households,0 +B & W RESORT MARINA,Income 10k-15k,Count,Households,0 +B & W RESORT MARINA,Income 15k-20k,Count,Households,0 +B & W RESORT MARINA,Income 20k-25k,Count,Households,0 +B & W RESORT MARINA,Income 25k-30k,Count,Households,0 +B & W RESORT MARINA,Income 30k-35k,Count,Households,0 +B & W RESORT MARINA,Income 35k-40k,Count,Households,0 +B & W RESORT MARINA,Income 40k-45k,Count,Households,0 +B & W RESORT MARINA,Income 45k-50k,Count,Households,0 +B & W RESORT MARINA,Income 50k-60k,Count,Households,0 +B & W RESORT MARINA,Income 60k-75k,Count,Households,0 +B & W RESORT MARINA,Income 75k-100k,Count,Households,0 +B & W RESORT MARINA,Income 100k-125k,Count,Households,0 +B & W RESORT MARINA,Income 125k-150k,Count,Households,0 +B & W RESORT MARINA,Income 150k-200k,Count,Households,0 +B & W RESORT MARINA,Income Above 200k,Count,Households,0 +B & W RESORT MARINA,Income 0-25k,Count,Households,0 +B & W RESORT MARINA,Income 25k-50k,Count,Households,0 +B & W RESORT MARINA,Income 50k-75k,Count,Households,0 +B & W RESORT MARINA,Income 0-50k,Count,Households,0 +B & W RESORT MARINA,Income 50k-100k,Count,Households,0 +B & W RESORT MARINA,Income 100k-150k,Count,Households,0 +B & W RESORT MARINA,Mortgage Total,Count,Households,0 +B & W RESORT MARINA,Mortgage Over 30% Income,Count,Households,0 +B & W RESORT MARINA,Mortgage Over 50% Income,Count,Households,0 +B & W RESORT MARINA,No Mortgage Total,Count,Households,0 +B & W RESORT MARINA,No Mortgage Over 30% Income,Count,Households,0 +B & W RESORT MARINA,No Mortgage Over 50% Income,Count,Households,0 +B & W RESORT MARINA,Rent Total,Count,Households,0 +B & W RESORT MARINA,Rent Over 30% Income,Count,Households,0 +B & W RESORT MARINA,Rent Over 50% Income,Count,Households,0 +B & W RESORT MARINA,Average Household Size,Hh Weighted,Household Weighted,2.03 +B & W RESORT MARINA,Median Household Income,Hh Weighted,Household Weighted,51977 +B & W RESORT MARINA,Per Capita Income,Pop Weighted,Population Weighted,40522 +B & W RESORT MARINA,Housing Costs Over 30% Income,Percent,Households,40.53 +B & W RESORT MARINA,Housing Costs Over 50% Income,Percent,Households,21.84 +CAL AM FRUITRIDGE VISTA,Population Total,Count,Population,22603 +CAL AM FRUITRIDGE VISTA,Hispanic / Latino,Count,Population,10939 +CAL AM FRUITRIDGE VISTA,White,Count,Population,3504 +CAL AM FRUITRIDGE VISTA,Black-/ African American,Count,Population,2663 +CAL AM FRUITRIDGE VISTA,Native American,Count,Population,121 +CAL AM FRUITRIDGE VISTA,Asian,Count,Population,4075 +CAL AM FRUITRIDGE VISTA,Pacific Islander,Count,Population,240 +CAL AM FRUITRIDGE VISTA,Other / Multiple,Count,Population,1060 +CAL AM FRUITRIDGE VISTA,Hispanic / Latino,Percent,Population,48.4 +CAL AM FRUITRIDGE VISTA,White,Percent,Population,15.5 +CAL AM FRUITRIDGE VISTA,Black-/ African American,Percent,Population,11.78 +CAL AM FRUITRIDGE VISTA,Native American,Percent,Population,0.54 +CAL AM FRUITRIDGE VISTA,Asian,Percent,Population,18.03 +CAL AM FRUITRIDGE VISTA,Pacific Islander,Percent,Population,1.06 +CAL AM FRUITRIDGE VISTA,Other / Multiple,Percent,Population,4.69 +CAL AM FRUITRIDGE VISTA,Poverty Total Assessed,Count,Population,22556 +CAL AM FRUITRIDGE VISTA,Poverty Below,Count,Population,6010 +CAL AM FRUITRIDGE VISTA,Poverty Above,Count,Population,16546 +CAL AM FRUITRIDGE VISTA,Poverty Rate,Percent,Population,26.64 +CAL AM FRUITRIDGE VISTA,Households Total,Count,Households,6900 +CAL AM FRUITRIDGE VISTA,Income Below 10k,Count,Households,354 +CAL AM FRUITRIDGE VISTA,Income 10k-15k,Count,Households,339 +CAL AM FRUITRIDGE VISTA,Income 15k-20k,Count,Households,521 +CAL AM FRUITRIDGE VISTA,Income 20k-25k,Count,Households,263 +CAL AM FRUITRIDGE VISTA,Income 25k-30k,Count,Households,367 +CAL AM FRUITRIDGE VISTA,Income 30k-35k,Count,Households,302 +CAL AM FRUITRIDGE VISTA,Income 35k-40k,Count,Households,359 +CAL AM FRUITRIDGE VISTA,Income 40k-45k,Count,Households,355 +CAL AM FRUITRIDGE VISTA,Income 45k-50k,Count,Households,565 +CAL AM FRUITRIDGE VISTA,Income 50k-60k,Count,Households,692 +CAL AM FRUITRIDGE VISTA,Income 60k-75k,Count,Households,876 +CAL AM FRUITRIDGE VISTA,Income 75k-100k,Count,Households,784 +CAL AM FRUITRIDGE VISTA,Income 100k-125k,Count,Households,459 +CAL AM FRUITRIDGE VISTA,Income 125k-150k,Count,Households,235 +CAL AM FRUITRIDGE VISTA,Income 150k-200k,Count,Households,287 +CAL AM FRUITRIDGE VISTA,Income Above 200k,Count,Households,141 +CAL AM FRUITRIDGE VISTA,Income 0-25k,Count,Households,1477 +CAL AM FRUITRIDGE VISTA,Income 25k-50k,Count,Households,1948 +CAL AM FRUITRIDGE VISTA,Income 50k-75k,Count,Households,1569 +CAL AM FRUITRIDGE VISTA,Income 0-50k,Count,Households,3425 +CAL AM FRUITRIDGE VISTA,Income 50k-100k,Count,Households,2352 +CAL AM FRUITRIDGE VISTA,Income 100k-150k,Count,Households,694 +CAL AM FRUITRIDGE VISTA,Mortgage Total,Count,Households,1620 +CAL AM FRUITRIDGE VISTA,Mortgage Over 30% Income,Count,Households,745 +CAL AM FRUITRIDGE VISTA,Mortgage Over 50% Income,Count,Households,345 +CAL AM FRUITRIDGE VISTA,No Mortgage Total,Count,Households,1236 +CAL AM FRUITRIDGE VISTA,No Mortgage Over 30% Income,Count,Households,95 +CAL AM FRUITRIDGE VISTA,No Mortgage Over 50% Income,Count,Households,58 +CAL AM FRUITRIDGE VISTA,Rent Total,Count,Households,4044 +CAL AM FRUITRIDGE VISTA,Rent Over 30% Income,Count,Households,2131 +CAL AM FRUITRIDGE VISTA,Rent Over 50% Income,Count,Households,1059 +CAL AM FRUITRIDGE VISTA,Average Household Size,Hh Weighted,Household Weighted,3.2578059416417346 +CAL AM FRUITRIDGE VISTA,Median Household Income,Hh Weighted,Household Weighted,53040.44113156888 +CAL AM FRUITRIDGE VISTA,Per Capita Income,Pop Weighted,Population Weighted,20519.569076441432 +CAL AM FRUITRIDGE VISTA,Housing Costs Over 30% Income,Percent,Households,43.06 +CAL AM FRUITRIDGE VISTA,Housing Costs Over 50% Income,Percent,Households,21.18 +CALAM - ANTELOPE,Population Total,Count,Population,33120 +CALAM - ANTELOPE,Hispanic / Latino,Count,Population,5245 +CALAM - ANTELOPE,White,Count,Population,19456 +CALAM - ANTELOPE,Black-/ African American,Count,Population,3199 +CALAM - ANTELOPE,Native American,Count,Population,113 +CALAM - ANTELOPE,Asian,Count,Population,2947 +CALAM - ANTELOPE,Pacific Islander,Count,Population,77 +CALAM - ANTELOPE,Other / Multiple,Count,Population,2082 +CALAM - ANTELOPE,Hispanic / Latino,Percent,Population,15.84 +CALAM - ANTELOPE,White,Percent,Population,58.74 +CALAM - ANTELOPE,Black-/ African American,Percent,Population,9.66 +CALAM - ANTELOPE,Native American,Percent,Population,0.34 +CALAM - ANTELOPE,Asian,Percent,Population,8.9 +CALAM - ANTELOPE,Pacific Islander,Percent,Population,0.23 +CALAM - ANTELOPE,Other / Multiple,Percent,Population,6.29 +CALAM - ANTELOPE,Poverty Total Assessed,Count,Population,33034 +CALAM - ANTELOPE,Poverty Below,Count,Population,3389 +CALAM - ANTELOPE,Poverty Above,Count,Population,29645 +CALAM - ANTELOPE,Poverty Rate,Percent,Population,10.26 +CALAM - ANTELOPE,Households Total,Count,Households,10529 +CALAM - ANTELOPE,Income Below 10k,Count,Households,315 +CALAM - ANTELOPE,Income 10k-15k,Count,Households,184 +CALAM - ANTELOPE,Income 15k-20k,Count,Households,101 +CALAM - ANTELOPE,Income 20k-25k,Count,Households,122 +CALAM - ANTELOPE,Income 25k-30k,Count,Households,116 +CALAM - ANTELOPE,Income 30k-35k,Count,Households,469 +CALAM - ANTELOPE,Income 35k-40k,Count,Households,248 +CALAM - ANTELOPE,Income 40k-45k,Count,Households,368 +CALAM - ANTELOPE,Income 45k-50k,Count,Households,449 +CALAM - ANTELOPE,Income 50k-60k,Count,Households,737 +CALAM - ANTELOPE,Income 60k-75k,Count,Households,1077 +CALAM - ANTELOPE,Income 75k-100k,Count,Households,1669 +CALAM - ANTELOPE,Income 100k-125k,Count,Households,1501 +CALAM - ANTELOPE,Income 125k-150k,Count,Households,1077 +CALAM - ANTELOPE,Income 150k-200k,Count,Households,1158 +CALAM - ANTELOPE,Income Above 200k,Count,Households,937 +CALAM - ANTELOPE,Income 0-25k,Count,Households,723 +CALAM - ANTELOPE,Income 25k-50k,Count,Households,1650 +CALAM - ANTELOPE,Income 50k-75k,Count,Households,1814 +CALAM - ANTELOPE,Income 0-50k,Count,Households,2373 +CALAM - ANTELOPE,Income 50k-100k,Count,Households,3483 +CALAM - ANTELOPE,Income 100k-150k,Count,Households,2578 +CALAM - ANTELOPE,Mortgage Total,Count,Households,5544 +CALAM - ANTELOPE,Mortgage Over 30% Income,Count,Households,1861 +CALAM - ANTELOPE,Mortgage Over 50% Income,Count,Households,621 +CALAM - ANTELOPE,No Mortgage Total,Count,Households,1747 +CALAM - ANTELOPE,No Mortgage Over 30% Income,Count,Households,184 +CALAM - ANTELOPE,No Mortgage Over 50% Income,Count,Households,106 +CALAM - ANTELOPE,Rent Total,Count,Households,3238 +CALAM - ANTELOPE,Rent Over 30% Income,Count,Households,1678 +CALAM - ANTELOPE,Rent Over 50% Income,Count,Households,649 +CALAM - ANTELOPE,Average Household Size,Hh Weighted,Household Weighted,3.1345302087117366 +CALAM - ANTELOPE,Median Household Income,Hh Weighted,Household Weighted,93741.54894285185 +CALAM - ANTELOPE,Per Capita Income,Pop Weighted,Population Weighted,34660.43709201826 +CALAM - ANTELOPE,Housing Costs Over 30% Income,Percent,Households,35.36 +CALAM - ANTELOPE,Housing Costs Over 50% Income,Percent,Households,13.07 +CALAM - ARDEN,Population Total,Count,Population,10112 +CALAM - ARDEN,Hispanic / Latino,Count,Population,3433 +CALAM - ARDEN,White,Count,Population,2392 +CALAM - ARDEN,Black-/ African American,Count,Population,1977 +CALAM - ARDEN,Native American,Count,Population,70 +CALAM - ARDEN,Asian,Count,Population,1082 +CALAM - ARDEN,Pacific Islander,Count,Population,59 +CALAM - ARDEN,Other / Multiple,Count,Population,1100 +CALAM - ARDEN,Hispanic / Latino,Percent,Population,33.95 +CALAM - ARDEN,White,Percent,Population,23.65 +CALAM - ARDEN,Black-/ African American,Percent,Population,19.55 +CALAM - ARDEN,Native American,Percent,Population,0.69 +CALAM - ARDEN,Asian,Percent,Population,10.7 +CALAM - ARDEN,Pacific Islander,Percent,Population,0.58 +CALAM - ARDEN,Other / Multiple,Percent,Population,10.87 +CALAM - ARDEN,Poverty Total Assessed,Count,Population,10034 +CALAM - ARDEN,Poverty Below,Count,Population,3130 +CALAM - ARDEN,Poverty Above,Count,Population,6904 +CALAM - ARDEN,Poverty Rate,Percent,Population,31.19 +CALAM - ARDEN,Households Total,Count,Households,3823 +CALAM - ARDEN,Income Below 10k,Count,Households,201 +CALAM - ARDEN,Income 10k-15k,Count,Households,259 +CALAM - ARDEN,Income 15k-20k,Count,Households,239 +CALAM - ARDEN,Income 20k-25k,Count,Households,167 +CALAM - ARDEN,Income 25k-30k,Count,Households,319 +CALAM - ARDEN,Income 30k-35k,Count,Households,190 +CALAM - ARDEN,Income 35k-40k,Count,Households,142 +CALAM - ARDEN,Income 40k-45k,Count,Households,236 +CALAM - ARDEN,Income 45k-50k,Count,Households,207 +CALAM - ARDEN,Income 50k-60k,Count,Households,440 +CALAM - ARDEN,Income 60k-75k,Count,Households,394 +CALAM - ARDEN,Income 75k-100k,Count,Households,535 +CALAM - ARDEN,Income 100k-125k,Count,Households,228 +CALAM - ARDEN,Income 125k-150k,Count,Households,148 +CALAM - ARDEN,Income 150k-200k,Count,Households,62 +CALAM - ARDEN,Income Above 200k,Count,Households,58 +CALAM - ARDEN,Income 0-25k,Count,Households,866 +CALAM - ARDEN,Income 25k-50k,Count,Households,1093 +CALAM - ARDEN,Income 50k-75k,Count,Households,834 +CALAM - ARDEN,Income 0-50k,Count,Households,1959 +CALAM - ARDEN,Income 50k-100k,Count,Households,1368 +CALAM - ARDEN,Income 100k-150k,Count,Households,376 +CALAM - ARDEN,Mortgage Total,Count,Households,265 +CALAM - ARDEN,Mortgage Over 30% Income,Count,Households,84 +CALAM - ARDEN,Mortgage Over 50% Income,Count,Households,46 +CALAM - ARDEN,No Mortgage Total,Count,Households,133 +CALAM - ARDEN,No Mortgage Over 30% Income,Count,Households,8 +CALAM - ARDEN,No Mortgage Over 50% Income,Count,Households,3 +CALAM - ARDEN,Rent Total,Count,Households,3426 +CALAM - ARDEN,Rent Over 30% Income,Count,Households,2124 +CALAM - ARDEN,Rent Over 50% Income,Count,Households,1170 +CALAM - ARDEN,Average Household Size,Hh Weighted,Household Weighted,2.6236426838601012 +CALAM - ARDEN,Median Household Income,Hh Weighted,Household Weighted,49624.62015306522 +CALAM - ARDEN,Per Capita Income,Pop Weighted,Population Weighted,22770.82157980776 +CALAM - ARDEN,Housing Costs Over 30% Income,Percent,Households,57.97 +CALAM - ARDEN,Housing Costs Over 50% Income,Percent,Households,31.87 +CALAM - ISLETON,Population Total,Count,Population,34 +CALAM - ISLETON,Hispanic / Latino,Count,Population,14 +CALAM - ISLETON,White,Count,Population,17 +CALAM - ISLETON,Black-/ African American,Count,Population,0 +CALAM - ISLETON,Native American,Count,Population,0 +CALAM - ISLETON,Asian,Count,Population,2 +CALAM - ISLETON,Pacific Islander,Count,Population,0 +CALAM - ISLETON,Other / Multiple,Count,Population,1 +CALAM - ISLETON,Hispanic / Latino,Percent,Population,42.06 +CALAM - ISLETON,White,Percent,Population,51.14 +CALAM - ISLETON,Black-/ African American,Percent,Population,0 +CALAM - ISLETON,Native American,Percent,Population,0 +CALAM - ISLETON,Asian,Percent,Population,4.55 +CALAM - ISLETON,Pacific Islander,Percent,Population,0 +CALAM - ISLETON,Other / Multiple,Percent,Population,2.25 +CALAM - ISLETON,Poverty Total Assessed,Count,Population,34 +CALAM - ISLETON,Poverty Below,Count,Population,7 +CALAM - ISLETON,Poverty Above,Count,Population,27 +CALAM - ISLETON,Poverty Rate,Percent,Population,20.89 +CALAM - ISLETON,Households Total,Count,Households,16 +CALAM - ISLETON,Income Below 10k,Count,Households,1 +CALAM - ISLETON,Income 10k-15k,Count,Households,1 +CALAM - ISLETON,Income 15k-20k,Count,Households,0 +CALAM - ISLETON,Income 20k-25k,Count,Households,1 +CALAM - ISLETON,Income 25k-30k,Count,Households,1 +CALAM - ISLETON,Income 30k-35k,Count,Households,0 +CALAM - ISLETON,Income 35k-40k,Count,Households,1 +CALAM - ISLETON,Income 40k-45k,Count,Households,1 +CALAM - ISLETON,Income 45k-50k,Count,Households,0 +CALAM - ISLETON,Income 50k-60k,Count,Households,2 +CALAM - ISLETON,Income 60k-75k,Count,Households,1 +CALAM - ISLETON,Income 75k-100k,Count,Households,1 +CALAM - ISLETON,Income 100k-125k,Count,Households,3 +CALAM - ISLETON,Income 125k-150k,Count,Households,1 +CALAM - ISLETON,Income 150k-200k,Count,Households,0 +CALAM - ISLETON,Income Above 200k,Count,Households,1 +CALAM - ISLETON,Income 0-25k,Count,Households,4 +CALAM - ISLETON,Income 25k-50k,Count,Households,3 +CALAM - ISLETON,Income 50k-75k,Count,Households,3 +CALAM - ISLETON,Income 0-50k,Count,Households,6 +CALAM - ISLETON,Income 50k-100k,Count,Households,4 +CALAM - ISLETON,Income 100k-150k,Count,Households,4 +CALAM - ISLETON,Mortgage Total,Count,Households,6 +CALAM - ISLETON,Mortgage Over 30% Income,Count,Households,4 +CALAM - ISLETON,Mortgage Over 50% Income,Count,Households,1 +CALAM - ISLETON,No Mortgage Total,Count,Households,7 +CALAM - ISLETON,No Mortgage Over 30% Income,Count,Households,2 +CALAM - ISLETON,No Mortgage Over 50% Income,Count,Households,2 +CALAM - ISLETON,Rent Total,Count,Households,4 +CALAM - ISLETON,Rent Over 30% Income,Count,Households,1 +CALAM - ISLETON,Rent Over 50% Income,Count,Households,1 +CALAM - ISLETON,Average Household Size,Hh Weighted,Household Weighted,2.0789934965188213 +CALAM - ISLETON,Median Household Income,Hh Weighted,Household Weighted,57361.758044022434 +CALAM - ISLETON,Per Capita Income,Pop Weighted,Population Weighted,40672.21234441078 +CALAM - ISLETON,Housing Costs Over 30% Income,Percent,Households,39.45 +CALAM - ISLETON,Housing Costs Over 50% Income,Percent,Households,20.68 +CALAM - LINCOLN OAKS,Population Total,Count,Population,42916 +CALAM - LINCOLN OAKS,Hispanic / Latino,Count,Population,9056 +CALAM - LINCOLN OAKS,White,Count,Population,26529 +CALAM - LINCOLN OAKS,Black-/ African American,Count,Population,1486 +CALAM - LINCOLN OAKS,Native American,Count,Population,143 +CALAM - LINCOLN OAKS,Asian,Count,Population,2706 +CALAM - LINCOLN OAKS,Pacific Islander,Count,Population,288 +CALAM - LINCOLN OAKS,Other / Multiple,Count,Population,2708 +CALAM - LINCOLN OAKS,Hispanic / Latino,Percent,Population,21.1 +CALAM - LINCOLN OAKS,White,Percent,Population,61.82 +CALAM - LINCOLN OAKS,Black-/ African American,Percent,Population,3.46 +CALAM - LINCOLN OAKS,Native American,Percent,Population,0.33 +CALAM - LINCOLN OAKS,Asian,Percent,Population,6.31 +CALAM - LINCOLN OAKS,Pacific Islander,Percent,Population,0.67 +CALAM - LINCOLN OAKS,Other / Multiple,Percent,Population,6.31 +CALAM - LINCOLN OAKS,Poverty Total Assessed,Count,Population,42823 +CALAM - LINCOLN OAKS,Poverty Below,Count,Population,4074 +CALAM - LINCOLN OAKS,Poverty Above,Count,Population,38749 +CALAM - LINCOLN OAKS,Poverty Rate,Percent,Population,9.51 +CALAM - LINCOLN OAKS,Households Total,Count,Households,15621 +CALAM - LINCOLN OAKS,Income Below 10k,Count,Households,740 +CALAM - LINCOLN OAKS,Income 10k-15k,Count,Households,375 +CALAM - LINCOLN OAKS,Income 15k-20k,Count,Households,308 +CALAM - LINCOLN OAKS,Income 20k-25k,Count,Households,622 +CALAM - LINCOLN OAKS,Income 25k-30k,Count,Households,488 +CALAM - LINCOLN OAKS,Income 30k-35k,Count,Households,616 +CALAM - LINCOLN OAKS,Income 35k-40k,Count,Households,585 +CALAM - LINCOLN OAKS,Income 40k-45k,Count,Households,629 +CALAM - LINCOLN OAKS,Income 45k-50k,Count,Households,645 +CALAM - LINCOLN OAKS,Income 50k-60k,Count,Households,1035 +CALAM - LINCOLN OAKS,Income 60k-75k,Count,Households,1641 +CALAM - LINCOLN OAKS,Income 75k-100k,Count,Households,2442 +CALAM - LINCOLN OAKS,Income 100k-125k,Count,Households,1889 +CALAM - LINCOLN OAKS,Income 125k-150k,Count,Households,1272 +CALAM - LINCOLN OAKS,Income 150k-200k,Count,Households,1555 +CALAM - LINCOLN OAKS,Income Above 200k,Count,Households,778 +CALAM - LINCOLN OAKS,Income 0-25k,Count,Households,2046 +CALAM - LINCOLN OAKS,Income 25k-50k,Count,Households,2964 +CALAM - LINCOLN OAKS,Income 50k-75k,Count,Households,2675 +CALAM - LINCOLN OAKS,Income 0-50k,Count,Households,5010 +CALAM - LINCOLN OAKS,Income 50k-100k,Count,Households,5118 +CALAM - LINCOLN OAKS,Income 100k-150k,Count,Households,3161 +CALAM - LINCOLN OAKS,Mortgage Total,Count,Households,7390 +CALAM - LINCOLN OAKS,Mortgage Over 30% Income,Count,Households,2671 +CALAM - LINCOLN OAKS,Mortgage Over 50% Income,Count,Households,919 +CALAM - LINCOLN OAKS,No Mortgage Total,Count,Households,3332 +CALAM - LINCOLN OAKS,No Mortgage Over 30% Income,Count,Households,503 +CALAM - LINCOLN OAKS,No Mortgage Over 50% Income,Count,Households,298 +CALAM - LINCOLN OAKS,Rent Total,Count,Households,4900 +CALAM - LINCOLN OAKS,Rent Over 30% Income,Count,Households,2523 +CALAM - LINCOLN OAKS,Rent Over 50% Income,Count,Households,1302 +CALAM - LINCOLN OAKS,Average Household Size,Hh Weighted,Household Weighted,2.7302804909283616 +CALAM - LINCOLN OAKS,Median Household Income,Hh Weighted,Household Weighted,82035.52088760637 +CALAM - LINCOLN OAKS,Per Capita Income,Pop Weighted,Population Weighted,33728.94235778291 +CALAM - LINCOLN OAKS,Housing Costs Over 30% Income,Percent,Households,36.46 +CALAM - LINCOLN OAKS,Housing Costs Over 50% Income,Percent,Households,16.13 +CALAM - PARKWAY,Population Total,Count,Population,58635 +CALAM - PARKWAY,Hispanic / Latino,Count,Population,18665 +CALAM - PARKWAY,White,Count,Population,8921 +CALAM - PARKWAY,Black-/ African American,Count,Population,6965 +CALAM - PARKWAY,Native American,Count,Population,21 +CALAM - PARKWAY,Asian,Count,Population,19228 +CALAM - PARKWAY,Pacific Islander,Count,Population,1386 +CALAM - PARKWAY,Other / Multiple,Count,Population,3449 +CALAM - PARKWAY,Hispanic / Latino,Percent,Population,31.83 +CALAM - PARKWAY,White,Percent,Population,15.21 +CALAM - PARKWAY,Black-/ African American,Percent,Population,11.88 +CALAM - PARKWAY,Native American,Percent,Population,0.04 +CALAM - PARKWAY,Asian,Percent,Population,32.79 +CALAM - PARKWAY,Pacific Islander,Percent,Population,2.36 +CALAM - PARKWAY,Other / Multiple,Percent,Population,5.88 +CALAM - PARKWAY,Poverty Total Assessed,Count,Population,58434 +CALAM - PARKWAY,Poverty Below,Count,Population,9804 +CALAM - PARKWAY,Poverty Above,Count,Population,48630 +CALAM - PARKWAY,Poverty Rate,Percent,Population,16.78 +CALAM - PARKWAY,Households Total,Count,Households,17667 +CALAM - PARKWAY,Income Below 10k,Count,Households,1081 +CALAM - PARKWAY,Income 10k-15k,Count,Households,753 +CALAM - PARKWAY,Income 15k-20k,Count,Households,514 +CALAM - PARKWAY,Income 20k-25k,Count,Households,713 +CALAM - PARKWAY,Income 25k-30k,Count,Households,694 +CALAM - PARKWAY,Income 30k-35k,Count,Households,640 +CALAM - PARKWAY,Income 35k-40k,Count,Households,713 +CALAM - PARKWAY,Income 40k-45k,Count,Households,700 +CALAM - PARKWAY,Income 45k-50k,Count,Households,727 +CALAM - PARKWAY,Income 50k-60k,Count,Households,1145 +CALAM - PARKWAY,Income 60k-75k,Count,Households,1918 +CALAM - PARKWAY,Income 75k-100k,Count,Households,2490 +CALAM - PARKWAY,Income 100k-125k,Count,Households,1634 +CALAM - PARKWAY,Income 125k-150k,Count,Households,1532 +CALAM - PARKWAY,Income 150k-200k,Count,Households,1546 +CALAM - PARKWAY,Income Above 200k,Count,Households,865 +CALAM - PARKWAY,Income 0-25k,Count,Households,3061 +CALAM - PARKWAY,Income 25k-50k,Count,Households,3475 +CALAM - PARKWAY,Income 50k-75k,Count,Households,3064 +CALAM - PARKWAY,Income 0-50k,Count,Households,6536 +CALAM - PARKWAY,Income 50k-100k,Count,Households,5554 +CALAM - PARKWAY,Income 100k-150k,Count,Households,3166 +CALAM - PARKWAY,Mortgage Total,Count,Households,7163 +CALAM - PARKWAY,Mortgage Over 30% Income,Count,Households,2719 +CALAM - PARKWAY,Mortgage Over 50% Income,Count,Households,1049 +CALAM - PARKWAY,No Mortgage Total,Count,Households,3418 +CALAM - PARKWAY,No Mortgage Over 30% Income,Count,Households,647 +CALAM - PARKWAY,No Mortgage Over 50% Income,Count,Households,383 +CALAM - PARKWAY,Rent Total,Count,Households,7086 +CALAM - PARKWAY,Rent Over 30% Income,Count,Households,3517 +CALAM - PARKWAY,Rent Over 50% Income,Count,Households,1917 +CALAM - PARKWAY,Average Household Size,Hh Weighted,Household Weighted,3.284607556891153 +CALAM - PARKWAY,Median Household Income,Hh Weighted,Household Weighted,72938.51439696936 +CALAM - PARKWAY,Per Capita Income,Pop Weighted,Population Weighted,26938.13941161509 +CALAM - PARKWAY,Housing Costs Over 30% Income,Percent,Households,38.96 +CALAM - PARKWAY,Housing Costs Over 50% Income,Percent,Households,18.96 +CALAM - SUBURBAN ROSEMONT,Population Total,Count,Population,57897 +CALAM - SUBURBAN ROSEMONT,Hispanic / Latino,Count,Population,13791 +CALAM - SUBURBAN ROSEMONT,White,Count,Population,25062 +CALAM - SUBURBAN ROSEMONT,Black-/ African American,Count,Population,7725 +CALAM - SUBURBAN ROSEMONT,Native American,Count,Population,91 +CALAM - SUBURBAN ROSEMONT,Asian,Count,Population,6905 +CALAM - SUBURBAN ROSEMONT,Pacific Islander,Count,Population,380 +CALAM - SUBURBAN ROSEMONT,Other / Multiple,Count,Population,3942 +CALAM - SUBURBAN ROSEMONT,Hispanic / Latino,Percent,Population,23.82 +CALAM - SUBURBAN ROSEMONT,White,Percent,Population,43.29 +CALAM - SUBURBAN ROSEMONT,Black-/ African American,Percent,Population,13.34 +CALAM - SUBURBAN ROSEMONT,Native American,Percent,Population,0.16 +CALAM - SUBURBAN ROSEMONT,Asian,Percent,Population,11.93 +CALAM - SUBURBAN ROSEMONT,Pacific Islander,Percent,Population,0.66 +CALAM - SUBURBAN ROSEMONT,Other / Multiple,Percent,Population,6.81 +CALAM - SUBURBAN ROSEMONT,Poverty Total Assessed,Count,Population,57661 +CALAM - SUBURBAN ROSEMONT,Poverty Below,Count,Population,8374 +CALAM - SUBURBAN ROSEMONT,Poverty Above,Count,Population,49287 +CALAM - SUBURBAN ROSEMONT,Poverty Rate,Percent,Population,14.52 +CALAM - SUBURBAN ROSEMONT,Households Total,Count,Households,21045 +CALAM - SUBURBAN ROSEMONT,Income Below 10k,Count,Households,1156 +CALAM - SUBURBAN ROSEMONT,Income 10k-15k,Count,Households,612 +CALAM - SUBURBAN ROSEMONT,Income 15k-20k,Count,Households,472 +CALAM - SUBURBAN ROSEMONT,Income 20k-25k,Count,Households,744 +CALAM - SUBURBAN ROSEMONT,Income 25k-30k,Count,Households,653 +CALAM - SUBURBAN ROSEMONT,Income 30k-35k,Count,Households,568 +CALAM - SUBURBAN ROSEMONT,Income 35k-40k,Count,Households,582 +CALAM - SUBURBAN ROSEMONT,Income 40k-45k,Count,Households,874 +CALAM - SUBURBAN ROSEMONT,Income 45k-50k,Count,Households,628 +CALAM - SUBURBAN ROSEMONT,Income 50k-60k,Count,Households,1289 +CALAM - SUBURBAN ROSEMONT,Income 60k-75k,Count,Households,2508 +CALAM - SUBURBAN ROSEMONT,Income 75k-100k,Count,Households,3438 +CALAM - SUBURBAN ROSEMONT,Income 100k-125k,Count,Households,2595 +CALAM - SUBURBAN ROSEMONT,Income 125k-150k,Count,Households,1594 +CALAM - SUBURBAN ROSEMONT,Income 150k-200k,Count,Households,1671 +CALAM - SUBURBAN ROSEMONT,Income Above 200k,Count,Households,1661 +CALAM - SUBURBAN ROSEMONT,Income 0-25k,Count,Households,2985 +CALAM - SUBURBAN ROSEMONT,Income 25k-50k,Count,Households,3305 +CALAM - SUBURBAN ROSEMONT,Income 50k-75k,Count,Households,3797 +CALAM - SUBURBAN ROSEMONT,Income 0-50k,Count,Households,6290 +CALAM - SUBURBAN ROSEMONT,Income 50k-100k,Count,Households,7235 +CALAM - SUBURBAN ROSEMONT,Income 100k-150k,Count,Households,4189 +CALAM - SUBURBAN ROSEMONT,Mortgage Total,Count,Households,8262 +CALAM - SUBURBAN ROSEMONT,Mortgage Over 30% Income,Count,Households,2262 +CALAM - SUBURBAN ROSEMONT,Mortgage Over 50% Income,Count,Households,730 +CALAM - SUBURBAN ROSEMONT,No Mortgage Total,Count,Households,3425 +CALAM - SUBURBAN ROSEMONT,No Mortgage Over 30% Income,Count,Households,439 +CALAM - SUBURBAN ROSEMONT,No Mortgage Over 50% Income,Count,Households,271 +CALAM - SUBURBAN ROSEMONT,Rent Total,Count,Households,9358 +CALAM - SUBURBAN ROSEMONT,Rent Over 30% Income,Count,Households,4521 +CALAM - SUBURBAN ROSEMONT,Rent Over 50% Income,Count,Households,2320 +CALAM - SUBURBAN ROSEMONT,Average Household Size,Hh Weighted,Household Weighted,2.7269365676013217 +CALAM - SUBURBAN ROSEMONT,Median Household Income,Hh Weighted,Household Weighted,81229.87090779487 +CALAM - SUBURBAN ROSEMONT,Per Capita Income,Pop Weighted,Population Weighted,34497.37344907046 +CALAM - SUBURBAN ROSEMONT,Housing Costs Over 30% Income,Percent,Households,34.31 +CALAM - SUBURBAN ROSEMONT,Housing Costs Over 50% Income,Percent,Households,15.78 +CALAM - WALNUT GROVE,Population Total,Count,Population,12 +CALAM - WALNUT GROVE,Hispanic / Latino,Count,Population,5 +CALAM - WALNUT GROVE,White,Count,Population,5 +CALAM - WALNUT GROVE,Black-/ African American,Count,Population,0 +CALAM - WALNUT GROVE,Native American,Count,Population,0 +CALAM - WALNUT GROVE,Asian,Count,Population,1 +CALAM - WALNUT GROVE,Pacific Islander,Count,Population,0 +CALAM - WALNUT GROVE,Other / Multiple,Count,Population,0 +CALAM - WALNUT GROVE,Hispanic / Latino,Percent,Population,44.6 +CALAM - WALNUT GROVE,White,Percent,Population,45.84 +CALAM - WALNUT GROVE,Black-/ African American,Percent,Population,0 +CALAM - WALNUT GROVE,Native American,Percent,Population,0 +CALAM - WALNUT GROVE,Asian,Percent,Population,5.93 +CALAM - WALNUT GROVE,Pacific Islander,Percent,Population,0 +CALAM - WALNUT GROVE,Other / Multiple,Percent,Population,3.63 +CALAM - WALNUT GROVE,Poverty Total Assessed,Count,Population,12 +CALAM - WALNUT GROVE,Poverty Below,Count,Population,2 +CALAM - WALNUT GROVE,Poverty Above,Count,Population,10 +CALAM - WALNUT GROVE,Poverty Rate,Percent,Population,15.75 +CALAM - WALNUT GROVE,Households Total,Count,Households,5 +CALAM - WALNUT GROVE,Income Below 10k,Count,Households,0 +CALAM - WALNUT GROVE,Income 10k-15k,Count,Households,0 +CALAM - WALNUT GROVE,Income 15k-20k,Count,Households,0 +CALAM - WALNUT GROVE,Income 20k-25k,Count,Households,0 +CALAM - WALNUT GROVE,Income 25k-30k,Count,Households,0 +CALAM - WALNUT GROVE,Income 30k-35k,Count,Households,0 +CALAM - WALNUT GROVE,Income 35k-40k,Count,Households,0 +CALAM - WALNUT GROVE,Income 40k-45k,Count,Households,0 +CALAM - WALNUT GROVE,Income 45k-50k,Count,Households,0 +CALAM - WALNUT GROVE,Income 50k-60k,Count,Households,0 +CALAM - WALNUT GROVE,Income 60k-75k,Count,Households,2 +CALAM - WALNUT GROVE,Income 75k-100k,Count,Households,0 +CALAM - WALNUT GROVE,Income 100k-125k,Count,Households,0 +CALAM - WALNUT GROVE,Income 125k-150k,Count,Households,0 +CALAM - WALNUT GROVE,Income 150k-200k,Count,Households,0 +CALAM - WALNUT GROVE,Income Above 200k,Count,Households,1 +CALAM - WALNUT GROVE,Income 0-25k,Count,Households,1 +CALAM - WALNUT GROVE,Income 25k-50k,Count,Households,1 +CALAM - WALNUT GROVE,Income 50k-75k,Count,Households,2 +CALAM - WALNUT GROVE,Income 0-50k,Count,Households,2 +CALAM - WALNUT GROVE,Income 50k-100k,Count,Households,2 +CALAM - WALNUT GROVE,Income 100k-150k,Count,Households,0 +CALAM - WALNUT GROVE,Mortgage Total,Count,Households,2 +CALAM - WALNUT GROVE,Mortgage Over 30% Income,Count,Households,0 +CALAM - WALNUT GROVE,Mortgage Over 50% Income,Count,Households,0 +CALAM - WALNUT GROVE,No Mortgage Total,Count,Households,1 +CALAM - WALNUT GROVE,No Mortgage Over 30% Income,Count,Households,0 +CALAM - WALNUT GROVE,No Mortgage Over 50% Income,Count,Households,0 +CALAM - WALNUT GROVE,Rent Total,Count,Households,2 +CALAM - WALNUT GROVE,Rent Over 30% Income,Count,Households,1 +CALAM - WALNUT GROVE,Rent Over 50% Income,Count,Households,0 +CALAM - WALNUT GROVE,Average Household Size,Hh Weighted,Household Weighted,2.49 +CALAM - WALNUT GROVE,Median Household Income,Hh Weighted,Household Weighted,68248 +CALAM - WALNUT GROVE,Per Capita Income,Pop Weighted,Population Weighted,38950 +CALAM - WALNUT GROVE,Housing Costs Over 30% Income,Percent,Households,24.49 +CALAM - WALNUT GROVE,Housing Costs Over 50% Income,Percent,Households,14.65 +CALIFORNIA STATE FAIR,Population Total,Count,Population,532 +CALIFORNIA STATE FAIR,Hispanic / Latino,Count,Population,78 +CALIFORNIA STATE FAIR,White,Count,Population,262 +CALIFORNIA STATE FAIR,Black-/ African American,Count,Population,91 +CALIFORNIA STATE FAIR,Native American,Count,Population,0 +CALIFORNIA STATE FAIR,Asian,Count,Population,48 +CALIFORNIA STATE FAIR,Pacific Islander,Count,Population,0 +CALIFORNIA STATE FAIR,Other / Multiple,Count,Population,52 +CALIFORNIA STATE FAIR,Hispanic / Latino,Percent,Population,14.68 +CALIFORNIA STATE FAIR,White,Percent,Population,49.25 +CALIFORNIA STATE FAIR,Black-/ African American,Percent,Population,17.13 +CALIFORNIA STATE FAIR,Native American,Percent,Population,0 +CALIFORNIA STATE FAIR,Asian,Percent,Population,9.1 +CALIFORNIA STATE FAIR,Pacific Islander,Percent,Population,0 +CALIFORNIA STATE FAIR,Other / Multiple,Percent,Population,9.85 +CALIFORNIA STATE FAIR,Poverty Total Assessed,Count,Population,526 +CALIFORNIA STATE FAIR,Poverty Below,Count,Population,152 +CALIFORNIA STATE FAIR,Poverty Above,Count,Population,374 +CALIFORNIA STATE FAIR,Poverty Rate,Percent,Population,28.89 +CALIFORNIA STATE FAIR,Households Total,Count,Households,285 +CALIFORNIA STATE FAIR,Income Below 10k,Count,Households,65 +CALIFORNIA STATE FAIR,Income 10k-15k,Count,Households,13 +CALIFORNIA STATE FAIR,Income 15k-20k,Count,Households,8 +CALIFORNIA STATE FAIR,Income 20k-25k,Count,Households,5 +CALIFORNIA STATE FAIR,Income 25k-30k,Count,Households,9 +CALIFORNIA STATE FAIR,Income 30k-35k,Count,Households,14 +CALIFORNIA STATE FAIR,Income 35k-40k,Count,Households,2 +CALIFORNIA STATE FAIR,Income 40k-45k,Count,Households,0 +CALIFORNIA STATE FAIR,Income 45k-50k,Count,Households,23 +CALIFORNIA STATE FAIR,Income 50k-60k,Count,Households,29 +CALIFORNIA STATE FAIR,Income 60k-75k,Count,Households,30 +CALIFORNIA STATE FAIR,Income 75k-100k,Count,Households,35 +CALIFORNIA STATE FAIR,Income 100k-125k,Count,Households,21 +CALIFORNIA STATE FAIR,Income 125k-150k,Count,Households,11 +CALIFORNIA STATE FAIR,Income 150k-200k,Count,Households,17 +CALIFORNIA STATE FAIR,Income Above 200k,Count,Households,3 +CALIFORNIA STATE FAIR,Income 0-25k,Count,Households,91 +CALIFORNIA STATE FAIR,Income 25k-50k,Count,Households,48 +CALIFORNIA STATE FAIR,Income 50k-75k,Count,Households,59 +CALIFORNIA STATE FAIR,Income 0-50k,Count,Households,140 +CALIFORNIA STATE FAIR,Income 50k-100k,Count,Households,93 +CALIFORNIA STATE FAIR,Income 100k-150k,Count,Households,32 +CALIFORNIA STATE FAIR,Mortgage Total,Count,Households,0 +CALIFORNIA STATE FAIR,Mortgage Over 30% Income,Count,Households,0 +CALIFORNIA STATE FAIR,Mortgage Over 50% Income,Count,Households,0 +CALIFORNIA STATE FAIR,No Mortgage Total,Count,Households,0 +CALIFORNIA STATE FAIR,No Mortgage Over 30% Income,Count,Households,0 +CALIFORNIA STATE FAIR,No Mortgage Over 50% Income,Count,Households,0 +CALIFORNIA STATE FAIR,Rent Total,Count,Households,285 +CALIFORNIA STATE FAIR,Rent Over 30% Income,Count,Households,177 +CALIFORNIA STATE FAIR,Rent Over 50% Income,Count,Households,95 +CALIFORNIA STATE FAIR,Average Household Size,Hh Weighted,Household Weighted,1.82 +CALIFORNIA STATE FAIR,Median Household Income,Hh Weighted,Household Weighted,52886 +CALIFORNIA STATE FAIR,Per Capita Income,Pop Weighted,Population Weighted,33141 +CALIFORNIA STATE FAIR,Housing Costs Over 30% Income,Percent,Households,62.11 +CALIFORNIA STATE FAIR,Housing Costs Over 50% Income,Percent,Households,33.45 +CARMICHAEL WATER DISTRICT,Population Total,Count,Population,39253 +CARMICHAEL WATER DISTRICT,Hispanic / Latino,Count,Population,6192 +CARMICHAEL WATER DISTRICT,White,Count,Population,25026 +CARMICHAEL WATER DISTRICT,Black-/ African American,Count,Population,2230 +CARMICHAEL WATER DISTRICT,Native American,Count,Population,68 +CARMICHAEL WATER DISTRICT,Asian,Count,Population,3326 +CARMICHAEL WATER DISTRICT,Pacific Islander,Count,Population,295 +CARMICHAEL WATER DISTRICT,Other / Multiple,Count,Population,2116 +CARMICHAEL WATER DISTRICT,Hispanic / Latino,Percent,Population,15.78 +CARMICHAEL WATER DISTRICT,White,Percent,Population,63.76 +CARMICHAEL WATER DISTRICT,Black-/ African American,Percent,Population,5.68 +CARMICHAEL WATER DISTRICT,Native American,Percent,Population,0.17 +CARMICHAEL WATER DISTRICT,Asian,Percent,Population,8.47 +CARMICHAEL WATER DISTRICT,Pacific Islander,Percent,Population,0.75 +CARMICHAEL WATER DISTRICT,Other / Multiple,Percent,Population,5.39 +CARMICHAEL WATER DISTRICT,Poverty Total Assessed,Count,Population,38700 +CARMICHAEL WATER DISTRICT,Poverty Below,Count,Population,5000 +CARMICHAEL WATER DISTRICT,Poverty Above,Count,Population,33700 +CARMICHAEL WATER DISTRICT,Poverty Rate,Percent,Population,12.92 +CARMICHAEL WATER DISTRICT,Households Total,Count,Households,15937 +CARMICHAEL WATER DISTRICT,Income Below 10k,Count,Households,570 +CARMICHAEL WATER DISTRICT,Income 10k-15k,Count,Households,534 +CARMICHAEL WATER DISTRICT,Income 15k-20k,Count,Households,513 +CARMICHAEL WATER DISTRICT,Income 20k-25k,Count,Households,472 +CARMICHAEL WATER DISTRICT,Income 25k-30k,Count,Households,398 +CARMICHAEL WATER DISTRICT,Income 30k-35k,Count,Households,607 +CARMICHAEL WATER DISTRICT,Income 35k-40k,Count,Households,522 +CARMICHAEL WATER DISTRICT,Income 40k-45k,Count,Households,684 +CARMICHAEL WATER DISTRICT,Income 45k-50k,Count,Households,541 +CARMICHAEL WATER DISTRICT,Income 50k-60k,Count,Households,996 +CARMICHAEL WATER DISTRICT,Income 60k-75k,Count,Households,1595 +CARMICHAEL WATER DISTRICT,Income 75k-100k,Count,Households,1782 +CARMICHAEL WATER DISTRICT,Income 100k-125k,Count,Households,1724 +CARMICHAEL WATER DISTRICT,Income 125k-150k,Count,Households,1200 +CARMICHAEL WATER DISTRICT,Income 150k-200k,Count,Households,1678 +CARMICHAEL WATER DISTRICT,Income Above 200k,Count,Households,2122 +CARMICHAEL WATER DISTRICT,Income 0-25k,Count,Households,2088 +CARMICHAEL WATER DISTRICT,Income 25k-50k,Count,Households,2751 +CARMICHAEL WATER DISTRICT,Income 50k-75k,Count,Households,2591 +CARMICHAEL WATER DISTRICT,Income 0-50k,Count,Households,4839 +CARMICHAEL WATER DISTRICT,Income 50k-100k,Count,Households,4373 +CARMICHAEL WATER DISTRICT,Income 100k-150k,Count,Households,2924 +CARMICHAEL WATER DISTRICT,Mortgage Total,Count,Households,5256 +CARMICHAEL WATER DISTRICT,Mortgage Over 30% Income,Count,Households,1399 +CARMICHAEL WATER DISTRICT,Mortgage Over 50% Income,Count,Households,669 +CARMICHAEL WATER DISTRICT,No Mortgage Total,Count,Households,3147 +CARMICHAEL WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,358 +CARMICHAEL WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,177 +CARMICHAEL WATER DISTRICT,Rent Total,Count,Households,7534 +CARMICHAEL WATER DISTRICT,Rent Over 30% Income,Count,Households,4056 +CARMICHAEL WATER DISTRICT,Rent Over 50% Income,Count,Households,2068 +CARMICHAEL WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,2.405914184458731 +CARMICHAEL WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,96967.64494126133 +CARMICHAEL WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,46901.802443947105 +CARMICHAEL WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,36.48 +CARMICHAEL WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,18.29 +CITRUS HEIGHTS WATER DISTRICT,Population Total,Count,Population,68912 +CITRUS HEIGHTS WATER DISTRICT,Hispanic / Latino,Count,Population,12380 +CITRUS HEIGHTS WATER DISTRICT,White,Count,Population,48148 +CITRUS HEIGHTS WATER DISTRICT,Black-/ African American,Count,Population,2092 +CITRUS HEIGHTS WATER DISTRICT,Native American,Count,Population,162 +CITRUS HEIGHTS WATER DISTRICT,Asian,Count,Population,2875 +CITRUS HEIGHTS WATER DISTRICT,Pacific Islander,Count,Population,71 +CITRUS HEIGHTS WATER DISTRICT,Other / Multiple,Count,Population,3186 +CITRUS HEIGHTS WATER DISTRICT,Hispanic / Latino,Percent,Population,17.96 +CITRUS HEIGHTS WATER DISTRICT,White,Percent,Population,69.87 +CITRUS HEIGHTS WATER DISTRICT,Black-/ African American,Percent,Population,3.04 +CITRUS HEIGHTS WATER DISTRICT,Native American,Percent,Population,0.23 +CITRUS HEIGHTS WATER DISTRICT,Asian,Percent,Population,4.17 +CITRUS HEIGHTS WATER DISTRICT,Pacific Islander,Percent,Population,0.1 +CITRUS HEIGHTS WATER DISTRICT,Other / Multiple,Percent,Population,4.62 +CITRUS HEIGHTS WATER DISTRICT,Poverty Total Assessed,Count,Population,68581 +CITRUS HEIGHTS WATER DISTRICT,Poverty Below,Count,Population,6961 +CITRUS HEIGHTS WATER DISTRICT,Poverty Above,Count,Population,61620 +CITRUS HEIGHTS WATER DISTRICT,Poverty Rate,Percent,Population,10.15 +CITRUS HEIGHTS WATER DISTRICT,Households Total,Count,Households,25633 +CITRUS HEIGHTS WATER DISTRICT,Income Below 10k,Count,Households,1012 +CITRUS HEIGHTS WATER DISTRICT,Income 10k-15k,Count,Households,569 +CITRUS HEIGHTS WATER DISTRICT,Income 15k-20k,Count,Households,446 +CITRUS HEIGHTS WATER DISTRICT,Income 20k-25k,Count,Households,769 +CITRUS HEIGHTS WATER DISTRICT,Income 25k-30k,Count,Households,665 +CITRUS HEIGHTS WATER DISTRICT,Income 30k-35k,Count,Households,867 +CITRUS HEIGHTS WATER DISTRICT,Income 35k-40k,Count,Households,841 +CITRUS HEIGHTS WATER DISTRICT,Income 40k-45k,Count,Households,723 +CITRUS HEIGHTS WATER DISTRICT,Income 45k-50k,Count,Households,1165 +CITRUS HEIGHTS WATER DISTRICT,Income 50k-60k,Count,Households,1875 +CITRUS HEIGHTS WATER DISTRICT,Income 60k-75k,Count,Households,3057 +CITRUS HEIGHTS WATER DISTRICT,Income 75k-100k,Count,Households,3954 +CITRUS HEIGHTS WATER DISTRICT,Income 100k-125k,Count,Households,2744 +CITRUS HEIGHTS WATER DISTRICT,Income 125k-150k,Count,Households,2332 +CITRUS HEIGHTS WATER DISTRICT,Income 150k-200k,Count,Households,2533 +CITRUS HEIGHTS WATER DISTRICT,Income Above 200k,Count,Households,2080 +CITRUS HEIGHTS WATER DISTRICT,Income 0-25k,Count,Households,2796 +CITRUS HEIGHTS WATER DISTRICT,Income 25k-50k,Count,Households,4261 +CITRUS HEIGHTS WATER DISTRICT,Income 50k-75k,Count,Households,4932 +CITRUS HEIGHTS WATER DISTRICT,Income 0-50k,Count,Households,7057 +CITRUS HEIGHTS WATER DISTRICT,Income 50k-100k,Count,Households,8886 +CITRUS HEIGHTS WATER DISTRICT,Income 100k-150k,Count,Households,5075 +CITRUS HEIGHTS WATER DISTRICT,Mortgage Total,Count,Households,10344 +CITRUS HEIGHTS WATER DISTRICT,Mortgage Over 30% Income,Count,Households,3553 +CITRUS HEIGHTS WATER DISTRICT,Mortgage Over 50% Income,Count,Households,1380 +CITRUS HEIGHTS WATER DISTRICT,No Mortgage Total,Count,Households,4293 +CITRUS HEIGHTS WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,554 +CITRUS HEIGHTS WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,286 +CITRUS HEIGHTS WATER DISTRICT,Rent Total,Count,Households,10996 +CITRUS HEIGHTS WATER DISTRICT,Rent Over 30% Income,Count,Households,5759 +CITRUS HEIGHTS WATER DISTRICT,Rent Over 50% Income,Count,Households,2620 +CITRUS HEIGHTS WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,2.653808184173017 +CITRUS HEIGHTS WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,82960.7829472826 +CITRUS HEIGHTS WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,37323.17430154909 +CITRUS HEIGHTS WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,38.49 +CITRUS HEIGHTS WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,16.72 +CITY OF SACRAMENTO MAIN,Population Total,Count,Population,516189 +CITY OF SACRAMENTO MAIN,Hispanic / Latino,Count,Population,151211 +CITY OF SACRAMENTO MAIN,White,Count,Population,159508 +CITY OF SACRAMENTO MAIN,Black-/ African American,Count,Population,62060 +CITY OF SACRAMENTO MAIN,Native American,Count,Population,1249 +CITY OF SACRAMENTO MAIN,Asian,Count,Population,98585 +CITY OF SACRAMENTO MAIN,Pacific Islander,Count,Population,9242 +CITY OF SACRAMENTO MAIN,Other / Multiple,Count,Population,34334 +CITY OF SACRAMENTO MAIN,Hispanic / Latino,Percent,Population,29.29 +CITY OF SACRAMENTO MAIN,White,Percent,Population,30.9 +CITY OF SACRAMENTO MAIN,Black-/ African American,Percent,Population,12.02 +CITY OF SACRAMENTO MAIN,Native American,Percent,Population,0.24 +CITY OF SACRAMENTO MAIN,Asian,Percent,Population,19.1 +CITY OF SACRAMENTO MAIN,Pacific Islander,Percent,Population,1.79 +CITY OF SACRAMENTO MAIN,Other / Multiple,Percent,Population,6.65 +CITY OF SACRAMENTO MAIN,Poverty Total Assessed,Count,Population,508800 +CITY OF SACRAMENTO MAIN,Poverty Below,Count,Population,77003 +CITY OF SACRAMENTO MAIN,Poverty Above,Count,Population,431797 +CITY OF SACRAMENTO MAIN,Poverty Rate,Percent,Population,15.13 +CITY OF SACRAMENTO MAIN,Households Total,Count,Households,194000 +CITY OF SACRAMENTO MAIN,Income Below 10k,Count,Households,9540 +CITY OF SACRAMENTO MAIN,Income 10k-15k,Count,Households,9401 +CITY OF SACRAMENTO MAIN,Income 15k-20k,Count,Households,6217 +CITY OF SACRAMENTO MAIN,Income 20k-25k,Count,Households,6407 +CITY OF SACRAMENTO MAIN,Income 25k-30k,Count,Households,5804 +CITY OF SACRAMENTO MAIN,Income 30k-35k,Count,Households,6255 +CITY OF SACRAMENTO MAIN,Income 35k-40k,Count,Households,6278 +CITY OF SACRAMENTO MAIN,Income 40k-45k,Count,Households,6139 +CITY OF SACRAMENTO MAIN,Income 45k-50k,Count,Households,6729 +CITY OF SACRAMENTO MAIN,Income 50k-60k,Count,Households,13349 +CITY OF SACRAMENTO MAIN,Income 60k-75k,Count,Households,17396 +CITY OF SACRAMENTO MAIN,Income 75k-100k,Count,Households,26982 +CITY OF SACRAMENTO MAIN,Income 100k-125k,Count,Households,20453 +CITY OF SACRAMENTO MAIN,Income 125k-150k,Count,Households,15080 +CITY OF SACRAMENTO MAIN,Income 150k-200k,Count,Households,17439 +CITY OF SACRAMENTO MAIN,Income Above 200k,Count,Households,20531 +CITY OF SACRAMENTO MAIN,Income 0-25k,Count,Households,31564 +CITY OF SACRAMENTO MAIN,Income 25k-50k,Count,Households,31205 +CITY OF SACRAMENTO MAIN,Income 50k-75k,Count,Households,30745 +CITY OF SACRAMENTO MAIN,Income 0-50k,Count,Households,62769 +CITY OF SACRAMENTO MAIN,Income 50k-100k,Count,Households,57728 +CITY OF SACRAMENTO MAIN,Income 100k-150k,Count,Households,35533 +CITY OF SACRAMENTO MAIN,Mortgage Total,Count,Households,67435 +CITY OF SACRAMENTO MAIN,Mortgage Over 30% Income,Count,Households,21769 +CITY OF SACRAMENTO MAIN,Mortgage Over 50% Income,Count,Households,8217 +CITY OF SACRAMENTO MAIN,No Mortgage Total,Count,Households,29857 +CITY OF SACRAMENTO MAIN,No Mortgage Over 30% Income,Count,Households,3476 +CITY OF SACRAMENTO MAIN,No Mortgage Over 50% Income,Count,Households,1805 +CITY OF SACRAMENTO MAIN,Rent Total,Count,Households,96708 +CITY OF SACRAMENTO MAIN,Rent Over 30% Income,Count,Households,47510 +CITY OF SACRAMENTO MAIN,Rent Over 50% Income,Count,Households,24524 +CITY OF SACRAMENTO MAIN,Average Household Size,Hh Weighted,Household Weighted,2.6095944008555163 +CITY OF SACRAMENTO MAIN,Median Household Income,Hh Weighted,Household Weighted,84694.01855912723 +CITY OF SACRAMENTO MAIN,Per Capita Income,Pop Weighted,Population Weighted,39105.608740826385 +CITY OF SACRAMENTO MAIN,Housing Costs Over 30% Income,Percent,Households,37.5 +CITY OF SACRAMENTO MAIN,Housing Costs Over 50% Income,Percent,Households,17.81 +DEL PASO MANOR COUNTY WATER DI,Population Total,Count,Population,5592 +DEL PASO MANOR COUNTY WATER DI,Hispanic / Latino,Count,Population,687 +DEL PASO MANOR COUNTY WATER DI,White,Count,Population,3967 +DEL PASO MANOR COUNTY WATER DI,Black-/ African American,Count,Population,390 +DEL PASO MANOR COUNTY WATER DI,Native American,Count,Population,15 +DEL PASO MANOR COUNTY WATER DI,Asian,Count,Population,119 +DEL PASO MANOR COUNTY WATER DI,Pacific Islander,Count,Population,31 +DEL PASO MANOR COUNTY WATER DI,Other / Multiple,Count,Population,382 +DEL PASO MANOR COUNTY WATER DI,Hispanic / Latino,Percent,Population,12.28 +DEL PASO MANOR COUNTY WATER DI,White,Percent,Population,70.95 +DEL PASO MANOR COUNTY WATER DI,Black-/ African American,Percent,Population,6.97 +DEL PASO MANOR COUNTY WATER DI,Native American,Percent,Population,0.26 +DEL PASO MANOR COUNTY WATER DI,Asian,Percent,Population,2.13 +DEL PASO MANOR COUNTY WATER DI,Pacific Islander,Percent,Population,0.56 +DEL PASO MANOR COUNTY WATER DI,Other / Multiple,Percent,Population,6.84 +DEL PASO MANOR COUNTY WATER DI,Poverty Total Assessed,Count,Population,5592 +DEL PASO MANOR COUNTY WATER DI,Poverty Below,Count,Population,621 +DEL PASO MANOR COUNTY WATER DI,Poverty Above,Count,Population,4971 +DEL PASO MANOR COUNTY WATER DI,Poverty Rate,Percent,Population,11.1 +DEL PASO MANOR COUNTY WATER DI,Households Total,Count,Households,2222 +DEL PASO MANOR COUNTY WATER DI,Income Below 10k,Count,Households,170 +DEL PASO MANOR COUNTY WATER DI,Income 10k-15k,Count,Households,45 +DEL PASO MANOR COUNTY WATER DI,Income 15k-20k,Count,Households,54 +DEL PASO MANOR COUNTY WATER DI,Income 20k-25k,Count,Households,66 +DEL PASO MANOR COUNTY WATER DI,Income 25k-30k,Count,Households,21 +DEL PASO MANOR COUNTY WATER DI,Income 30k-35k,Count,Households,51 +DEL PASO MANOR COUNTY WATER DI,Income 35k-40k,Count,Households,66 +DEL PASO MANOR COUNTY WATER DI,Income 40k-45k,Count,Households,237 +DEL PASO MANOR COUNTY WATER DI,Income 45k-50k,Count,Households,40 +DEL PASO MANOR COUNTY WATER DI,Income 50k-60k,Count,Households,158 +DEL PASO MANOR COUNTY WATER DI,Income 60k-75k,Count,Households,278 +DEL PASO MANOR COUNTY WATER DI,Income 75k-100k,Count,Households,166 +DEL PASO MANOR COUNTY WATER DI,Income 100k-125k,Count,Households,171 +DEL PASO MANOR COUNTY WATER DI,Income 125k-150k,Count,Households,120 +DEL PASO MANOR COUNTY WATER DI,Income 150k-200k,Count,Households,347 +DEL PASO MANOR COUNTY WATER DI,Income Above 200k,Count,Households,231 +DEL PASO MANOR COUNTY WATER DI,Income 0-25k,Count,Households,336 +DEL PASO MANOR COUNTY WATER DI,Income 25k-50k,Count,Households,416 +DEL PASO MANOR COUNTY WATER DI,Income 50k-75k,Count,Households,436 +DEL PASO MANOR COUNTY WATER DI,Income 0-50k,Count,Households,752 +DEL PASO MANOR COUNTY WATER DI,Income 50k-100k,Count,Households,601 +DEL PASO MANOR COUNTY WATER DI,Income 100k-150k,Count,Households,291 +DEL PASO MANOR COUNTY WATER DI,Mortgage Total,Count,Households,922 +DEL PASO MANOR COUNTY WATER DI,Mortgage Over 30% Income,Count,Households,326 +DEL PASO MANOR COUNTY WATER DI,Mortgage Over 50% Income,Count,Households,189 +DEL PASO MANOR COUNTY WATER DI,No Mortgage Total,Count,Households,572 +DEL PASO MANOR COUNTY WATER DI,No Mortgage Over 30% Income,Count,Households,112 +DEL PASO MANOR COUNTY WATER DI,No Mortgage Over 50% Income,Count,Households,68 +DEL PASO MANOR COUNTY WATER DI,Rent Total,Count,Households,729 +DEL PASO MANOR COUNTY WATER DI,Rent Over 30% Income,Count,Households,509 +DEL PASO MANOR COUNTY WATER DI,Rent Over 50% Income,Count,Households,114 +DEL PASO MANOR COUNTY WATER DI,Average Household Size,Hh Weighted,Household Weighted,2.5168950080102928 +DEL PASO MANOR COUNTY WATER DI,Median Household Income,Hh Weighted,Household Weighted,90374.38264756008 +DEL PASO MANOR COUNTY WATER DI,Per Capita Income,Pop Weighted,Population Weighted,40254.832685705536 +DEL PASO MANOR COUNTY WATER DI,Housing Costs Over 30% Income,Percent,Households,42.59 +DEL PASO MANOR COUNTY WATER DI,Housing Costs Over 50% Income,Percent,Households,16.67 +DELTA CROSSING MHP,Population Total,Count,Population,0 +DELTA CROSSING MHP,Hispanic / Latino,Count,Population,0 +DELTA CROSSING MHP,White,Count,Population,0 +DELTA CROSSING MHP,Black-/ African American,Count,Population,0 +DELTA CROSSING MHP,Native American,Count,Population,0 +DELTA CROSSING MHP,Asian,Count,Population,0 +DELTA CROSSING MHP,Pacific Islander,Count,Population,0 +DELTA CROSSING MHP,Other / Multiple,Count,Population,0 +DELTA CROSSING MHP,Hispanic / Latino,Percent,Population,69.19 +DELTA CROSSING MHP,White,Percent,Population,28.71 +DELTA CROSSING MHP,Black-/ African American,Percent,Population,0 +DELTA CROSSING MHP,Native American,Percent,Population,0 +DELTA CROSSING MHP,Asian,Percent,Population,0 +DELTA CROSSING MHP,Pacific Islander,Percent,Population,0 +DELTA CROSSING MHP,Other / Multiple,Percent,Population,2.1 +DELTA CROSSING MHP,Poverty Total Assessed,Count,Population,0 +DELTA CROSSING MHP,Poverty Below,Count,Population,0 +DELTA CROSSING MHP,Poverty Above,Count,Population,0 +DELTA CROSSING MHP,Poverty Rate,Percent,Population,17.42 +DELTA CROSSING MHP,Households Total,Count,Households,0 +DELTA CROSSING MHP,Income Below 10k,Count,Households,0 +DELTA CROSSING MHP,Income 10k-15k,Count,Households,0 +DELTA CROSSING MHP,Income 15k-20k,Count,Households,0 +DELTA CROSSING MHP,Income 20k-25k,Count,Households,0 +DELTA CROSSING MHP,Income 25k-30k,Count,Households,0 +DELTA CROSSING MHP,Income 30k-35k,Count,Households,0 +DELTA CROSSING MHP,Income 35k-40k,Count,Households,0 +DELTA CROSSING MHP,Income 40k-45k,Count,Households,0 +DELTA CROSSING MHP,Income 45k-50k,Count,Households,0 +DELTA CROSSING MHP,Income 50k-60k,Count,Households,0 +DELTA CROSSING MHP,Income 60k-75k,Count,Households,0 +DELTA CROSSING MHP,Income 75k-100k,Count,Households,0 +DELTA CROSSING MHP,Income 100k-125k,Count,Households,0 +DELTA CROSSING MHP,Income 125k-150k,Count,Households,0 +DELTA CROSSING MHP,Income 150k-200k,Count,Households,0 +DELTA CROSSING MHP,Income Above 200k,Count,Households,0 +DELTA CROSSING MHP,Income 0-25k,Count,Households,0 +DELTA CROSSING MHP,Income 25k-50k,Count,Households,0 +DELTA CROSSING MHP,Income 50k-75k,Count,Households,0 +DELTA CROSSING MHP,Income 0-50k,Count,Households,0 +DELTA CROSSING MHP,Income 50k-100k,Count,Households,0 +DELTA CROSSING MHP,Income 100k-150k,Count,Households,0 +DELTA CROSSING MHP,Mortgage Total,Count,Households,0 +DELTA CROSSING MHP,Mortgage Over 30% Income,Count,Households,0 +DELTA CROSSING MHP,Mortgage Over 50% Income,Count,Households,0 +DELTA CROSSING MHP,No Mortgage Total,Count,Households,0 +DELTA CROSSING MHP,No Mortgage Over 30% Income,Count,Households,0 +DELTA CROSSING MHP,No Mortgage Over 50% Income,Count,Households,0 +DELTA CROSSING MHP,Rent Total,Count,Households,0 +DELTA CROSSING MHP,Rent Over 30% Income,Count,Households,0 +DELTA CROSSING MHP,Rent Over 50% Income,Count,Households,0 +DELTA CROSSING MHP,Average Household Size,Hh Weighted,Household Weighted,2.55 +DELTA CROSSING MHP,Median Household Income,Hh Weighted,Household Weighted,56250 +DELTA CROSSING MHP,Per Capita Income,Pop Weighted,Population Weighted,23510 +DELTA CROSSING MHP,Housing Costs Over 30% Income,Percent,Households,45.66 +DELTA CROSSING MHP,Housing Costs Over 50% Income,Percent,Households,25.57 +EAST WALNUT GROVE [SWS],Population Total,Count,Population,3 +EAST WALNUT GROVE [SWS],Hispanic / Latino,Count,Population,2 +EAST WALNUT GROVE [SWS],White,Count,Population,2 +EAST WALNUT GROVE [SWS],Black-/ African American,Count,Population,0 +EAST WALNUT GROVE [SWS],Native American,Count,Population,0 +EAST WALNUT GROVE [SWS],Asian,Count,Population,0 +EAST WALNUT GROVE [SWS],Pacific Islander,Count,Population,0 +EAST WALNUT GROVE [SWS],Other / Multiple,Count,Population,0 +EAST WALNUT GROVE [SWS],Hispanic / Latino,Percent,Population,44.6 +EAST WALNUT GROVE [SWS],White,Percent,Population,45.84 +EAST WALNUT GROVE [SWS],Black-/ African American,Percent,Population,0 +EAST WALNUT GROVE [SWS],Native American,Percent,Population,0 +EAST WALNUT GROVE [SWS],Asian,Percent,Population,5.93 +EAST WALNUT GROVE [SWS],Pacific Islander,Percent,Population,0 +EAST WALNUT GROVE [SWS],Other / Multiple,Percent,Population,3.63 +EAST WALNUT GROVE [SWS],Poverty Total Assessed,Count,Population,3 +EAST WALNUT GROVE [SWS],Poverty Below,Count,Population,1 +EAST WALNUT GROVE [SWS],Poverty Above,Count,Population,3 +EAST WALNUT GROVE [SWS],Poverty Rate,Percent,Population,15.75 +EAST WALNUT GROVE [SWS],Households Total,Count,Households,1 +EAST WALNUT GROVE [SWS],Income Below 10k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 10k-15k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 15k-20k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 20k-25k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 25k-30k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 30k-35k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 35k-40k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 40k-45k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 45k-50k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 50k-60k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 60k-75k,Count,Households,1 +EAST WALNUT GROVE [SWS],Income 75k-100k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 100k-125k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 125k-150k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 150k-200k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income Above 200k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 0-25k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 25k-50k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 50k-75k,Count,Households,1 +EAST WALNUT GROVE [SWS],Income 0-50k,Count,Households,0 +EAST WALNUT GROVE [SWS],Income 50k-100k,Count,Households,1 +EAST WALNUT GROVE [SWS],Income 100k-150k,Count,Households,0 +EAST WALNUT GROVE [SWS],Mortgage Total,Count,Households,0 +EAST WALNUT GROVE [SWS],Mortgage Over 30% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],Mortgage Over 50% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],No Mortgage Total,Count,Households,0 +EAST WALNUT GROVE [SWS],No Mortgage Over 30% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],No Mortgage Over 50% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],Rent Total,Count,Households,1 +EAST WALNUT GROVE [SWS],Rent Over 30% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],Rent Over 50% Income,Count,Households,0 +EAST WALNUT GROVE [SWS],Average Household Size,Hh Weighted,Household Weighted,2.49 +EAST WALNUT GROVE [SWS],Median Household Income,Hh Weighted,Household Weighted,68248 +EAST WALNUT GROVE [SWS],Per Capita Income,Pop Weighted,Population Weighted,38950 +EAST WALNUT GROVE [SWS],Housing Costs Over 30% Income,Percent,Households,24.49 +EAST WALNUT GROVE [SWS],Housing Costs Over 50% Income,Percent,Households,14.65 +EDGEWATER MOBILE HOME PARK,Population Total,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Hispanic / Latino,Count,Population,0 +EDGEWATER MOBILE HOME PARK,White,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Black-/ African American,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Native American,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Asian,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Pacific Islander,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Other / Multiple,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Hispanic / Latino,Percent,Population,3.9 +EDGEWATER MOBILE HOME PARK,White,Percent,Population,89.23 +EDGEWATER MOBILE HOME PARK,Black-/ African American,Percent,Population,3.23 +EDGEWATER MOBILE HOME PARK,Native American,Percent,Population,0 +EDGEWATER MOBILE HOME PARK,Asian,Percent,Population,0 +EDGEWATER MOBILE HOME PARK,Pacific Islander,Percent,Population,0 +EDGEWATER MOBILE HOME PARK,Other / Multiple,Percent,Population,3.63 +EDGEWATER MOBILE HOME PARK,Poverty Total Assessed,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Poverty Below,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Poverty Above,Count,Population,0 +EDGEWATER MOBILE HOME PARK,Poverty Rate,Percent,Population,35.94 +EDGEWATER MOBILE HOME PARK,Households Total,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income Below 10k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 10k-15k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 15k-20k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 20k-25k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 25k-30k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 30k-35k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 35k-40k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 40k-45k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 45k-50k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 50k-60k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 60k-75k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 75k-100k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 100k-125k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 125k-150k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 150k-200k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income Above 200k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 0-25k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 25k-50k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 50k-75k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 0-50k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 50k-100k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Income 100k-150k,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Mortgage Total,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Mortgage Over 30% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Mortgage Over 50% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,No Mortgage Total,Count,Households,0 +EDGEWATER MOBILE HOME PARK,No Mortgage Over 30% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,No Mortgage Over 50% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Rent Total,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Rent Over 30% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Rent Over 50% Income,Count,Households,0 +EDGEWATER MOBILE HOME PARK,Average Household Size,Hh Weighted,Household Weighted,1.79 +EDGEWATER MOBILE HOME PARK,Median Household Income,Hh Weighted,Household Weighted,38125 +EDGEWATER MOBILE HOME PARK,Per Capita Income,Pop Weighted,Population Weighted,33103 +EDGEWATER MOBILE HOME PARK,Housing Costs Over 30% Income,Percent,Households,28.02 +EDGEWATER MOBILE HOME PARK,Housing Costs Over 50% Income,Percent,Households,23.19 +EL DORADO MOBILE HOME PARK,Population Total,Count,Population,139 +EL DORADO MOBILE HOME PARK,Hispanic / Latino,Count,Population,84 +EL DORADO MOBILE HOME PARK,White,Count,Population,11 +EL DORADO MOBILE HOME PARK,Black-/ African American,Count,Population,15 +EL DORADO MOBILE HOME PARK,Native American,Count,Population,0 +EL DORADO MOBILE HOME PARK,Asian,Count,Population,19 +EL DORADO MOBILE HOME PARK,Pacific Islander,Count,Population,0 +EL DORADO MOBILE HOME PARK,Other / Multiple,Count,Population,11 +EL DORADO MOBILE HOME PARK,Hispanic / Latino,Percent,Population,60.26 +EL DORADO MOBILE HOME PARK,White,Percent,Population,7.8 +EL DORADO MOBILE HOME PARK,Black-/ African American,Percent,Population,10.48 +EL DORADO MOBILE HOME PARK,Native American,Percent,Population,0 +EL DORADO MOBILE HOME PARK,Asian,Percent,Population,13.27 +EL DORADO MOBILE HOME PARK,Pacific Islander,Percent,Population,0 +EL DORADO MOBILE HOME PARK,Other / Multiple,Percent,Population,8.19 +EL DORADO MOBILE HOME PARK,Poverty Total Assessed,Count,Population,139 +EL DORADO MOBILE HOME PARK,Poverty Below,Count,Population,60 +EL DORADO MOBILE HOME PARK,Poverty Above,Count,Population,79 +EL DORADO MOBILE HOME PARK,Poverty Rate,Percent,Population,43.12 +EL DORADO MOBILE HOME PARK,Households Total,Count,Households,48 +EL DORADO MOBILE HOME PARK,Income Below 10k,Count,Households,6 +EL DORADO MOBILE HOME PARK,Income 10k-15k,Count,Households,10 +EL DORADO MOBILE HOME PARK,Income 15k-20k,Count,Households,0 +EL DORADO MOBILE HOME PARK,Income 20k-25k,Count,Households,4 +EL DORADO MOBILE HOME PARK,Income 25k-30k,Count,Households,6 +EL DORADO MOBILE HOME PARK,Income 30k-35k,Count,Households,1 +EL DORADO MOBILE HOME PARK,Income 35k-40k,Count,Households,0 +EL DORADO MOBILE HOME PARK,Income 40k-45k,Count,Households,8 +EL DORADO MOBILE HOME PARK,Income 45k-50k,Count,Households,1 +EL DORADO MOBILE HOME PARK,Income 50k-60k,Count,Households,7 +EL DORADO MOBILE HOME PARK,Income 60k-75k,Count,Households,0 +EL DORADO MOBILE HOME PARK,Income 75k-100k,Count,Households,1 +EL DORADO MOBILE HOME PARK,Income 100k-125k,Count,Households,0 +EL DORADO MOBILE HOME PARK,Income 125k-150k,Count,Households,4 +EL DORADO MOBILE HOME PARK,Income 150k-200k,Count,Households,0 +EL DORADO MOBILE HOME PARK,Income Above 200k,Count,Households,1 +EL DORADO MOBILE HOME PARK,Income 0-25k,Count,Households,19 +EL DORADO MOBILE HOME PARK,Income 25k-50k,Count,Households,15 +EL DORADO MOBILE HOME PARK,Income 50k-75k,Count,Households,8 +EL DORADO MOBILE HOME PARK,Income 0-50k,Count,Households,34 +EL DORADO MOBILE HOME PARK,Income 50k-100k,Count,Households,9 +EL DORADO MOBILE HOME PARK,Income 100k-150k,Count,Households,4 +EL DORADO MOBILE HOME PARK,Mortgage Total,Count,Households,3 +EL DORADO MOBILE HOME PARK,Mortgage Over 30% Income,Count,Households,0 +EL DORADO MOBILE HOME PARK,Mortgage Over 50% Income,Count,Households,0 +EL DORADO MOBILE HOME PARK,No Mortgage Total,Count,Households,10 +EL DORADO MOBILE HOME PARK,No Mortgage Over 30% Income,Count,Households,5 +EL DORADO MOBILE HOME PARK,No Mortgage Over 50% Income,Count,Households,5 +EL DORADO MOBILE HOME PARK,Rent Total,Count,Households,35 +EL DORADO MOBILE HOME PARK,Rent Over 30% Income,Count,Households,17 +EL DORADO MOBILE HOME PARK,Rent Over 50% Income,Count,Households,10 +EL DORADO MOBILE HOME PARK,Average Household Size,Hh Weighted,Household Weighted,2.71 +EL DORADO MOBILE HOME PARK,Median Household Income,Hh Weighted,Household Weighted,29468 +EL DORADO MOBILE HOME PARK,Per Capita Income,Pop Weighted,Population Weighted,17394 +EL DORADO MOBILE HOME PARK,Housing Costs Over 30% Income,Percent,Households,46.7 +EL DORADO MOBILE HOME PARK,Housing Costs Over 50% Income,Percent,Households,31.09 +EL DORADO WEST MHP,Population Total,Count,Population,148 +EL DORADO WEST MHP,Hispanic / Latino,Count,Population,89 +EL DORADO WEST MHP,White,Count,Population,12 +EL DORADO WEST MHP,Black-/ African American,Count,Population,16 +EL DORADO WEST MHP,Native American,Count,Population,0 +EL DORADO WEST MHP,Asian,Count,Population,20 +EL DORADO WEST MHP,Pacific Islander,Count,Population,0 +EL DORADO WEST MHP,Other / Multiple,Count,Population,12 +EL DORADO WEST MHP,Hispanic / Latino,Percent,Population,60.26 +EL DORADO WEST MHP,White,Percent,Population,7.8 +EL DORADO WEST MHP,Black-/ African American,Percent,Population,10.48 +EL DORADO WEST MHP,Native American,Percent,Population,0 +EL DORADO WEST MHP,Asian,Percent,Population,13.27 +EL DORADO WEST MHP,Pacific Islander,Percent,Population,0 +EL DORADO WEST MHP,Other / Multiple,Percent,Population,8.19 +EL DORADO WEST MHP,Poverty Total Assessed,Count,Population,147 +EL DORADO WEST MHP,Poverty Below,Count,Population,63 +EL DORADO WEST MHP,Poverty Above,Count,Population,84 +EL DORADO WEST MHP,Poverty Rate,Percent,Population,43.12 +EL DORADO WEST MHP,Households Total,Count,Households,51 +EL DORADO WEST MHP,Income Below 10k,Count,Households,6 +EL DORADO WEST MHP,Income 10k-15k,Count,Households,10 +EL DORADO WEST MHP,Income 15k-20k,Count,Households,0 +EL DORADO WEST MHP,Income 20k-25k,Count,Households,4 +EL DORADO WEST MHP,Income 25k-30k,Count,Households,6 +EL DORADO WEST MHP,Income 30k-35k,Count,Households,1 +EL DORADO WEST MHP,Income 35k-40k,Count,Households,0 +EL DORADO WEST MHP,Income 40k-45k,Count,Households,8 +EL DORADO WEST MHP,Income 45k-50k,Count,Households,2 +EL DORADO WEST MHP,Income 50k-60k,Count,Households,8 +EL DORADO WEST MHP,Income 60k-75k,Count,Households,0 +EL DORADO WEST MHP,Income 75k-100k,Count,Households,1 +EL DORADO WEST MHP,Income 100k-125k,Count,Households,0 +EL DORADO WEST MHP,Income 125k-150k,Count,Households,5 +EL DORADO WEST MHP,Income 150k-200k,Count,Households,0 +EL DORADO WEST MHP,Income Above 200k,Count,Households,1 +EL DORADO WEST MHP,Income 0-25k,Count,Households,20 +EL DORADO WEST MHP,Income 25k-50k,Count,Households,16 +EL DORADO WEST MHP,Income 50k-75k,Count,Households,8 +EL DORADO WEST MHP,Income 0-50k,Count,Households,37 +EL DORADO WEST MHP,Income 50k-100k,Count,Households,9 +EL DORADO WEST MHP,Income 100k-150k,Count,Households,5 +EL DORADO WEST MHP,Mortgage Total,Count,Households,3 +EL DORADO WEST MHP,Mortgage Over 30% Income,Count,Households,0 +EL DORADO WEST MHP,Mortgage Over 50% Income,Count,Households,0 +EL DORADO WEST MHP,No Mortgage Total,Count,Households,10 +EL DORADO WEST MHP,No Mortgage Over 30% Income,Count,Households,6 +EL DORADO WEST MHP,No Mortgage Over 50% Income,Count,Households,6 +EL DORADO WEST MHP,Rent Total,Count,Households,38 +EL DORADO WEST MHP,Rent Over 30% Income,Count,Households,18 +EL DORADO WEST MHP,Rent Over 50% Income,Count,Households,10 +EL DORADO WEST MHP,Average Household Size,Hh Weighted,Household Weighted,2.7099999999999995 +EL DORADO WEST MHP,Median Household Income,Hh Weighted,Household Weighted,29468.000000000004 +EL DORADO WEST MHP,Per Capita Income,Pop Weighted,Population Weighted,17394 +EL DORADO WEST MHP,Housing Costs Over 30% Income,Percent,Households,46.7 +EL DORADO WEST MHP,Housing Costs Over 50% Income,Percent,Households,31.09 +ELEVEN OAKS MOBILE HOME COMMUNITY,Population Total,Count,Population,233 +ELEVEN OAKS MOBILE HOME COMMUNITY,Hispanic / Latino,Count,Population,45 +ELEVEN OAKS MOBILE HOME COMMUNITY,White,Count,Population,94 +ELEVEN OAKS MOBILE HOME COMMUNITY,Black-/ African American,Count,Population,56 +ELEVEN OAKS MOBILE HOME COMMUNITY,Native American,Count,Population,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Asian,Count,Population,37 +ELEVEN OAKS MOBILE HOME COMMUNITY,Pacific Islander,Count,Population,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Other / Multiple,Count,Population,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,Hispanic / Latino,Percent,Population,19.27 +ELEVEN OAKS MOBILE HOME COMMUNITY,White,Percent,Population,40.19 +ELEVEN OAKS MOBILE HOME COMMUNITY,Black-/ African American,Percent,Population,24.01 +ELEVEN OAKS MOBILE HOME COMMUNITY,Native American,Percent,Population,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Asian,Percent,Population,15.91 +ELEVEN OAKS MOBILE HOME COMMUNITY,Pacific Islander,Percent,Population,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Other / Multiple,Percent,Population,0.62 +ELEVEN OAKS MOBILE HOME COMMUNITY,Poverty Total Assessed,Count,Population,233 +ELEVEN OAKS MOBILE HOME COMMUNITY,Poverty Below,Count,Population,87 +ELEVEN OAKS MOBILE HOME COMMUNITY,Poverty Above,Count,Population,146 +ELEVEN OAKS MOBILE HOME COMMUNITY,Poverty Rate,Percent,Population,37.48 +ELEVEN OAKS MOBILE HOME COMMUNITY,Households Total,Count,Households,71 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income Below 10k,Count,Households,7 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 10k-15k,Count,Households,2 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 15k-20k,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 20k-25k,Count,Households,6 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 25k-30k,Count,Households,10 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 30k-35k,Count,Households,2 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 35k-40k,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 40k-45k,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 45k-50k,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 50k-60k,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 60k-75k,Count,Households,13 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 75k-100k,Count,Households,17 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 100k-125k,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 125k-150k,Count,Households,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 150k-200k,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income Above 200k,Count,Households,0 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 0-25k,Count,Households,17 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 25k-50k,Count,Households,17 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 50k-75k,Count,Households,15 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 0-50k,Count,Households,34 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 50k-100k,Count,Households,32 +ELEVEN OAKS MOBILE HOME COMMUNITY,Income 100k-150k,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Mortgage Total,Count,Households,8 +ELEVEN OAKS MOBILE HOME COMMUNITY,Mortgage Over 30% Income,Count,Households,3 +ELEVEN OAKS MOBILE HOME COMMUNITY,Mortgage Over 50% Income,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,No Mortgage Total,Count,Households,21 +ELEVEN OAKS MOBILE HOME COMMUNITY,No Mortgage Over 30% Income,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,No Mortgage Over 50% Income,Count,Households,1 +ELEVEN OAKS MOBILE HOME COMMUNITY,Rent Total,Count,Households,42 +ELEVEN OAKS MOBILE HOME COMMUNITY,Rent Over 30% Income,Count,Households,29 +ELEVEN OAKS MOBILE HOME COMMUNITY,Rent Over 50% Income,Count,Households,23 +ELEVEN OAKS MOBILE HOME COMMUNITY,Average Household Size,Hh Weighted,Household Weighted,3.28 +ELEVEN OAKS MOBILE HOME COMMUNITY,Median Household Income,Hh Weighted,Household Weighted,60521 +ELEVEN OAKS MOBILE HOME COMMUNITY,Per Capita Income,Pop Weighted,Population Weighted,18213 +ELEVEN OAKS MOBILE HOME COMMUNITY,Housing Costs Over 30% Income,Percent,Households,46.85 +ELEVEN OAKS MOBILE HOME COMMUNITY,Housing Costs Over 50% Income,Percent,Households,35.36 +ELK GROVE WATER SERVICE,Population Total,Count,Population,42647 +ELK GROVE WATER SERVICE,Hispanic / Latino,Count,Population,7656 +ELK GROVE WATER SERVICE,White,Count,Population,19550 +ELK GROVE WATER SERVICE,Black-/ African American,Count,Population,3209 +ELK GROVE WATER SERVICE,Native American,Count,Population,70 +ELK GROVE WATER SERVICE,Asian,Count,Population,8939 +ELK GROVE WATER SERVICE,Pacific Islander,Count,Population,388 +ELK GROVE WATER SERVICE,Other / Multiple,Count,Population,2835 +ELK GROVE WATER SERVICE,Hispanic / Latino,Percent,Population,17.95 +ELK GROVE WATER SERVICE,White,Percent,Population,45.84 +ELK GROVE WATER SERVICE,Black-/ African American,Percent,Population,7.53 +ELK GROVE WATER SERVICE,Native American,Percent,Population,0.16 +ELK GROVE WATER SERVICE,Asian,Percent,Population,20.96 +ELK GROVE WATER SERVICE,Pacific Islander,Percent,Population,0.91 +ELK GROVE WATER SERVICE,Other / Multiple,Percent,Population,6.65 +ELK GROVE WATER SERVICE,Poverty Total Assessed,Count,Population,42258 +ELK GROVE WATER SERVICE,Poverty Below,Count,Population,3264 +ELK GROVE WATER SERVICE,Poverty Above,Count,Population,38994 +ELK GROVE WATER SERVICE,Poverty Rate,Percent,Population,7.72 +ELK GROVE WATER SERVICE,Households Total,Count,Households,13239 +ELK GROVE WATER SERVICE,Income Below 10k,Count,Households,430 +ELK GROVE WATER SERVICE,Income 10k-15k,Count,Households,202 +ELK GROVE WATER SERVICE,Income 15k-20k,Count,Households,253 +ELK GROVE WATER SERVICE,Income 20k-25k,Count,Households,224 +ELK GROVE WATER SERVICE,Income 25k-30k,Count,Households,328 +ELK GROVE WATER SERVICE,Income 30k-35k,Count,Households,102 +ELK GROVE WATER SERVICE,Income 35k-40k,Count,Households,345 +ELK GROVE WATER SERVICE,Income 40k-45k,Count,Households,292 +ELK GROVE WATER SERVICE,Income 45k-50k,Count,Households,245 +ELK GROVE WATER SERVICE,Income 50k-60k,Count,Households,667 +ELK GROVE WATER SERVICE,Income 60k-75k,Count,Households,1117 +ELK GROVE WATER SERVICE,Income 75k-100k,Count,Households,1441 +ELK GROVE WATER SERVICE,Income 100k-125k,Count,Households,1470 +ELK GROVE WATER SERVICE,Income 125k-150k,Count,Households,1386 +ELK GROVE WATER SERVICE,Income 150k-200k,Count,Households,1907 +ELK GROVE WATER SERVICE,Income Above 200k,Count,Households,2832 +ELK GROVE WATER SERVICE,Income 0-25k,Count,Households,1108 +ELK GROVE WATER SERVICE,Income 25k-50k,Count,Households,1311 +ELK GROVE WATER SERVICE,Income 50k-75k,Count,Households,1784 +ELK GROVE WATER SERVICE,Income 0-50k,Count,Households,2420 +ELK GROVE WATER SERVICE,Income 50k-100k,Count,Households,3225 +ELK GROVE WATER SERVICE,Income 100k-150k,Count,Households,2856 +ELK GROVE WATER SERVICE,Mortgage Total,Count,Households,7552 +ELK GROVE WATER SERVICE,Mortgage Over 30% Income,Count,Households,1903 +ELK GROVE WATER SERVICE,Mortgage Over 50% Income,Count,Households,628 +ELK GROVE WATER SERVICE,No Mortgage Total,Count,Households,2861 +ELK GROVE WATER SERVICE,No Mortgage Over 30% Income,Count,Households,283 +ELK GROVE WATER SERVICE,No Mortgage Over 50% Income,Count,Households,113 +ELK GROVE WATER SERVICE,Rent Total,Count,Households,2826 +ELK GROVE WATER SERVICE,Rent Over 30% Income,Count,Households,1595 +ELK GROVE WATER SERVICE,Rent Over 50% Income,Count,Households,864 +ELK GROVE WATER SERVICE,Average Household Size,Hh Weighted,Household Weighted,3.179068135170295 +ELK GROVE WATER SERVICE,Median Household Income,Hh Weighted,Household Weighted,122770.99741351404 +ELK GROVE WATER SERVICE,Per Capita Income,Pop Weighted,Population Weighted,43429.03313732531 +ELK GROVE WATER SERVICE,Housing Costs Over 30% Income,Percent,Households,28.55 +ELK GROVE WATER SERVICE,Housing Costs Over 50% Income,Percent,Households,12.12 +FAIR OAKS WATER DISTRICT,Population Total,Count,Population,36003 +FAIR OAKS WATER DISTRICT,Hispanic / Latino,Count,Population,4655 +FAIR OAKS WATER DISTRICT,White,Count,Population,27050 +FAIR OAKS WATER DISTRICT,Black-/ African American,Count,Population,708 +FAIR OAKS WATER DISTRICT,Native American,Count,Population,94 +FAIR OAKS WATER DISTRICT,Asian,Count,Population,1372 +FAIR OAKS WATER DISTRICT,Pacific Islander,Count,Population,12 +FAIR OAKS WATER DISTRICT,Other / Multiple,Count,Population,2113 +FAIR OAKS WATER DISTRICT,Hispanic / Latino,Percent,Population,12.93 +FAIR OAKS WATER DISTRICT,White,Percent,Population,75.13 +FAIR OAKS WATER DISTRICT,Black-/ African American,Percent,Population,1.97 +FAIR OAKS WATER DISTRICT,Native American,Percent,Population,0.26 +FAIR OAKS WATER DISTRICT,Asian,Percent,Population,3.81 +FAIR OAKS WATER DISTRICT,Pacific Islander,Percent,Population,0.03 +FAIR OAKS WATER DISTRICT,Other / Multiple,Percent,Population,5.87 +FAIR OAKS WATER DISTRICT,Poverty Total Assessed,Count,Population,35775 +FAIR OAKS WATER DISTRICT,Poverty Below,Count,Population,2852 +FAIR OAKS WATER DISTRICT,Poverty Above,Count,Population,32923 +FAIR OAKS WATER DISTRICT,Poverty Rate,Percent,Population,7.97 +FAIR OAKS WATER DISTRICT,Households Total,Count,Households,14233 +FAIR OAKS WATER DISTRICT,Income Below 10k,Count,Households,546 +FAIR OAKS WATER DISTRICT,Income 10k-15k,Count,Households,332 +FAIR OAKS WATER DISTRICT,Income 15k-20k,Count,Households,113 +FAIR OAKS WATER DISTRICT,Income 20k-25k,Count,Households,229 +FAIR OAKS WATER DISTRICT,Income 25k-30k,Count,Households,208 +FAIR OAKS WATER DISTRICT,Income 30k-35k,Count,Households,391 +FAIR OAKS WATER DISTRICT,Income 35k-40k,Count,Households,206 +FAIR OAKS WATER DISTRICT,Income 40k-45k,Count,Households,469 +FAIR OAKS WATER DISTRICT,Income 45k-50k,Count,Households,293 +FAIR OAKS WATER DISTRICT,Income 50k-60k,Count,Households,804 +FAIR OAKS WATER DISTRICT,Income 60k-75k,Count,Households,1064 +FAIR OAKS WATER DISTRICT,Income 75k-100k,Count,Households,2214 +FAIR OAKS WATER DISTRICT,Income 100k-125k,Count,Households,1447 +FAIR OAKS WATER DISTRICT,Income 125k-150k,Count,Households,1568 +FAIR OAKS WATER DISTRICT,Income 150k-200k,Count,Households,1875 +FAIR OAKS WATER DISTRICT,Income Above 200k,Count,Households,2474 +FAIR OAKS WATER DISTRICT,Income 0-25k,Count,Households,1220 +FAIR OAKS WATER DISTRICT,Income 25k-50k,Count,Households,1568 +FAIR OAKS WATER DISTRICT,Income 50k-75k,Count,Households,1868 +FAIR OAKS WATER DISTRICT,Income 0-50k,Count,Households,2788 +FAIR OAKS WATER DISTRICT,Income 50k-100k,Count,Households,4082 +FAIR OAKS WATER DISTRICT,Income 100k-150k,Count,Households,3016 +FAIR OAKS WATER DISTRICT,Mortgage Total,Count,Households,7090 +FAIR OAKS WATER DISTRICT,Mortgage Over 30% Income,Count,Households,1872 +FAIR OAKS WATER DISTRICT,Mortgage Over 50% Income,Count,Households,845 +FAIR OAKS WATER DISTRICT,No Mortgage Total,Count,Households,3092 +FAIR OAKS WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,261 +FAIR OAKS WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,108 +FAIR OAKS WATER DISTRICT,Rent Total,Count,Households,4051 +FAIR OAKS WATER DISTRICT,Rent Over 30% Income,Count,Households,1844 +FAIR OAKS WATER DISTRICT,Rent Over 50% Income,Count,Households,768 +FAIR OAKS WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,2.4802167837955067 +FAIR OAKS WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,107985.74325851546 +FAIR OAKS WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,54435.00970487184 +FAIR OAKS WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,27.94 +FAIR OAKS WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,12.09 +FLORIN COUNTY WATER DISTRICT,Population Total,Count,Population,9951 +FLORIN COUNTY WATER DISTRICT,Hispanic / Latino,Count,Population,2963 +FLORIN COUNTY WATER DISTRICT,White,Count,Population,1548 +FLORIN COUNTY WATER DISTRICT,Black-/ African American,Count,Population,1394 +FLORIN COUNTY WATER DISTRICT,Native American,Count,Population,7 +FLORIN COUNTY WATER DISTRICT,Asian,Count,Population,2743 +FLORIN COUNTY WATER DISTRICT,Pacific Islander,Count,Population,866 +FLORIN COUNTY WATER DISTRICT,Other / Multiple,Count,Population,430 +FLORIN COUNTY WATER DISTRICT,Hispanic / Latino,Percent,Population,29.78 +FLORIN COUNTY WATER DISTRICT,White,Percent,Population,15.56 +FLORIN COUNTY WATER DISTRICT,Black-/ African American,Percent,Population,14.01 +FLORIN COUNTY WATER DISTRICT,Native American,Percent,Population,0.07 +FLORIN COUNTY WATER DISTRICT,Asian,Percent,Population,27.56 +FLORIN COUNTY WATER DISTRICT,Pacific Islander,Percent,Population,8.7 +FLORIN COUNTY WATER DISTRICT,Other / Multiple,Percent,Population,4.32 +FLORIN COUNTY WATER DISTRICT,Poverty Total Assessed,Count,Population,9835 +FLORIN COUNTY WATER DISTRICT,Poverty Below,Count,Population,1285 +FLORIN COUNTY WATER DISTRICT,Poverty Above,Count,Population,8550 +FLORIN COUNTY WATER DISTRICT,Poverty Rate,Percent,Population,13.06 +FLORIN COUNTY WATER DISTRICT,Households Total,Count,Households,2755 +FLORIN COUNTY WATER DISTRICT,Income Below 10k,Count,Households,84 +FLORIN COUNTY WATER DISTRICT,Income 10k-15k,Count,Households,125 +FLORIN COUNTY WATER DISTRICT,Income 15k-20k,Count,Households,53 +FLORIN COUNTY WATER DISTRICT,Income 20k-25k,Count,Households,154 +FLORIN COUNTY WATER DISTRICT,Income 25k-30k,Count,Households,103 +FLORIN COUNTY WATER DISTRICT,Income 30k-35k,Count,Households,46 +FLORIN COUNTY WATER DISTRICT,Income 35k-40k,Count,Households,86 +FLORIN COUNTY WATER DISTRICT,Income 40k-45k,Count,Households,176 +FLORIN COUNTY WATER DISTRICT,Income 45k-50k,Count,Households,224 +FLORIN COUNTY WATER DISTRICT,Income 50k-60k,Count,Households,258 +FLORIN COUNTY WATER DISTRICT,Income 60k-75k,Count,Households,223 +FLORIN COUNTY WATER DISTRICT,Income 75k-100k,Count,Households,432 +FLORIN COUNTY WATER DISTRICT,Income 100k-125k,Count,Households,297 +FLORIN COUNTY WATER DISTRICT,Income 125k-150k,Count,Households,215 +FLORIN COUNTY WATER DISTRICT,Income 150k-200k,Count,Households,143 +FLORIN COUNTY WATER DISTRICT,Income Above 200k,Count,Households,137 +FLORIN COUNTY WATER DISTRICT,Income 0-25k,Count,Households,417 +FLORIN COUNTY WATER DISTRICT,Income 25k-50k,Count,Households,635 +FLORIN COUNTY WATER DISTRICT,Income 50k-75k,Count,Households,481 +FLORIN COUNTY WATER DISTRICT,Income 0-50k,Count,Households,1051 +FLORIN COUNTY WATER DISTRICT,Income 50k-100k,Count,Households,913 +FLORIN COUNTY WATER DISTRICT,Income 100k-150k,Count,Households,512 +FLORIN COUNTY WATER DISTRICT,Mortgage Total,Count,Households,981 +FLORIN COUNTY WATER DISTRICT,Mortgage Over 30% Income,Count,Households,426 +FLORIN COUNTY WATER DISTRICT,Mortgage Over 50% Income,Count,Households,90 +FLORIN COUNTY WATER DISTRICT,No Mortgage Total,Count,Households,675 +FLORIN COUNTY WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,49 +FLORIN COUNTY WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,28 +FLORIN COUNTY WATER DISTRICT,Rent Total,Count,Households,1100 +FLORIN COUNTY WATER DISTRICT,Rent Over 30% Income,Count,Households,476 +FLORIN COUNTY WATER DISTRICT,Rent Over 50% Income,Count,Households,260 +FLORIN COUNTY WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,3.573005180660505 +FLORIN COUNTY WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,67048.12268615587 +FLORIN COUNTY WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,24517.639859299343 +FLORIN COUNTY WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,34.48 +FLORIN COUNTY WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,13.7 +FOLSOM STATE PRISON,Population Total,Count,Population,3536 +FOLSOM STATE PRISON,Hispanic / Latino,Count,Population,1257 +FOLSOM STATE PRISON,White,Count,Population,652 +FOLSOM STATE PRISON,Black-/ African American,Count,Population,1390 +FOLSOM STATE PRISON,Native American,Count,Population,57 +FOLSOM STATE PRISON,Asian,Count,Population,70 +FOLSOM STATE PRISON,Pacific Islander,Count,Population,34 +FOLSOM STATE PRISON,Other / Multiple,Count,Population,77 +FOLSOM STATE PRISON,Hispanic / Latino,Percent,Population,35.55 +FOLSOM STATE PRISON,White,Percent,Population,18.43 +FOLSOM STATE PRISON,Black-/ African American,Percent,Population,39.31 +FOLSOM STATE PRISON,Native American,Percent,Population,1.6 +FOLSOM STATE PRISON,Asian,Percent,Population,1.97 +FOLSOM STATE PRISON,Pacific Islander,Percent,Population,0.96 +FOLSOM STATE PRISON,Other / Multiple,Percent,Population,2.17 +FOLSOM STATE PRISON,Poverty Total Assessed,Count,Population,29 +FOLSOM STATE PRISON,Poverty Below,Count,Population,1 +FOLSOM STATE PRISON,Poverty Above,Count,Population,28 +FOLSOM STATE PRISON,Poverty Rate,Percent,Population,2.2 +FOLSOM STATE PRISON,Households Total,Count,Households,23 +FOLSOM STATE PRISON,Income Below 10k,Count,Households,0 +FOLSOM STATE PRISON,Income 10k-15k,Count,Households,0 +FOLSOM STATE PRISON,Income 15k-20k,Count,Households,0 +FOLSOM STATE PRISON,Income 20k-25k,Count,Households,0 +FOLSOM STATE PRISON,Income 25k-30k,Count,Households,0 +FOLSOM STATE PRISON,Income 30k-35k,Count,Households,0 +FOLSOM STATE PRISON,Income 35k-40k,Count,Households,0 +FOLSOM STATE PRISON,Income 40k-45k,Count,Households,0 +FOLSOM STATE PRISON,Income 45k-50k,Count,Households,0 +FOLSOM STATE PRISON,Income 50k-60k,Count,Households,0 +FOLSOM STATE PRISON,Income 60k-75k,Count,Households,0 +FOLSOM STATE PRISON,Income 75k-100k,Count,Households,0 +FOLSOM STATE PRISON,Income 100k-125k,Count,Households,4 +FOLSOM STATE PRISON,Income 125k-150k,Count,Households,4 +FOLSOM STATE PRISON,Income 150k-200k,Count,Households,12 +FOLSOM STATE PRISON,Income Above 200k,Count,Households,1 +FOLSOM STATE PRISON,Income 0-25k,Count,Households,0 +FOLSOM STATE PRISON,Income 25k-50k,Count,Households,0 +FOLSOM STATE PRISON,Income 50k-75k,Count,Households,0 +FOLSOM STATE PRISON,Income 0-50k,Count,Households,0 +FOLSOM STATE PRISON,Income 50k-100k,Count,Households,1 +FOLSOM STATE PRISON,Income 100k-150k,Count,Households,8 +FOLSOM STATE PRISON,Mortgage Total,Count,Households,3 +FOLSOM STATE PRISON,Mortgage Over 30% Income,Count,Households,1 +FOLSOM STATE PRISON,Mortgage Over 50% Income,Count,Households,0 +FOLSOM STATE PRISON,No Mortgage Total,Count,Households,0 +FOLSOM STATE PRISON,No Mortgage Over 30% Income,Count,Households,0 +FOLSOM STATE PRISON,No Mortgage Over 50% Income,Count,Households,0 +FOLSOM STATE PRISON,Rent Total,Count,Households,19 +FOLSOM STATE PRISON,Rent Over 30% Income,Count,Households,0 +FOLSOM STATE PRISON,Rent Over 50% Income,Count,Households,0 +FOLSOM STATE PRISON,Average Household Size,Hh Weighted,Household Weighted,2.726311489407616 +FOLSOM STATE PRISON,Median Household Income,Hh Weighted,Household Weighted,161047.2164545734 +FOLSOM STATE PRISON,Per Capita Income,Pop Weighted,Population Weighted,2271.2201161818602 +FOLSOM STATE PRISON,Housing Costs Over 30% Income,Percent,Households,4.67 +FOLSOM STATE PRISON,Housing Costs Over 50% Income,Percent,Households,0.53 +"FOLSOM, CITY OF - ASHLAND",Population Total,Count,Population,3845 +"FOLSOM, CITY OF - ASHLAND",Hispanic / Latino,Count,Population,318 +"FOLSOM, CITY OF - ASHLAND",White,Count,Population,2934 +"FOLSOM, CITY OF - ASHLAND",Black-/ African American,Count,Population,43 +"FOLSOM, CITY OF - ASHLAND",Native American,Count,Population,1 +"FOLSOM, CITY OF - ASHLAND",Asian,Count,Population,125 +"FOLSOM, CITY OF - ASHLAND",Pacific Islander,Count,Population,1 +"FOLSOM, CITY OF - ASHLAND",Other / Multiple,Count,Population,423 +"FOLSOM, CITY OF - ASHLAND",Hispanic / Latino,Percent,Population,8.26 +"FOLSOM, CITY OF - ASHLAND",White,Percent,Population,76.32 +"FOLSOM, CITY OF - ASHLAND",Black-/ African American,Percent,Population,1.12 +"FOLSOM, CITY OF - ASHLAND",Native American,Percent,Population,0.03 +"FOLSOM, CITY OF - ASHLAND",Asian,Percent,Population,3.26 +"FOLSOM, CITY OF - ASHLAND",Pacific Islander,Percent,Population,0.02 +"FOLSOM, CITY OF - ASHLAND",Other / Multiple,Percent,Population,10.99 +"FOLSOM, CITY OF - ASHLAND",Poverty Total Assessed,Count,Population,3780 +"FOLSOM, CITY OF - ASHLAND",Poverty Below,Count,Population,143 +"FOLSOM, CITY OF - ASHLAND",Poverty Above,Count,Population,3637 +"FOLSOM, CITY OF - ASHLAND",Poverty Rate,Percent,Population,3.79 +"FOLSOM, CITY OF - ASHLAND",Households Total,Count,Households,1800 +"FOLSOM, CITY OF - ASHLAND",Income Below 10k,Count,Households,44 +"FOLSOM, CITY OF - ASHLAND",Income 10k-15k,Count,Households,17 +"FOLSOM, CITY OF - ASHLAND",Income 15k-20k,Count,Households,104 +"FOLSOM, CITY OF - ASHLAND",Income 20k-25k,Count,Households,43 +"FOLSOM, CITY OF - ASHLAND",Income 25k-30k,Count,Households,34 +"FOLSOM, CITY OF - ASHLAND",Income 30k-35k,Count,Households,209 +"FOLSOM, CITY OF - ASHLAND",Income 35k-40k,Count,Households,103 +"FOLSOM, CITY OF - ASHLAND",Income 40k-45k,Count,Households,74 +"FOLSOM, CITY OF - ASHLAND",Income 45k-50k,Count,Households,43 +"FOLSOM, CITY OF - ASHLAND",Income 50k-60k,Count,Households,43 +"FOLSOM, CITY OF - ASHLAND",Income 60k-75k,Count,Households,158 +"FOLSOM, CITY OF - ASHLAND",Income 75k-100k,Count,Households,248 +"FOLSOM, CITY OF - ASHLAND",Income 100k-125k,Count,Households,132 +"FOLSOM, CITY OF - ASHLAND",Income 125k-150k,Count,Households,80 +"FOLSOM, CITY OF - ASHLAND",Income 150k-200k,Count,Households,123 +"FOLSOM, CITY OF - ASHLAND",Income Above 200k,Count,Households,345 +"FOLSOM, CITY OF - ASHLAND",Income 0-25k,Count,Households,208 +"FOLSOM, CITY OF - ASHLAND",Income 25k-50k,Count,Households,463 +"FOLSOM, CITY OF - ASHLAND",Income 50k-75k,Count,Households,201 +"FOLSOM, CITY OF - ASHLAND",Income 0-50k,Count,Households,670 +"FOLSOM, CITY OF - ASHLAND",Income 50k-100k,Count,Households,449 +"FOLSOM, CITY OF - ASHLAND",Income 100k-150k,Count,Households,212 +"FOLSOM, CITY OF - ASHLAND",Mortgage Total,Count,Households,594 +"FOLSOM, CITY OF - ASHLAND",Mortgage Over 30% Income,Count,Households,164 +"FOLSOM, CITY OF - ASHLAND",Mortgage Over 50% Income,Count,Households,90 +"FOLSOM, CITY OF - ASHLAND",No Mortgage Total,Count,Households,847 +"FOLSOM, CITY OF - ASHLAND",No Mortgage Over 30% Income,Count,Households,368 +"FOLSOM, CITY OF - ASHLAND",No Mortgage Over 50% Income,Count,Households,82 +"FOLSOM, CITY OF - ASHLAND",Rent Total,Count,Households,358 +"FOLSOM, CITY OF - ASHLAND",Rent Over 30% Income,Count,Households,196 +"FOLSOM, CITY OF - ASHLAND",Rent Over 50% Income,Count,Households,74 +"FOLSOM, CITY OF - ASHLAND",Average Household Size,Hh Weighted,Household Weighted,2.087285536960886 +"FOLSOM, CITY OF - ASHLAND",Median Household Income,Hh Weighted,Household Weighted,76810.17111631615 +"FOLSOM, CITY OF - ASHLAND",Per Capita Income,Pop Weighted,Population Weighted,56773.973812166274 +"FOLSOM, CITY OF - ASHLAND",Housing Costs Over 30% Income,Percent,Households,40.42 +"FOLSOM, CITY OF - ASHLAND",Housing Costs Over 50% Income,Percent,Households,13.7 +"FOLSOM, CITY OF - MAIN",Population Total,Count,Population,62462 +"FOLSOM, CITY OF - MAIN",Hispanic / Latino,Count,Population,8433 +"FOLSOM, CITY OF - MAIN",White,Count,Population,35222 +"FOLSOM, CITY OF - MAIN",Black-/ African American,Count,Population,1693 +"FOLSOM, CITY OF - MAIN",Native American,Count,Population,105 +"FOLSOM, CITY OF - MAIN",Asian,Count,Population,12934 +"FOLSOM, CITY OF - MAIN",Pacific Islander,Count,Population,177 +"FOLSOM, CITY OF - MAIN",Other / Multiple,Count,Population,3897 +"FOLSOM, CITY OF - MAIN",Hispanic / Latino,Percent,Population,13.5 +"FOLSOM, CITY OF - MAIN",White,Percent,Population,56.39 +"FOLSOM, CITY OF - MAIN",Black-/ African American,Percent,Population,2.71 +"FOLSOM, CITY OF - MAIN",Native American,Percent,Population,0.17 +"FOLSOM, CITY OF - MAIN",Asian,Percent,Population,20.71 +"FOLSOM, CITY OF - MAIN",Pacific Islander,Percent,Population,0.28 +"FOLSOM, CITY OF - MAIN",Other / Multiple,Percent,Population,6.24 +"FOLSOM, CITY OF - MAIN",Poverty Total Assessed,Count,Population,62115 +"FOLSOM, CITY OF - MAIN",Poverty Below,Count,Population,3405 +"FOLSOM, CITY OF - MAIN",Poverty Above,Count,Population,58710 +"FOLSOM, CITY OF - MAIN",Poverty Rate,Percent,Population,5.48 +"FOLSOM, CITY OF - MAIN",Households Total,Count,Households,22409 +"FOLSOM, CITY OF - MAIN",Income Below 10k,Count,Households,807 +"FOLSOM, CITY OF - MAIN",Income 10k-15k,Count,Households,218 +"FOLSOM, CITY OF - MAIN",Income 15k-20k,Count,Households,390 +"FOLSOM, CITY OF - MAIN",Income 20k-25k,Count,Households,477 +"FOLSOM, CITY OF - MAIN",Income 25k-30k,Count,Households,418 +"FOLSOM, CITY OF - MAIN",Income 30k-35k,Count,Households,283 +"FOLSOM, CITY OF - MAIN",Income 35k-40k,Count,Households,329 +"FOLSOM, CITY OF - MAIN",Income 40k-45k,Count,Households,373 +"FOLSOM, CITY OF - MAIN",Income 45k-50k,Count,Households,451 +"FOLSOM, CITY OF - MAIN",Income 50k-60k,Count,Households,670 +"FOLSOM, CITY OF - MAIN",Income 60k-75k,Count,Households,1181 +"FOLSOM, CITY OF - MAIN",Income 75k-100k,Count,Households,2255 +"FOLSOM, CITY OF - MAIN",Income 100k-125k,Count,Households,2382 +"FOLSOM, CITY OF - MAIN",Income 125k-150k,Count,Households,1747 +"FOLSOM, CITY OF - MAIN",Income 150k-200k,Count,Households,4083 +"FOLSOM, CITY OF - MAIN",Income Above 200k,Count,Households,6344 +"FOLSOM, CITY OF - MAIN",Income 0-25k,Count,Households,1892 +"FOLSOM, CITY OF - MAIN",Income 25k-50k,Count,Households,1855 +"FOLSOM, CITY OF - MAIN",Income 50k-75k,Count,Households,1851 +"FOLSOM, CITY OF - MAIN",Income 0-50k,Count,Households,3747 +"FOLSOM, CITY OF - MAIN",Income 50k-100k,Count,Households,4106 +"FOLSOM, CITY OF - MAIN",Income 100k-150k,Count,Households,4129 +"FOLSOM, CITY OF - MAIN",Mortgage Total,Count,Households,11491 +"FOLSOM, CITY OF - MAIN",Mortgage Over 30% Income,Count,Households,2728 +"FOLSOM, CITY OF - MAIN",Mortgage Over 50% Income,Count,Households,1179 +"FOLSOM, CITY OF - MAIN",No Mortgage Total,Count,Households,3590 +"FOLSOM, CITY OF - MAIN",No Mortgage Over 30% Income,Count,Households,237 +"FOLSOM, CITY OF - MAIN",No Mortgage Over 50% Income,Count,Households,146 +"FOLSOM, CITY OF - MAIN",Rent Total,Count,Households,7328 +"FOLSOM, CITY OF - MAIN",Rent Over 30% Income,Count,Households,3010 +"FOLSOM, CITY OF - MAIN",Rent Over 50% Income,Count,Households,1321 +"FOLSOM, CITY OF - MAIN",Average Household Size,Hh Weighted,Household Weighted,2.7693559569400805 +"FOLSOM, CITY OF - MAIN",Median Household Income,Hh Weighted,Household Weighted,141856.37152396925 +"FOLSOM, CITY OF - MAIN",Per Capita Income,Pop Weighted,Population Weighted,58469.34569630441 +"FOLSOM, CITY OF - MAIN",Housing Costs Over 30% Income,Percent,Households,26.66 +"FOLSOM, CITY OF - MAIN",Housing Costs Over 50% Income,Percent,Households,11.81 +FREEPORT MARINA,Population Total,Count,Population,3 +FREEPORT MARINA,Hispanic / Latino,Count,Population,2 +FREEPORT MARINA,White,Count,Population,1 +FREEPORT MARINA,Black-/ African American,Count,Population,0 +FREEPORT MARINA,Native American,Count,Population,0 +FREEPORT MARINA,Asian,Count,Population,0 +FREEPORT MARINA,Pacific Islander,Count,Population,0 +FREEPORT MARINA,Other / Multiple,Count,Population,0 +FREEPORT MARINA,Hispanic / Latino,Percent,Population,69.19 +FREEPORT MARINA,White,Percent,Population,28.71 +FREEPORT MARINA,Black-/ African American,Percent,Population,0 +FREEPORT MARINA,Native American,Percent,Population,0 +FREEPORT MARINA,Asian,Percent,Population,0 +FREEPORT MARINA,Pacific Islander,Percent,Population,0 +FREEPORT MARINA,Other / Multiple,Percent,Population,2.1 +FREEPORT MARINA,Poverty Total Assessed,Count,Population,3 +FREEPORT MARINA,Poverty Below,Count,Population,1 +FREEPORT MARINA,Poverty Above,Count,Population,3 +FREEPORT MARINA,Poverty Rate,Percent,Population,17.42 +FREEPORT MARINA,Households Total,Count,Households,1 +FREEPORT MARINA,Income Below 10k,Count,Households,0 +FREEPORT MARINA,Income 10k-15k,Count,Households,0 +FREEPORT MARINA,Income 15k-20k,Count,Households,0 +FREEPORT MARINA,Income 20k-25k,Count,Households,0 +FREEPORT MARINA,Income 25k-30k,Count,Households,0 +FREEPORT MARINA,Income 30k-35k,Count,Households,0 +FREEPORT MARINA,Income 35k-40k,Count,Households,0 +FREEPORT MARINA,Income 40k-45k,Count,Households,0 +FREEPORT MARINA,Income 45k-50k,Count,Households,0 +FREEPORT MARINA,Income 50k-60k,Count,Households,0 +FREEPORT MARINA,Income 60k-75k,Count,Households,0 +FREEPORT MARINA,Income 75k-100k,Count,Households,0 +FREEPORT MARINA,Income 100k-125k,Count,Households,0 +FREEPORT MARINA,Income 125k-150k,Count,Households,0 +FREEPORT MARINA,Income 150k-200k,Count,Households,0 +FREEPORT MARINA,Income Above 200k,Count,Households,0 +FREEPORT MARINA,Income 0-25k,Count,Households,0 +FREEPORT MARINA,Income 25k-50k,Count,Households,0 +FREEPORT MARINA,Income 50k-75k,Count,Households,0 +FREEPORT MARINA,Income 0-50k,Count,Households,0 +FREEPORT MARINA,Income 50k-100k,Count,Households,0 +FREEPORT MARINA,Income 100k-150k,Count,Households,0 +FREEPORT MARINA,Mortgage Total,Count,Households,0 +FREEPORT MARINA,Mortgage Over 30% Income,Count,Households,0 +FREEPORT MARINA,Mortgage Over 50% Income,Count,Households,0 +FREEPORT MARINA,No Mortgage Total,Count,Households,0 +FREEPORT MARINA,No Mortgage Over 30% Income,Count,Households,0 +FREEPORT MARINA,No Mortgage Over 50% Income,Count,Households,0 +FREEPORT MARINA,Rent Total,Count,Households,0 +FREEPORT MARINA,Rent Over 30% Income,Count,Households,0 +FREEPORT MARINA,Rent Over 50% Income,Count,Households,0 +FREEPORT MARINA,Average Household Size,Hh Weighted,Household Weighted,2.55 +FREEPORT MARINA,Median Household Income,Hh Weighted,Household Weighted,56250 +FREEPORT MARINA,Per Capita Income,Pop Weighted,Population Weighted,23510 +FREEPORT MARINA,Housing Costs Over 30% Income,Percent,Households,45.66 +FREEPORT MARINA,Housing Costs Over 50% Income,Percent,Households,25.57 +"GALT, CITY OF",Population Total,Count,Population,21490 +"GALT, CITY OF",Hispanic / Latino,Count,Population,9314 +"GALT, CITY OF",White,Count,Population,9952 +"GALT, CITY OF",Black-/ African American,Count,Population,520 +"GALT, CITY OF",Native American,Count,Population,22 +"GALT, CITY OF",Asian,Count,Population,872 +"GALT, CITY OF",Pacific Islander,Count,Population,20 +"GALT, CITY OF",Other / Multiple,Count,Population,789 +"GALT, CITY OF",Hispanic / Latino,Percent,Population,43.34 +"GALT, CITY OF",White,Percent,Population,46.31 +"GALT, CITY OF",Black-/ African American,Percent,Population,2.42 +"GALT, CITY OF",Native American,Percent,Population,0.1 +"GALT, CITY OF",Asian,Percent,Population,4.06 +"GALT, CITY OF",Pacific Islander,Percent,Population,0.09 +"GALT, CITY OF",Other / Multiple,Percent,Population,3.67 +"GALT, CITY OF",Poverty Total Assessed,Count,Population,21341 +"GALT, CITY OF",Poverty Below,Count,Population,1404 +"GALT, CITY OF",Poverty Above,Count,Population,19937 +"GALT, CITY OF",Poverty Rate,Percent,Population,6.58 +"GALT, CITY OF",Households Total,Count,Households,6988 +"GALT, CITY OF",Income Below 10k,Count,Households,139 +"GALT, CITY OF",Income 10k-15k,Count,Households,168 +"GALT, CITY OF",Income 15k-20k,Count,Households,243 +"GALT, CITY OF",Income 20k-25k,Count,Households,210 +"GALT, CITY OF",Income 25k-30k,Count,Households,141 +"GALT, CITY OF",Income 30k-35k,Count,Households,342 +"GALT, CITY OF",Income 35k-40k,Count,Households,161 +"GALT, CITY OF",Income 40k-45k,Count,Households,347 +"GALT, CITY OF",Income 45k-50k,Count,Households,152 +"GALT, CITY OF",Income 50k-60k,Count,Households,550 +"GALT, CITY OF",Income 60k-75k,Count,Households,687 +"GALT, CITY OF",Income 75k-100k,Count,Households,807 +"GALT, CITY OF",Income 100k-125k,Count,Households,1096 +"GALT, CITY OF",Income 125k-150k,Count,Households,504 +"GALT, CITY OF",Income 150k-200k,Count,Households,789 +"GALT, CITY OF",Income Above 200k,Count,Households,650 +"GALT, CITY OF",Income 0-25k,Count,Households,761 +"GALT, CITY OF",Income 25k-50k,Count,Households,1143 +"GALT, CITY OF",Income 50k-75k,Count,Households,1237 +"GALT, CITY OF",Income 0-50k,Count,Households,1904 +"GALT, CITY OF",Income 50k-100k,Count,Households,2044 +"GALT, CITY OF",Income 100k-150k,Count,Households,1601 +"GALT, CITY OF",Mortgage Total,Count,Households,3724 +"GALT, CITY OF",Mortgage Over 30% Income,Count,Households,907 +"GALT, CITY OF",Mortgage Over 50% Income,Count,Households,523 +"GALT, CITY OF",No Mortgage Total,Count,Households,1454 +"GALT, CITY OF",No Mortgage Over 30% Income,Count,Households,109 +"GALT, CITY OF",No Mortgage Over 50% Income,Count,Households,44 +"GALT, CITY OF",Rent Total,Count,Households,1809 +"GALT, CITY OF",Rent Over 30% Income,Count,Households,906 +"GALT, CITY OF",Rent Over 50% Income,Count,Households,414 +"GALT, CITY OF",Average Household Size,Hh Weighted,Household Weighted,3.048248495417214 +"GALT, CITY OF",Median Household Income,Hh Weighted,Household Weighted,90632.9331221346 +"GALT, CITY OF",Per Capita Income,Pop Weighted,Population Weighted,33685.541351256594 +"GALT, CITY OF",Housing Costs Over 30% Income,Percent,Households,27.52 +"GALT, CITY OF",Housing Costs Over 50% Income,Percent,Households,14.05 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Population Total,Count,Population,6556 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Hispanic / Latino,Count,Population,1706 +GOLDEN STATE WATER CO - ARDEN WATER SERV,White,Count,Population,2887 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Black-/ African American,Count,Population,322 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Native American,Count,Population,0 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Asian,Count,Population,888 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Pacific Islander,Count,Population,11 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Other / Multiple,Count,Population,742 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Hispanic / Latino,Percent,Population,26.02 +GOLDEN STATE WATER CO - ARDEN WATER SERV,White,Percent,Population,44.04 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Black-/ African American,Percent,Population,4.91 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Native American,Percent,Population,0 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Asian,Percent,Population,13.54 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Pacific Islander,Percent,Population,0.16 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Other / Multiple,Percent,Population,11.32 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Poverty Total Assessed,Count,Population,6453 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Poverty Below,Count,Population,1626 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Poverty Above,Count,Population,4828 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Poverty Rate,Percent,Population,25.19 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Households Total,Count,Households,2173 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income Below 10k,Count,Households,19 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 10k-15k,Count,Households,82 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 15k-20k,Count,Households,19 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 20k-25k,Count,Households,141 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 25k-30k,Count,Households,53 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 30k-35k,Count,Households,173 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 35k-40k,Count,Households,34 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 40k-45k,Count,Households,179 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 45k-50k,Count,Households,37 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 50k-60k,Count,Households,139 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 60k-75k,Count,Households,351 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 75k-100k,Count,Households,319 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 100k-125k,Count,Households,132 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 125k-150k,Count,Households,172 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 150k-200k,Count,Households,141 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income Above 200k,Count,Households,183 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 0-25k,Count,Households,262 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 25k-50k,Count,Households,476 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 50k-75k,Count,Households,490 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 0-50k,Count,Households,738 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 50k-100k,Count,Households,809 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Income 100k-150k,Count,Households,303 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Mortgage Total,Count,Households,728 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Mortgage Over 30% Income,Count,Households,239 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Mortgage Over 50% Income,Count,Households,123 +GOLDEN STATE WATER CO - ARDEN WATER SERV,No Mortgage Total,Count,Households,131 +GOLDEN STATE WATER CO - ARDEN WATER SERV,No Mortgage Over 30% Income,Count,Households,0 +GOLDEN STATE WATER CO - ARDEN WATER SERV,No Mortgage Over 50% Income,Count,Households,0 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Rent Total,Count,Households,1315 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Rent Over 30% Income,Count,Households,599 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Rent Over 50% Income,Count,Households,335 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Average Household Size,Hh Weighted,Household Weighted,2.8977160737987506 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Median Household Income,Hh Weighted,Household Weighted,66579.356425836 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Per Capita Income,Pop Weighted,Population Weighted,30417.362367453836 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Housing Costs Over 30% Income,Percent,Households,38.56 +GOLDEN STATE WATER CO - ARDEN WATER SERV,Housing Costs Over 50% Income,Percent,Households,21.09 +GOLDEN STATE WATER CO. - CORDOVA,Population Total,Count,Population,48115 +GOLDEN STATE WATER CO. - CORDOVA,Hispanic / Latino,Count,Population,9009 +GOLDEN STATE WATER CO. - CORDOVA,White,Count,Population,26042 +GOLDEN STATE WATER CO. - CORDOVA,Black-/ African American,Count,Population,3982 +GOLDEN STATE WATER CO. - CORDOVA,Native American,Count,Population,229 +GOLDEN STATE WATER CO. - CORDOVA,Asian,Count,Population,6050 +GOLDEN STATE WATER CO. - CORDOVA,Pacific Islander,Count,Population,188 +GOLDEN STATE WATER CO. - CORDOVA,Other / Multiple,Count,Population,2615 +GOLDEN STATE WATER CO. - CORDOVA,Hispanic / Latino,Percent,Population,18.72 +GOLDEN STATE WATER CO. - CORDOVA,White,Percent,Population,54.13 +GOLDEN STATE WATER CO. - CORDOVA,Black-/ African American,Percent,Population,8.28 +GOLDEN STATE WATER CO. - CORDOVA,Native American,Percent,Population,0.48 +GOLDEN STATE WATER CO. - CORDOVA,Asian,Percent,Population,12.57 +GOLDEN STATE WATER CO. - CORDOVA,Pacific Islander,Percent,Population,0.39 +GOLDEN STATE WATER CO. - CORDOVA,Other / Multiple,Percent,Population,5.43 +GOLDEN STATE WATER CO. - CORDOVA,Poverty Total Assessed,Count,Population,47835 +GOLDEN STATE WATER CO. - CORDOVA,Poverty Below,Count,Population,4408 +GOLDEN STATE WATER CO. - CORDOVA,Poverty Above,Count,Population,43427 +GOLDEN STATE WATER CO. - CORDOVA,Poverty Rate,Percent,Population,9.21 +GOLDEN STATE WATER CO. - CORDOVA,Households Total,Count,Households,18022 +GOLDEN STATE WATER CO. - CORDOVA,Income Below 10k,Count,Households,509 +GOLDEN STATE WATER CO. - CORDOVA,Income 10k-15k,Count,Households,482 +GOLDEN STATE WATER CO. - CORDOVA,Income 15k-20k,Count,Households,310 +GOLDEN STATE WATER CO. - CORDOVA,Income 20k-25k,Count,Households,496 +GOLDEN STATE WATER CO. - CORDOVA,Income 25k-30k,Count,Households,480 +GOLDEN STATE WATER CO. - CORDOVA,Income 30k-35k,Count,Households,437 +GOLDEN STATE WATER CO. - CORDOVA,Income 35k-40k,Count,Households,389 +GOLDEN STATE WATER CO. - CORDOVA,Income 40k-45k,Count,Households,469 +GOLDEN STATE WATER CO. - CORDOVA,Income 45k-50k,Count,Households,598 +GOLDEN STATE WATER CO. - CORDOVA,Income 50k-60k,Count,Households,1276 +GOLDEN STATE WATER CO. - CORDOVA,Income 60k-75k,Count,Households,1692 +GOLDEN STATE WATER CO. - CORDOVA,Income 75k-100k,Count,Households,2653 +GOLDEN STATE WATER CO. - CORDOVA,Income 100k-125k,Count,Households,2565 +GOLDEN STATE WATER CO. - CORDOVA,Income 125k-150k,Count,Households,1671 +GOLDEN STATE WATER CO. - CORDOVA,Income 150k-200k,Count,Households,1948 +GOLDEN STATE WATER CO. - CORDOVA,Income Above 200k,Count,Households,2047 +GOLDEN STATE WATER CO. - CORDOVA,Income 0-25k,Count,Households,1796 +GOLDEN STATE WATER CO. - CORDOVA,Income 25k-50k,Count,Households,2374 +GOLDEN STATE WATER CO. - CORDOVA,Income 50k-75k,Count,Households,2968 +GOLDEN STATE WATER CO. - CORDOVA,Income 0-50k,Count,Households,4170 +GOLDEN STATE WATER CO. - CORDOVA,Income 50k-100k,Count,Households,5621 +GOLDEN STATE WATER CO. - CORDOVA,Income 100k-150k,Count,Households,4236 +GOLDEN STATE WATER CO. - CORDOVA,Mortgage Total,Count,Households,7380 +GOLDEN STATE WATER CO. - CORDOVA,Mortgage Over 30% Income,Count,Households,2174 +GOLDEN STATE WATER CO. - CORDOVA,Mortgage Over 50% Income,Count,Households,836 +GOLDEN STATE WATER CO. - CORDOVA,No Mortgage Total,Count,Households,3506 +GOLDEN STATE WATER CO. - CORDOVA,No Mortgage Over 30% Income,Count,Households,364 +GOLDEN STATE WATER CO. - CORDOVA,No Mortgage Over 50% Income,Count,Households,201 +GOLDEN STATE WATER CO. - CORDOVA,Rent Total,Count,Households,7137 +GOLDEN STATE WATER CO. - CORDOVA,Rent Over 30% Income,Count,Households,2744 +GOLDEN STATE WATER CO. - CORDOVA,Rent Over 50% Income,Count,Households,1410 +GOLDEN STATE WATER CO. - CORDOVA,Average Household Size,Hh Weighted,Household Weighted,2.6507166775240423 +GOLDEN STATE WATER CO. - CORDOVA,Median Household Income,Hh Weighted,Household Weighted,96697.06109071148 +GOLDEN STATE WATER CO. - CORDOVA,Per Capita Income,Pop Weighted,Population Weighted,42695.412199827406 +GOLDEN STATE WATER CO. - CORDOVA,Housing Costs Over 30% Income,Percent,Households,29.31 +GOLDEN STATE WATER CO. - CORDOVA,Housing Costs Over 50% Income,Percent,Households,13.58 +HAPPY HARBOR (SWS),Population Total,Count,Population,0 +HAPPY HARBOR (SWS),Hispanic / Latino,Count,Population,0 +HAPPY HARBOR (SWS),White,Count,Population,0 +HAPPY HARBOR (SWS),Black-/ African American,Count,Population,0 +HAPPY HARBOR (SWS),Native American,Count,Population,0 +HAPPY HARBOR (SWS),Asian,Count,Population,0 +HAPPY HARBOR (SWS),Pacific Islander,Count,Population,0 +HAPPY HARBOR (SWS),Other / Multiple,Count,Population,0 +HAPPY HARBOR (SWS),Hispanic / Latino,Percent,Population,3.9 +HAPPY HARBOR (SWS),White,Percent,Population,89.23 +HAPPY HARBOR (SWS),Black-/ African American,Percent,Population,3.23 +HAPPY HARBOR (SWS),Native American,Percent,Population,0 +HAPPY HARBOR (SWS),Asian,Percent,Population,0 +HAPPY HARBOR (SWS),Pacific Islander,Percent,Population,0 +HAPPY HARBOR (SWS),Other / Multiple,Percent,Population,3.63 +HAPPY HARBOR (SWS),Poverty Total Assessed,Count,Population,0 +HAPPY HARBOR (SWS),Poverty Below,Count,Population,0 +HAPPY HARBOR (SWS),Poverty Above,Count,Population,0 +HAPPY HARBOR (SWS),Poverty Rate,Percent,Population,35.94 +HAPPY HARBOR (SWS),Households Total,Count,Households,0 +HAPPY HARBOR (SWS),Income Below 10k,Count,Households,0 +HAPPY HARBOR (SWS),Income 10k-15k,Count,Households,0 +HAPPY HARBOR (SWS),Income 15k-20k,Count,Households,0 +HAPPY HARBOR (SWS),Income 20k-25k,Count,Households,0 +HAPPY HARBOR (SWS),Income 25k-30k,Count,Households,0 +HAPPY HARBOR (SWS),Income 30k-35k,Count,Households,0 +HAPPY HARBOR (SWS),Income 35k-40k,Count,Households,0 +HAPPY HARBOR (SWS),Income 40k-45k,Count,Households,0 +HAPPY HARBOR (SWS),Income 45k-50k,Count,Households,0 +HAPPY HARBOR (SWS),Income 50k-60k,Count,Households,0 +HAPPY HARBOR (SWS),Income 60k-75k,Count,Households,0 +HAPPY HARBOR (SWS),Income 75k-100k,Count,Households,0 +HAPPY HARBOR (SWS),Income 100k-125k,Count,Households,0 +HAPPY HARBOR (SWS),Income 125k-150k,Count,Households,0 +HAPPY HARBOR (SWS),Income 150k-200k,Count,Households,0 +HAPPY HARBOR (SWS),Income Above 200k,Count,Households,0 +HAPPY HARBOR (SWS),Income 0-25k,Count,Households,0 +HAPPY HARBOR (SWS),Income 25k-50k,Count,Households,0 +HAPPY HARBOR (SWS),Income 50k-75k,Count,Households,0 +HAPPY HARBOR (SWS),Income 0-50k,Count,Households,0 +HAPPY HARBOR (SWS),Income 50k-100k,Count,Households,0 +HAPPY HARBOR (SWS),Income 100k-150k,Count,Households,0 +HAPPY HARBOR (SWS),Mortgage Total,Count,Households,0 +HAPPY HARBOR (SWS),Mortgage Over 30% Income,Count,Households,0 +HAPPY HARBOR (SWS),Mortgage Over 50% Income,Count,Households,0 +HAPPY HARBOR (SWS),No Mortgage Total,Count,Households,0 +HAPPY HARBOR (SWS),No Mortgage Over 30% Income,Count,Households,0 +HAPPY HARBOR (SWS),No Mortgage Over 50% Income,Count,Households,0 +HAPPY HARBOR (SWS),Rent Total,Count,Households,0 +HAPPY HARBOR (SWS),Rent Over 30% Income,Count,Households,0 +HAPPY HARBOR (SWS),Rent Over 50% Income,Count,Households,0 +HAPPY HARBOR (SWS),Average Household Size,Hh Weighted,Household Weighted,1.79 +HAPPY HARBOR (SWS),Median Household Income,Hh Weighted,Household Weighted,38125 +HAPPY HARBOR (SWS),Per Capita Income,Pop Weighted,Population Weighted,33103 +HAPPY HARBOR (SWS),Housing Costs Over 30% Income,Percent,Households,28.02 +HAPPY HARBOR (SWS),Housing Costs Over 50% Income,Percent,Households,23.19 +HOLIDAY MOBILE VILLAGE,Population Total,Count,Population,46 +HOLIDAY MOBILE VILLAGE,Hispanic / Latino,Count,Population,18 +HOLIDAY MOBILE VILLAGE,White,Count,Population,7 +HOLIDAY MOBILE VILLAGE,Black-/ African American,Count,Population,3 +HOLIDAY MOBILE VILLAGE,Native American,Count,Population,0 +HOLIDAY MOBILE VILLAGE,Asian,Count,Population,15 +HOLIDAY MOBILE VILLAGE,Pacific Islander,Count,Population,0 +HOLIDAY MOBILE VILLAGE,Other / Multiple,Count,Population,3 +HOLIDAY MOBILE VILLAGE,Hispanic / Latino,Percent,Population,38.66 +HOLIDAY MOBILE VILLAGE,White,Percent,Population,15.12 +HOLIDAY MOBILE VILLAGE,Black-/ African American,Percent,Population,7.1 +HOLIDAY MOBILE VILLAGE,Native American,Percent,Population,0 +HOLIDAY MOBILE VILLAGE,Asian,Percent,Population,32.49 +HOLIDAY MOBILE VILLAGE,Pacific Islander,Percent,Population,0 +HOLIDAY MOBILE VILLAGE,Other / Multiple,Percent,Population,6.64 +HOLIDAY MOBILE VILLAGE,Poverty Total Assessed,Count,Population,46 +HOLIDAY MOBILE VILLAGE,Poverty Below,Count,Population,10 +HOLIDAY MOBILE VILLAGE,Poverty Above,Count,Population,36 +HOLIDAY MOBILE VILLAGE,Poverty Rate,Percent,Population,22.33 +HOLIDAY MOBILE VILLAGE,Households Total,Count,Households,16 +HOLIDAY MOBILE VILLAGE,Income Below 10k,Count,Households,2 +HOLIDAY MOBILE VILLAGE,Income 10k-15k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Income 15k-20k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 20k-25k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Income 25k-30k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 30k-35k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Income 35k-40k,Count,Households,5 +HOLIDAY MOBILE VILLAGE,Income 40k-45k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Income 45k-50k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 50k-60k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 60k-75k,Count,Households,2 +HOLIDAY MOBILE VILLAGE,Income 75k-100k,Count,Households,2 +HOLIDAY MOBILE VILLAGE,Income 100k-125k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Income 125k-150k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 150k-200k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income Above 200k,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Income 0-25k,Count,Households,4 +HOLIDAY MOBILE VILLAGE,Income 25k-50k,Count,Households,7 +HOLIDAY MOBILE VILLAGE,Income 50k-75k,Count,Households,2 +HOLIDAY MOBILE VILLAGE,Income 0-50k,Count,Households,11 +HOLIDAY MOBILE VILLAGE,Income 50k-100k,Count,Households,4 +HOLIDAY MOBILE VILLAGE,Income 100k-150k,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Mortgage Total,Count,Households,2 +HOLIDAY MOBILE VILLAGE,Mortgage Over 30% Income,Count,Households,0 +HOLIDAY MOBILE VILLAGE,Mortgage Over 50% Income,Count,Households,0 +HOLIDAY MOBILE VILLAGE,No Mortgage Total,Count,Households,2 +HOLIDAY MOBILE VILLAGE,No Mortgage Over 30% Income,Count,Households,1 +HOLIDAY MOBILE VILLAGE,No Mortgage Over 50% Income,Count,Households,1 +HOLIDAY MOBILE VILLAGE,Rent Total,Count,Households,12 +HOLIDAY MOBILE VILLAGE,Rent Over 30% Income,Count,Households,6 +HOLIDAY MOBILE VILLAGE,Rent Over 50% Income,Count,Households,4 +HOLIDAY MOBILE VILLAGE,Average Household Size,Hh Weighted,Household Weighted,2.86 +HOLIDAY MOBILE VILLAGE,Median Household Income,Hh Weighted,Household Weighted,38491 +HOLIDAY MOBILE VILLAGE,Per Capita Income,Pop Weighted,Population Weighted,16707 +HOLIDAY MOBILE VILLAGE,Housing Costs Over 30% Income,Percent,Households,44.88 +HOLIDAY MOBILE VILLAGE,Housing Costs Over 50% Income,Percent,Households,28.55 +HOOD WATER MAINTENCE DIST [SWS],Population Total,Count,Population,1 +HOOD WATER MAINTENCE DIST [SWS],Hispanic / Latino,Count,Population,1 +HOOD WATER MAINTENCE DIST [SWS],White,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Black-/ African American,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Native American,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Asian,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Pacific Islander,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Other / Multiple,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Hispanic / Latino,Percent,Population,69.19 +HOOD WATER MAINTENCE DIST [SWS],White,Percent,Population,28.71 +HOOD WATER MAINTENCE DIST [SWS],Black-/ African American,Percent,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Native American,Percent,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Asian,Percent,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Pacific Islander,Percent,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Other / Multiple,Percent,Population,2.1 +HOOD WATER MAINTENCE DIST [SWS],Poverty Total Assessed,Count,Population,1 +HOOD WATER MAINTENCE DIST [SWS],Poverty Below,Count,Population,0 +HOOD WATER MAINTENCE DIST [SWS],Poverty Above,Count,Population,1 +HOOD WATER MAINTENCE DIST [SWS],Poverty Rate,Percent,Population,17.42 +HOOD WATER MAINTENCE DIST [SWS],Households Total,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income Below 10k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 10k-15k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 15k-20k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 20k-25k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 25k-30k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 30k-35k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 35k-40k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 40k-45k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 45k-50k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 50k-60k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 60k-75k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 75k-100k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 100k-125k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 125k-150k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 150k-200k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income Above 200k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 0-25k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 25k-50k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 50k-75k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 0-50k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 50k-100k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Income 100k-150k,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Mortgage Total,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Mortgage Over 30% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Mortgage Over 50% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],No Mortgage Total,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],No Mortgage Over 30% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],No Mortgage Over 50% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Rent Total,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Rent Over 30% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Rent Over 50% Income,Count,Households,0 +HOOD WATER MAINTENCE DIST [SWS],Average Household Size,Hh Weighted,Household Weighted,2.55 +HOOD WATER MAINTENCE DIST [SWS],Median Household Income,Hh Weighted,Household Weighted,56250 +HOOD WATER MAINTENCE DIST [SWS],Per Capita Income,Pop Weighted,Population Weighted,23510 +HOOD WATER MAINTENCE DIST [SWS],Housing Costs Over 30% Income,Percent,Households,45.66 +HOOD WATER MAINTENCE DIST [SWS],Housing Costs Over 50% Income,Percent,Households,25.57 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Population Total,Count,Population,209 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Hispanic / Latino,Count,Population,52 +IMPERIAL MANOR MOBILEHOME COMMUNITY,White,Count,Population,129 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Black-/ African American,Count,Population,1 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Native American,Count,Population,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Asian,Count,Population,6 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Pacific Islander,Count,Population,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Other / Multiple,Count,Population,21 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Hispanic / Latino,Percent,Population,24.93 +IMPERIAL MANOR MOBILEHOME COMMUNITY,White,Percent,Population,61.63 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Black-/ African American,Percent,Population,0.45 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Native American,Percent,Population,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Asian,Percent,Population,2.93 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Pacific Islander,Percent,Population,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Other / Multiple,Percent,Population,10.05 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Poverty Total Assessed,Count,Population,209 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Poverty Below,Count,Population,45 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Poverty Above,Count,Population,164 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Poverty Rate,Percent,Population,21.48 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Households Total,Count,Households,124 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income Below 10k,Count,Households,4 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 10k-15k,Count,Households,26 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 15k-20k,Count,Households,18 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 20k-25k,Count,Households,3 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 25k-30k,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 30k-35k,Count,Households,16 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 35k-40k,Count,Households,7 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 40k-45k,Count,Households,5 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 45k-50k,Count,Households,6 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 50k-60k,Count,Households,1 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 60k-75k,Count,Households,4 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 75k-100k,Count,Households,29 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 100k-125k,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 125k-150k,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 150k-200k,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income Above 200k,Count,Households,6 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 0-25k,Count,Households,51 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 25k-50k,Count,Households,34 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 50k-75k,Count,Households,5 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 0-50k,Count,Households,84 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 50k-100k,Count,Households,34 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Income 100k-150k,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Mortgage Total,Count,Households,9 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Mortgage Over 30% Income,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Mortgage Over 50% Income,Count,Households,0 +IMPERIAL MANOR MOBILEHOME COMMUNITY,No Mortgage Total,Count,Households,89 +IMPERIAL MANOR MOBILEHOME COMMUNITY,No Mortgage Over 30% Income,Count,Households,37 +IMPERIAL MANOR MOBILEHOME COMMUNITY,No Mortgage Over 50% Income,Count,Households,34 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Rent Total,Count,Households,27 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Rent Over 30% Income,Count,Households,27 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Rent Over 50% Income,Count,Households,22 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Average Household Size,Hh Weighted,Household Weighted,1.6803625908618791 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Median Household Income,Hh Weighted,Household Weighted,31831.837612603995 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Per Capita Income,Pop Weighted,Population Weighted,32878.16681958172 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Housing Costs Over 30% Income,Percent,Households,50.97 +IMPERIAL MANOR MOBILEHOME COMMUNITY,Housing Costs Over 50% Income,Percent,Households,45.07 +KORTHS PIRATES LAIR,Population Total,Count,Population,0 +KORTHS PIRATES LAIR,Hispanic / Latino,Count,Population,0 +KORTHS PIRATES LAIR,White,Count,Population,0 +KORTHS PIRATES LAIR,Black-/ African American,Count,Population,0 +KORTHS PIRATES LAIR,Native American,Count,Population,0 +KORTHS PIRATES LAIR,Asian,Count,Population,0 +KORTHS PIRATES LAIR,Pacific Islander,Count,Population,0 +KORTHS PIRATES LAIR,Other / Multiple,Count,Population,0 +KORTHS PIRATES LAIR,Hispanic / Latino,Percent,Population,3.9 +KORTHS PIRATES LAIR,White,Percent,Population,89.23 +KORTHS PIRATES LAIR,Black-/ African American,Percent,Population,3.23 +KORTHS PIRATES LAIR,Native American,Percent,Population,0 +KORTHS PIRATES LAIR,Asian,Percent,Population,0 +KORTHS PIRATES LAIR,Pacific Islander,Percent,Population,0 +KORTHS PIRATES LAIR,Other / Multiple,Percent,Population,3.63 +KORTHS PIRATES LAIR,Poverty Total Assessed,Count,Population,0 +KORTHS PIRATES LAIR,Poverty Below,Count,Population,0 +KORTHS PIRATES LAIR,Poverty Above,Count,Population,0 +KORTHS PIRATES LAIR,Poverty Rate,Percent,Population,35.94 +KORTHS PIRATES LAIR,Households Total,Count,Households,0 +KORTHS PIRATES LAIR,Income Below 10k,Count,Households,0 +KORTHS PIRATES LAIR,Income 10k-15k,Count,Households,0 +KORTHS PIRATES LAIR,Income 15k-20k,Count,Households,0 +KORTHS PIRATES LAIR,Income 20k-25k,Count,Households,0 +KORTHS PIRATES LAIR,Income 25k-30k,Count,Households,0 +KORTHS PIRATES LAIR,Income 30k-35k,Count,Households,0 +KORTHS PIRATES LAIR,Income 35k-40k,Count,Households,0 +KORTHS PIRATES LAIR,Income 40k-45k,Count,Households,0 +KORTHS PIRATES LAIR,Income 45k-50k,Count,Households,0 +KORTHS PIRATES LAIR,Income 50k-60k,Count,Households,0 +KORTHS PIRATES LAIR,Income 60k-75k,Count,Households,0 +KORTHS PIRATES LAIR,Income 75k-100k,Count,Households,0 +KORTHS PIRATES LAIR,Income 100k-125k,Count,Households,0 +KORTHS PIRATES LAIR,Income 125k-150k,Count,Households,0 +KORTHS PIRATES LAIR,Income 150k-200k,Count,Households,0 +KORTHS PIRATES LAIR,Income Above 200k,Count,Households,0 +KORTHS PIRATES LAIR,Income 0-25k,Count,Households,0 +KORTHS PIRATES LAIR,Income 25k-50k,Count,Households,0 +KORTHS PIRATES LAIR,Income 50k-75k,Count,Households,0 +KORTHS PIRATES LAIR,Income 0-50k,Count,Households,0 +KORTHS PIRATES LAIR,Income 50k-100k,Count,Households,0 +KORTHS PIRATES LAIR,Income 100k-150k,Count,Households,0 +KORTHS PIRATES LAIR,Mortgage Total,Count,Households,0 +KORTHS PIRATES LAIR,Mortgage Over 30% Income,Count,Households,0 +KORTHS PIRATES LAIR,Mortgage Over 50% Income,Count,Households,0 +KORTHS PIRATES LAIR,No Mortgage Total,Count,Households,0 +KORTHS PIRATES LAIR,No Mortgage Over 30% Income,Count,Households,0 +KORTHS PIRATES LAIR,No Mortgage Over 50% Income,Count,Households,0 +KORTHS PIRATES LAIR,Rent Total,Count,Households,0 +KORTHS PIRATES LAIR,Rent Over 30% Income,Count,Households,0 +KORTHS PIRATES LAIR,Rent Over 50% Income,Count,Households,0 +KORTHS PIRATES LAIR,Average Household Size,Hh Weighted,Household Weighted,1.79 +KORTHS PIRATES LAIR,Median Household Income,Hh Weighted,Household Weighted,38125 +KORTHS PIRATES LAIR,Per Capita Income,Pop Weighted,Population Weighted,33103 +KORTHS PIRATES LAIR,Housing Costs Over 30% Income,Percent,Households,28.02 +KORTHS PIRATES LAIR,Housing Costs Over 50% Income,Percent,Households,23.19 +LAGUNA DEL SOL INC,Population Total,Count,Population,24 +LAGUNA DEL SOL INC,Hispanic / Latino,Count,Population,5 +LAGUNA DEL SOL INC,White,Count,Population,18 +LAGUNA DEL SOL INC,Black-/ African American,Count,Population,0 +LAGUNA DEL SOL INC,Native American,Count,Population,0 +LAGUNA DEL SOL INC,Asian,Count,Population,0 +LAGUNA DEL SOL INC,Pacific Islander,Count,Population,0 +LAGUNA DEL SOL INC,Other / Multiple,Count,Population,0 +LAGUNA DEL SOL INC,Hispanic / Latino,Percent,Population,21.55 +LAGUNA DEL SOL INC,White,Percent,Population,75.2 +LAGUNA DEL SOL INC,Black-/ African American,Percent,Population,0 +LAGUNA DEL SOL INC,Native American,Percent,Population,0.67 +LAGUNA DEL SOL INC,Asian,Percent,Population,1.46 +LAGUNA DEL SOL INC,Pacific Islander,Percent,Population,0 +LAGUNA DEL SOL INC,Other / Multiple,Percent,Population,1.12 +LAGUNA DEL SOL INC,Poverty Total Assessed,Count,Population,24 +LAGUNA DEL SOL INC,Poverty Below,Count,Population,2 +LAGUNA DEL SOL INC,Poverty Above,Count,Population,22 +LAGUNA DEL SOL INC,Poverty Rate,Percent,Population,6.4 +LAGUNA DEL SOL INC,Households Total,Count,Households,9 +LAGUNA DEL SOL INC,Income Below 10k,Count,Households,0 +LAGUNA DEL SOL INC,Income 10k-15k,Count,Households,1 +LAGUNA DEL SOL INC,Income 15k-20k,Count,Households,1 +LAGUNA DEL SOL INC,Income 20k-25k,Count,Households,0 +LAGUNA DEL SOL INC,Income 25k-30k,Count,Households,0 +LAGUNA DEL SOL INC,Income 30k-35k,Count,Households,0 +LAGUNA DEL SOL INC,Income 35k-40k,Count,Households,0 +LAGUNA DEL SOL INC,Income 40k-45k,Count,Households,0 +LAGUNA DEL SOL INC,Income 45k-50k,Count,Households,0 +LAGUNA DEL SOL INC,Income 50k-60k,Count,Households,0 +LAGUNA DEL SOL INC,Income 60k-75k,Count,Households,0 +LAGUNA DEL SOL INC,Income 75k-100k,Count,Households,2 +LAGUNA DEL SOL INC,Income 100k-125k,Count,Households,0 +LAGUNA DEL SOL INC,Income 125k-150k,Count,Households,0 +LAGUNA DEL SOL INC,Income 150k-200k,Count,Households,1 +LAGUNA DEL SOL INC,Income Above 200k,Count,Households,2 +LAGUNA DEL SOL INC,Income 0-25k,Count,Households,2 +LAGUNA DEL SOL INC,Income 25k-50k,Count,Households,1 +LAGUNA DEL SOL INC,Income 50k-75k,Count,Households,0 +LAGUNA DEL SOL INC,Income 0-50k,Count,Households,3 +LAGUNA DEL SOL INC,Income 50k-100k,Count,Households,2 +LAGUNA DEL SOL INC,Income 100k-150k,Count,Households,1 +LAGUNA DEL SOL INC,Mortgage Total,Count,Households,5 +LAGUNA DEL SOL INC,Mortgage Over 30% Income,Count,Households,2 +LAGUNA DEL SOL INC,Mortgage Over 50% Income,Count,Households,2 +LAGUNA DEL SOL INC,No Mortgage Total,Count,Households,3 +LAGUNA DEL SOL INC,No Mortgage Over 30% Income,Count,Households,0 +LAGUNA DEL SOL INC,No Mortgage Over 50% Income,Count,Households,0 +LAGUNA DEL SOL INC,Rent Total,Count,Households,2 +LAGUNA DEL SOL INC,Rent Over 30% Income,Count,Households,0 +LAGUNA DEL SOL INC,Rent Over 50% Income,Count,Households,0 +LAGUNA DEL SOL INC,Average Household Size,Hh Weighted,Household Weighted,2.64 +LAGUNA DEL SOL INC,Median Household Income,Hh Weighted,Household Weighted,95227 +LAGUNA DEL SOL INC,Per Capita Income,Pop Weighted,Population Weighted,50793 +LAGUNA DEL SOL INC,Housing Costs Over 30% Income,Percent,Households,23.37 +LAGUNA DEL SOL INC,Housing Costs Over 50% Income,Percent,Households,23.37 +LAGUNA VILLAGE RV PARK,Population Total,Count,Population,20 +LAGUNA VILLAGE RV PARK,Hispanic / Latino,Count,Population,3 +LAGUNA VILLAGE RV PARK,White,Count,Population,2 +LAGUNA VILLAGE RV PARK,Black-/ African American,Count,Population,1 +LAGUNA VILLAGE RV PARK,Native American,Count,Population,0 +LAGUNA VILLAGE RV PARK,Asian,Count,Population,11 +LAGUNA VILLAGE RV PARK,Pacific Islander,Count,Population,2 +LAGUNA VILLAGE RV PARK,Other / Multiple,Count,Population,2 +LAGUNA VILLAGE RV PARK,Hispanic / Latino,Percent,Population,12.79 +LAGUNA VILLAGE RV PARK,White,Percent,Population,8.48 +LAGUNA VILLAGE RV PARK,Black-/ African American,Percent,Population,7.28 +LAGUNA VILLAGE RV PARK,Native American,Percent,Population,0 +LAGUNA VILLAGE RV PARK,Asian,Percent,Population,52.62 +LAGUNA VILLAGE RV PARK,Pacific Islander,Percent,Population,8.38 +LAGUNA VILLAGE RV PARK,Other / Multiple,Percent,Population,10.45 +LAGUNA VILLAGE RV PARK,Poverty Total Assessed,Count,Population,20 +LAGUNA VILLAGE RV PARK,Poverty Below,Count,Population,2 +LAGUNA VILLAGE RV PARK,Poverty Above,Count,Population,18 +LAGUNA VILLAGE RV PARK,Poverty Rate,Percent,Population,11.79 +LAGUNA VILLAGE RV PARK,Households Total,Count,Households,7 +LAGUNA VILLAGE RV PARK,Income Below 10k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 10k-15k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 15k-20k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 20k-25k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 25k-30k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 30k-35k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 35k-40k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 40k-45k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 45k-50k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 50k-60k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 60k-75k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 75k-100k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 100k-125k,Count,Households,0 +LAGUNA VILLAGE RV PARK,Income 125k-150k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 150k-200k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income Above 200k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 0-25k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 25k-50k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 50k-75k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Income 0-50k,Count,Households,2 +LAGUNA VILLAGE RV PARK,Income 50k-100k,Count,Households,2 +LAGUNA VILLAGE RV PARK,Income 100k-150k,Count,Households,1 +LAGUNA VILLAGE RV PARK,Mortgage Total,Count,Households,3 +LAGUNA VILLAGE RV PARK,Mortgage Over 30% Income,Count,Households,1 +LAGUNA VILLAGE RV PARK,Mortgage Over 50% Income,Count,Households,0 +LAGUNA VILLAGE RV PARK,No Mortgage Total,Count,Households,1 +LAGUNA VILLAGE RV PARK,No Mortgage Over 30% Income,Count,Households,0 +LAGUNA VILLAGE RV PARK,No Mortgage Over 50% Income,Count,Households,0 +LAGUNA VILLAGE RV PARK,Rent Total,Count,Households,3 +LAGUNA VILLAGE RV PARK,Rent Over 30% Income,Count,Households,1 +LAGUNA VILLAGE RV PARK,Rent Over 50% Income,Count,Households,0 +LAGUNA VILLAGE RV PARK,Average Household Size,Hh Weighted,Household Weighted,3.03 +LAGUNA VILLAGE RV PARK,Median Household Income,Hh Weighted,Household Weighted,84332 +LAGUNA VILLAGE RV PARK,Per Capita Income,Pop Weighted,Population Weighted,32668 +LAGUNA VILLAGE RV PARK,Housing Costs Over 30% Income,Percent,Households,32.52 +LAGUNA VILLAGE RV PARK,Housing Costs Over 50% Income,Percent,Households,12.26 +LINCOLN CHAN-HOME RANCH,Population Total,Count,Population,4 +LINCOLN CHAN-HOME RANCH,Hispanic / Latino,Count,Population,2 +LINCOLN CHAN-HOME RANCH,White,Count,Population,2 +LINCOLN CHAN-HOME RANCH,Black-/ African American,Count,Population,0 +LINCOLN CHAN-HOME RANCH,Native American,Count,Population,0 +LINCOLN CHAN-HOME RANCH,Asian,Count,Population,0 +LINCOLN CHAN-HOME RANCH,Pacific Islander,Count,Population,0 +LINCOLN CHAN-HOME RANCH,Other / Multiple,Count,Population,0 +LINCOLN CHAN-HOME RANCH,Hispanic / Latino,Percent,Population,44.6 +LINCOLN CHAN-HOME RANCH,White,Percent,Population,45.84 +LINCOLN CHAN-HOME RANCH,Black-/ African American,Percent,Population,0 +LINCOLN CHAN-HOME RANCH,Native American,Percent,Population,0 +LINCOLN CHAN-HOME RANCH,Asian,Percent,Population,5.93 +LINCOLN CHAN-HOME RANCH,Pacific Islander,Percent,Population,0 +LINCOLN CHAN-HOME RANCH,Other / Multiple,Percent,Population,3.63 +LINCOLN CHAN-HOME RANCH,Poverty Total Assessed,Count,Population,4 +LINCOLN CHAN-HOME RANCH,Poverty Below,Count,Population,1 +LINCOLN CHAN-HOME RANCH,Poverty Above,Count,Population,3 +LINCOLN CHAN-HOME RANCH,Poverty Rate,Percent,Population,15.75 +LINCOLN CHAN-HOME RANCH,Households Total,Count,Households,2 +LINCOLN CHAN-HOME RANCH,Income Below 10k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 10k-15k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 15k-20k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 20k-25k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 25k-30k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 30k-35k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 35k-40k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 40k-45k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 45k-50k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 50k-60k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 60k-75k,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Income 75k-100k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 100k-125k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 125k-150k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 150k-200k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income Above 200k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 0-25k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 25k-50k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Income 50k-75k,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Income 0-50k,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Income 50k-100k,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Income 100k-150k,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Mortgage Total,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Mortgage Over 30% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Mortgage Over 50% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,No Mortgage Total,Count,Households,0 +LINCOLN CHAN-HOME RANCH,No Mortgage Over 30% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,No Mortgage Over 50% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Rent Total,Count,Households,1 +LINCOLN CHAN-HOME RANCH,Rent Over 30% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Rent Over 50% Income,Count,Households,0 +LINCOLN CHAN-HOME RANCH,Average Household Size,Hh Weighted,Household Weighted,2.49 +LINCOLN CHAN-HOME RANCH,Median Household Income,Hh Weighted,Household Weighted,68248 +LINCOLN CHAN-HOME RANCH,Per Capita Income,Pop Weighted,Population Weighted,38950 +LINCOLN CHAN-HOME RANCH,Housing Costs Over 30% Income,Percent,Households,24.49 +LINCOLN CHAN-HOME RANCH,Housing Costs Over 50% Income,Percent,Households,14.65 +LOCKE WATER WORKS CO [SWS],Population Total,Count,Population,1 +LOCKE WATER WORKS CO [SWS],Hispanic / Latino,Count,Population,0 +LOCKE WATER WORKS CO [SWS],White,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Black-/ African American,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Native American,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Asian,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Pacific Islander,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Other / Multiple,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Hispanic / Latino,Percent,Population,44.6 +LOCKE WATER WORKS CO [SWS],White,Percent,Population,45.84 +LOCKE WATER WORKS CO [SWS],Black-/ African American,Percent,Population,0 +LOCKE WATER WORKS CO [SWS],Native American,Percent,Population,0 +LOCKE WATER WORKS CO [SWS],Asian,Percent,Population,5.93 +LOCKE WATER WORKS CO [SWS],Pacific Islander,Percent,Population,0 +LOCKE WATER WORKS CO [SWS],Other / Multiple,Percent,Population,3.63 +LOCKE WATER WORKS CO [SWS],Poverty Total Assessed,Count,Population,1 +LOCKE WATER WORKS CO [SWS],Poverty Below,Count,Population,0 +LOCKE WATER WORKS CO [SWS],Poverty Above,Count,Population,1 +LOCKE WATER WORKS CO [SWS],Poverty Rate,Percent,Population,15.75 +LOCKE WATER WORKS CO [SWS],Households Total,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income Below 10k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 10k-15k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 15k-20k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 20k-25k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 25k-30k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 30k-35k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 35k-40k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 40k-45k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 45k-50k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 50k-60k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 60k-75k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 75k-100k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 100k-125k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 125k-150k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 150k-200k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income Above 200k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 0-25k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 25k-50k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 50k-75k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 0-50k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 50k-100k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Income 100k-150k,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Mortgage Total,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Mortgage Over 30% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Mortgage Over 50% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],No Mortgage Total,Count,Households,0 +LOCKE WATER WORKS CO [SWS],No Mortgage Over 30% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],No Mortgage Over 50% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Rent Total,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Rent Over 30% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Rent Over 50% Income,Count,Households,0 +LOCKE WATER WORKS CO [SWS],Average Household Size,Hh Weighted,Household Weighted,2.49 +LOCKE WATER WORKS CO [SWS],Median Household Income,Hh Weighted,Household Weighted,68248 +LOCKE WATER WORKS CO [SWS],Per Capita Income,Pop Weighted,Population Weighted,38950 +LOCKE WATER WORKS CO [SWS],Housing Costs Over 30% Income,Percent,Households,24.49 +LOCKE WATER WORKS CO [SWS],Housing Costs Over 50% Income,Percent,Households,14.65 +MAGNOLIA MUTUAL WATER,Population Total,Count,Population,1 +MAGNOLIA MUTUAL WATER,Hispanic / Latino,Count,Population,0 +MAGNOLIA MUTUAL WATER,White,Count,Population,0 +MAGNOLIA MUTUAL WATER,Black-/ African American,Count,Population,0 +MAGNOLIA MUTUAL WATER,Native American,Count,Population,0 +MAGNOLIA MUTUAL WATER,Asian,Count,Population,0 +MAGNOLIA MUTUAL WATER,Pacific Islander,Count,Population,0 +MAGNOLIA MUTUAL WATER,Other / Multiple,Count,Population,0 +MAGNOLIA MUTUAL WATER,Hispanic / Latino,Percent,Population,44.6 +MAGNOLIA MUTUAL WATER,White,Percent,Population,45.84 +MAGNOLIA MUTUAL WATER,Black-/ African American,Percent,Population,0 +MAGNOLIA MUTUAL WATER,Native American,Percent,Population,0 +MAGNOLIA MUTUAL WATER,Asian,Percent,Population,5.93 +MAGNOLIA MUTUAL WATER,Pacific Islander,Percent,Population,0 +MAGNOLIA MUTUAL WATER,Other / Multiple,Percent,Population,3.63 +MAGNOLIA MUTUAL WATER,Poverty Total Assessed,Count,Population,1 +MAGNOLIA MUTUAL WATER,Poverty Below,Count,Population,0 +MAGNOLIA MUTUAL WATER,Poverty Above,Count,Population,1 +MAGNOLIA MUTUAL WATER,Poverty Rate,Percent,Population,15.75 +MAGNOLIA MUTUAL WATER,Households Total,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income Below 10k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 10k-15k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 15k-20k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 20k-25k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 25k-30k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 30k-35k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 35k-40k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 40k-45k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 45k-50k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 50k-60k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 60k-75k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 75k-100k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 100k-125k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 125k-150k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 150k-200k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income Above 200k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 0-25k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 25k-50k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 50k-75k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 0-50k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 50k-100k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Income 100k-150k,Count,Households,0 +MAGNOLIA MUTUAL WATER,Mortgage Total,Count,Households,0 +MAGNOLIA MUTUAL WATER,Mortgage Over 30% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,Mortgage Over 50% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,No Mortgage Total,Count,Households,0 +MAGNOLIA MUTUAL WATER,No Mortgage Over 30% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,No Mortgage Over 50% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,Rent Total,Count,Households,0 +MAGNOLIA MUTUAL WATER,Rent Over 30% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,Rent Over 50% Income,Count,Households,0 +MAGNOLIA MUTUAL WATER,Average Household Size,Hh Weighted,Household Weighted,2.49 +MAGNOLIA MUTUAL WATER,Median Household Income,Hh Weighted,Household Weighted,68248 +MAGNOLIA MUTUAL WATER,Per Capita Income,Pop Weighted,Population Weighted,38950 +MAGNOLIA MUTUAL WATER,Housing Costs Over 30% Income,Percent,Households,24.49 +MAGNOLIA MUTUAL WATER,Housing Costs Over 50% Income,Percent,Households,14.65 +MC CLELLAN MHP,Population Total,Count,Population,269 +MC CLELLAN MHP,Hispanic / Latino,Count,Population,52 +MC CLELLAN MHP,White,Count,Population,108 +MC CLELLAN MHP,Black-/ African American,Count,Population,65 +MC CLELLAN MHP,Native American,Count,Population,0 +MC CLELLAN MHP,Asian,Count,Population,43 +MC CLELLAN MHP,Pacific Islander,Count,Population,0 +MC CLELLAN MHP,Other / Multiple,Count,Population,2 +MC CLELLAN MHP,Hispanic / Latino,Percent,Population,19.27 +MC CLELLAN MHP,White,Percent,Population,40.19 +MC CLELLAN MHP,Black-/ African American,Percent,Population,24.01 +MC CLELLAN MHP,Native American,Percent,Population,0 +MC CLELLAN MHP,Asian,Percent,Population,15.91 +MC CLELLAN MHP,Pacific Islander,Percent,Population,0 +MC CLELLAN MHP,Other / Multiple,Percent,Population,0.62 +MC CLELLAN MHP,Poverty Total Assessed,Count,Population,269 +MC CLELLAN MHP,Poverty Below,Count,Population,101 +MC CLELLAN MHP,Poverty Above,Count,Population,168 +MC CLELLAN MHP,Poverty Rate,Percent,Population,37.48 +MC CLELLAN MHP,Households Total,Count,Households,82 +MC CLELLAN MHP,Income Below 10k,Count,Households,8 +MC CLELLAN MHP,Income 10k-15k,Count,Households,2 +MC CLELLAN MHP,Income 15k-20k,Count,Households,3 +MC CLELLAN MHP,Income 20k-25k,Count,Households,7 +MC CLELLAN MHP,Income 25k-30k,Count,Households,11 +MC CLELLAN MHP,Income 30k-35k,Count,Households,2 +MC CLELLAN MHP,Income 35k-40k,Count,Households,2 +MC CLELLAN MHP,Income 40k-45k,Count,Households,1 +MC CLELLAN MHP,Income 45k-50k,Count,Households,3 +MC CLELLAN MHP,Income 50k-60k,Count,Households,1 +MC CLELLAN MHP,Income 60k-75k,Count,Households,15 +MC CLELLAN MHP,Income 75k-100k,Count,Households,20 +MC CLELLAN MHP,Income 100k-125k,Count,Households,3 +MC CLELLAN MHP,Income 125k-150k,Count,Households,0 +MC CLELLAN MHP,Income 150k-200k,Count,Households,3 +MC CLELLAN MHP,Income Above 200k,Count,Households,0 +MC CLELLAN MHP,Income 0-25k,Count,Households,20 +MC CLELLAN MHP,Income 25k-50k,Count,Households,19 +MC CLELLAN MHP,Income 50k-75k,Count,Households,17 +MC CLELLAN MHP,Income 0-50k,Count,Households,39 +MC CLELLAN MHP,Income 50k-100k,Count,Households,36 +MC CLELLAN MHP,Income 100k-150k,Count,Households,3 +MC CLELLAN MHP,Mortgage Total,Count,Households,9 +MC CLELLAN MHP,Mortgage Over 30% Income,Count,Households,4 +MC CLELLAN MHP,Mortgage Over 50% Income,Count,Households,2 +MC CLELLAN MHP,No Mortgage Total,Count,Households,25 +MC CLELLAN MHP,No Mortgage Over 30% Income,Count,Households,1 +MC CLELLAN MHP,No Mortgage Over 50% Income,Count,Households,1 +MC CLELLAN MHP,Rent Total,Count,Households,48 +MC CLELLAN MHP,Rent Over 30% Income,Count,Households,34 +MC CLELLAN MHP,Rent Over 50% Income,Count,Households,27 +MC CLELLAN MHP,Average Household Size,Hh Weighted,Household Weighted,3.28 +MC CLELLAN MHP,Median Household Income,Hh Weighted,Household Weighted,60521 +MC CLELLAN MHP,Per Capita Income,Pop Weighted,Population Weighted,18213 +MC CLELLAN MHP,Housing Costs Over 30% Income,Percent,Households,46.85 +MC CLELLAN MHP,Housing Costs Over 50% Income,Percent,Households,35.36 +OLYMPIA MOBILODGE,Population Total,Count,Population,290 +OLYMPIA MOBILODGE,Hispanic / Latino,Count,Population,70 +OLYMPIA MOBILODGE,White,Count,Population,81 +OLYMPIA MOBILODGE,Black-/ African American,Count,Population,18 +OLYMPIA MOBILODGE,Native American,Count,Population,0 +OLYMPIA MOBILODGE,Asian,Count,Population,101 +OLYMPIA MOBILODGE,Pacific Islander,Count,Population,16 +OLYMPIA MOBILODGE,Other / Multiple,Count,Population,3 +OLYMPIA MOBILODGE,Hispanic / Latino,Percent,Population,24.12 +OLYMPIA MOBILODGE,White,Percent,Population,28.03 +OLYMPIA MOBILODGE,Black-/ African American,Percent,Population,6.3 +OLYMPIA MOBILODGE,Native American,Percent,Population,0 +OLYMPIA MOBILODGE,Asian,Percent,Population,34.95 +OLYMPIA MOBILODGE,Pacific Islander,Percent,Population,5.53 +OLYMPIA MOBILODGE,Other / Multiple,Percent,Population,1.08 +OLYMPIA MOBILODGE,Poverty Total Assessed,Count,Population,290 +OLYMPIA MOBILODGE,Poverty Below,Count,Population,68 +OLYMPIA MOBILODGE,Poverty Above,Count,Population,222 +OLYMPIA MOBILODGE,Poverty Rate,Percent,Population,23.43 +OLYMPIA MOBILODGE,Households Total,Count,Households,114 +OLYMPIA MOBILODGE,Income Below 10k,Count,Households,11 +OLYMPIA MOBILODGE,Income 10k-15k,Count,Households,0 +OLYMPIA MOBILODGE,Income 15k-20k,Count,Households,6 +OLYMPIA MOBILODGE,Income 20k-25k,Count,Households,10 +OLYMPIA MOBILODGE,Income 25k-30k,Count,Households,9 +OLYMPIA MOBILODGE,Income 30k-35k,Count,Households,3 +OLYMPIA MOBILODGE,Income 35k-40k,Count,Households,13 +OLYMPIA MOBILODGE,Income 40k-45k,Count,Households,0 +OLYMPIA MOBILODGE,Income 45k-50k,Count,Households,0 +OLYMPIA MOBILODGE,Income 50k-60k,Count,Households,10 +OLYMPIA MOBILODGE,Income 60k-75k,Count,Households,19 +OLYMPIA MOBILODGE,Income 75k-100k,Count,Households,8 +OLYMPIA MOBILODGE,Income 100k-125k,Count,Households,3 +OLYMPIA MOBILODGE,Income 125k-150k,Count,Households,12 +OLYMPIA MOBILODGE,Income 150k-200k,Count,Households,5 +OLYMPIA MOBILODGE,Income Above 200k,Count,Households,5 +OLYMPIA MOBILODGE,Income 0-25k,Count,Households,28 +OLYMPIA MOBILODGE,Income 25k-50k,Count,Households,25 +OLYMPIA MOBILODGE,Income 50k-75k,Count,Households,29 +OLYMPIA MOBILODGE,Income 0-50k,Count,Households,53 +OLYMPIA MOBILODGE,Income 50k-100k,Count,Households,36 +OLYMPIA MOBILODGE,Income 100k-150k,Count,Households,14 +OLYMPIA MOBILODGE,Mortgage Total,Count,Households,31 +OLYMPIA MOBILODGE,Mortgage Over 30% Income,Count,Households,22 +OLYMPIA MOBILODGE,Mortgage Over 50% Income,Count,Households,10 +OLYMPIA MOBILODGE,No Mortgage Total,Count,Households,51 +OLYMPIA MOBILODGE,No Mortgage Over 30% Income,Count,Households,12 +OLYMPIA MOBILODGE,No Mortgage Over 50% Income,Count,Households,10 +OLYMPIA MOBILODGE,Rent Total,Count,Households,33 +OLYMPIA MOBILODGE,Rent Over 30% Income,Count,Households,9 +OLYMPIA MOBILODGE,Rent Over 50% Income,Count,Households,7 +OLYMPIA MOBILODGE,Average Household Size,Hh Weighted,Household Weighted,2.5100000000000002 +OLYMPIA MOBILODGE,Median Household Income,Hh Weighted,Household Weighted,53786 +OLYMPIA MOBILODGE,Per Capita Income,Pop Weighted,Population Weighted,29451 +OLYMPIA MOBILODGE,Housing Costs Over 30% Income,Percent,Households,37.35 +OLYMPIA MOBILODGE,Housing Costs Over 50% Income,Percent,Households,23.74 +ORANGE VALE WATER COMPANY,Population Total,Count,Population,17387 +ORANGE VALE WATER COMPANY,Hispanic / Latino,Count,Population,2658 +ORANGE VALE WATER COMPANY,White,Count,Population,12308 +ORANGE VALE WATER COMPANY,Black-/ African American,Count,Population,241 +ORANGE VALE WATER COMPANY,Native American,Count,Population,181 +ORANGE VALE WATER COMPANY,Asian,Count,Population,633 +ORANGE VALE WATER COMPANY,Pacific Islander,Count,Population,86 +ORANGE VALE WATER COMPANY,Other / Multiple,Count,Population,1281 +ORANGE VALE WATER COMPANY,Hispanic / Latino,Percent,Population,15.28 +ORANGE VALE WATER COMPANY,White,Percent,Population,70.79 +ORANGE VALE WATER COMPANY,Black-/ African American,Percent,Population,1.39 +ORANGE VALE WATER COMPANY,Native American,Percent,Population,1.04 +ORANGE VALE WATER COMPANY,Asian,Percent,Population,3.64 +ORANGE VALE WATER COMPANY,Pacific Islander,Percent,Population,0.49 +ORANGE VALE WATER COMPANY,Other / Multiple,Percent,Population,7.37 +ORANGE VALE WATER COMPANY,Poverty Total Assessed,Count,Population,17288 +ORANGE VALE WATER COMPANY,Poverty Below,Count,Population,1904 +ORANGE VALE WATER COMPANY,Poverty Above,Count,Population,15384 +ORANGE VALE WATER COMPANY,Poverty Rate,Percent,Population,11.01 +ORANGE VALE WATER COMPANY,Households Total,Count,Households,6595 +ORANGE VALE WATER COMPANY,Income Below 10k,Count,Households,389 +ORANGE VALE WATER COMPANY,Income 10k-15k,Count,Households,111 +ORANGE VALE WATER COMPANY,Income 15k-20k,Count,Households,61 +ORANGE VALE WATER COMPANY,Income 20k-25k,Count,Households,94 +ORANGE VALE WATER COMPANY,Income 25k-30k,Count,Households,226 +ORANGE VALE WATER COMPANY,Income 30k-35k,Count,Households,58 +ORANGE VALE WATER COMPANY,Income 35k-40k,Count,Households,274 +ORANGE VALE WATER COMPANY,Income 40k-45k,Count,Households,120 +ORANGE VALE WATER COMPANY,Income 45k-50k,Count,Households,181 +ORANGE VALE WATER COMPANY,Income 50k-60k,Count,Households,372 +ORANGE VALE WATER COMPANY,Income 60k-75k,Count,Households,752 +ORANGE VALE WATER COMPANY,Income 75k-100k,Count,Households,990 +ORANGE VALE WATER COMPANY,Income 100k-125k,Count,Households,901 +ORANGE VALE WATER COMPANY,Income 125k-150k,Count,Households,626 +ORANGE VALE WATER COMPANY,Income 150k-200k,Count,Households,678 +ORANGE VALE WATER COMPANY,Income Above 200k,Count,Households,766 +ORANGE VALE WATER COMPANY,Income 0-25k,Count,Households,655 +ORANGE VALE WATER COMPANY,Income 25k-50k,Count,Households,858 +ORANGE VALE WATER COMPANY,Income 50k-75k,Count,Households,1123 +ORANGE VALE WATER COMPANY,Income 0-50k,Count,Households,1512 +ORANGE VALE WATER COMPANY,Income 50k-100k,Count,Households,2113 +ORANGE VALE WATER COMPANY,Income 100k-150k,Count,Households,1526 +ORANGE VALE WATER COMPANY,Mortgage Total,Count,Households,3246 +ORANGE VALE WATER COMPANY,Mortgage Over 30% Income,Count,Households,1021 +ORANGE VALE WATER COMPANY,Mortgage Over 50% Income,Count,Households,453 +ORANGE VALE WATER COMPANY,No Mortgage Total,Count,Households,1686 +ORANGE VALE WATER COMPANY,No Mortgage Over 30% Income,Count,Households,315 +ORANGE VALE WATER COMPANY,No Mortgage Over 50% Income,Count,Households,185 +ORANGE VALE WATER COMPANY,Rent Total,Count,Households,1663 +ORANGE VALE WATER COMPANY,Rent Over 30% Income,Count,Households,693 +ORANGE VALE WATER COMPANY,Rent Over 50% Income,Count,Households,305 +ORANGE VALE WATER COMPANY,Average Household Size,Hh Weighted,Household Weighted,2.608348457768683 +ORANGE VALE WATER COMPANY,Median Household Income,Hh Weighted,Household Weighted,92693.71491876646 +ORANGE VALE WATER COMPANY,Per Capita Income,Pop Weighted,Population Weighted,42509.89363050402 +ORANGE VALE WATER COMPANY,Housing Costs Over 30% Income,Percent,Households,30.77 +ORANGE VALE WATER COMPANY,Housing Costs Over 50% Income,Percent,Households,14.29 +PLANTATION MOBILE HOME PARK,Population Total,Count,Population,10 +PLANTATION MOBILE HOME PARK,Hispanic / Latino,Count,Population,4 +PLANTATION MOBILE HOME PARK,White,Count,Population,1 +PLANTATION MOBILE HOME PARK,Black-/ African American,Count,Population,1 +PLANTATION MOBILE HOME PARK,Native American,Count,Population,0 +PLANTATION MOBILE HOME PARK,Asian,Count,Population,3 +PLANTATION MOBILE HOME PARK,Pacific Islander,Count,Population,0 +PLANTATION MOBILE HOME PARK,Other / Multiple,Count,Population,1 +PLANTATION MOBILE HOME PARK,Hispanic / Latino,Percent,Population,38.66 +PLANTATION MOBILE HOME PARK,White,Percent,Population,15.12 +PLANTATION MOBILE HOME PARK,Black-/ African American,Percent,Population,7.1 +PLANTATION MOBILE HOME PARK,Native American,Percent,Population,0 +PLANTATION MOBILE HOME PARK,Asian,Percent,Population,32.49 +PLANTATION MOBILE HOME PARK,Pacific Islander,Percent,Population,0 +PLANTATION MOBILE HOME PARK,Other / Multiple,Percent,Population,6.64 +PLANTATION MOBILE HOME PARK,Poverty Total Assessed,Count,Population,10 +PLANTATION MOBILE HOME PARK,Poverty Below,Count,Population,2 +PLANTATION MOBILE HOME PARK,Poverty Above,Count,Population,7 +PLANTATION MOBILE HOME PARK,Poverty Rate,Percent,Population,22.33 +PLANTATION MOBILE HOME PARK,Households Total,Count,Households,3 +PLANTATION MOBILE HOME PARK,Income Below 10k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 10k-15k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 15k-20k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 20k-25k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 25k-30k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 30k-35k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 35k-40k,Count,Households,1 +PLANTATION MOBILE HOME PARK,Income 40k-45k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 45k-50k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 50k-60k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 60k-75k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 75k-100k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 100k-125k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 125k-150k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 150k-200k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income Above 200k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 0-25k,Count,Households,1 +PLANTATION MOBILE HOME PARK,Income 25k-50k,Count,Households,1 +PLANTATION MOBILE HOME PARK,Income 50k-75k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Income 0-50k,Count,Households,2 +PLANTATION MOBILE HOME PARK,Income 50k-100k,Count,Households,1 +PLANTATION MOBILE HOME PARK,Income 100k-150k,Count,Households,0 +PLANTATION MOBILE HOME PARK,Mortgage Total,Count,Households,1 +PLANTATION MOBILE HOME PARK,Mortgage Over 30% Income,Count,Households,0 +PLANTATION MOBILE HOME PARK,Mortgage Over 50% Income,Count,Households,0 +PLANTATION MOBILE HOME PARK,No Mortgage Total,Count,Households,0 +PLANTATION MOBILE HOME PARK,No Mortgage Over 30% Income,Count,Households,0 +PLANTATION MOBILE HOME PARK,No Mortgage Over 50% Income,Count,Households,0 +PLANTATION MOBILE HOME PARK,Rent Total,Count,Households,2 +PLANTATION MOBILE HOME PARK,Rent Over 30% Income,Count,Households,1 +PLANTATION MOBILE HOME PARK,Rent Over 50% Income,Count,Households,1 +PLANTATION MOBILE HOME PARK,Average Household Size,Hh Weighted,Household Weighted,2.86 +PLANTATION MOBILE HOME PARK,Median Household Income,Hh Weighted,Household Weighted,38491 +PLANTATION MOBILE HOME PARK,Per Capita Income,Pop Weighted,Population Weighted,16707 +PLANTATION MOBILE HOME PARK,Housing Costs Over 30% Income,Percent,Households,44.88 +PLANTATION MOBILE HOME PARK,Housing Costs Over 50% Income,Percent,Households,28.55 +RANCHO MARINA,Population Total,Count,Population,0 +RANCHO MARINA,Hispanic / Latino,Count,Population,0 +RANCHO MARINA,White,Count,Population,0 +RANCHO MARINA,Black-/ African American,Count,Population,0 +RANCHO MARINA,Native American,Count,Population,0 +RANCHO MARINA,Asian,Count,Population,0 +RANCHO MARINA,Pacific Islander,Count,Population,0 +RANCHO MARINA,Other / Multiple,Count,Population,0 +RANCHO MARINA,Hispanic / Latino,Percent,Population,3.9 +RANCHO MARINA,White,Percent,Population,89.23 +RANCHO MARINA,Black-/ African American,Percent,Population,3.23 +RANCHO MARINA,Native American,Percent,Population,0 +RANCHO MARINA,Asian,Percent,Population,0 +RANCHO MARINA,Pacific Islander,Percent,Population,0 +RANCHO MARINA,Other / Multiple,Percent,Population,3.63 +RANCHO MARINA,Poverty Total Assessed,Count,Population,0 +RANCHO MARINA,Poverty Below,Count,Population,0 +RANCHO MARINA,Poverty Above,Count,Population,0 +RANCHO MARINA,Poverty Rate,Percent,Population,35.94 +RANCHO MARINA,Households Total,Count,Households,0 +RANCHO MARINA,Income Below 10k,Count,Households,0 +RANCHO MARINA,Income 10k-15k,Count,Households,0 +RANCHO MARINA,Income 15k-20k,Count,Households,0 +RANCHO MARINA,Income 20k-25k,Count,Households,0 +RANCHO MARINA,Income 25k-30k,Count,Households,0 +RANCHO MARINA,Income 30k-35k,Count,Households,0 +RANCHO MARINA,Income 35k-40k,Count,Households,0 +RANCHO MARINA,Income 40k-45k,Count,Households,0 +RANCHO MARINA,Income 45k-50k,Count,Households,0 +RANCHO MARINA,Income 50k-60k,Count,Households,0 +RANCHO MARINA,Income 60k-75k,Count,Households,0 +RANCHO MARINA,Income 75k-100k,Count,Households,0 +RANCHO MARINA,Income 100k-125k,Count,Households,0 +RANCHO MARINA,Income 125k-150k,Count,Households,0 +RANCHO MARINA,Income 150k-200k,Count,Households,0 +RANCHO MARINA,Income Above 200k,Count,Households,0 +RANCHO MARINA,Income 0-25k,Count,Households,0 +RANCHO MARINA,Income 25k-50k,Count,Households,0 +RANCHO MARINA,Income 50k-75k,Count,Households,0 +RANCHO MARINA,Income 0-50k,Count,Households,0 +RANCHO MARINA,Income 50k-100k,Count,Households,0 +RANCHO MARINA,Income 100k-150k,Count,Households,0 +RANCHO MARINA,Mortgage Total,Count,Households,0 +RANCHO MARINA,Mortgage Over 30% Income,Count,Households,0 +RANCHO MARINA,Mortgage Over 50% Income,Count,Households,0 +RANCHO MARINA,No Mortgage Total,Count,Households,0 +RANCHO MARINA,No Mortgage Over 30% Income,Count,Households,0 +RANCHO MARINA,No Mortgage Over 50% Income,Count,Households,0 +RANCHO MARINA,Rent Total,Count,Households,0 +RANCHO MARINA,Rent Over 30% Income,Count,Households,0 +RANCHO MARINA,Rent Over 50% Income,Count,Households,0 +RANCHO MARINA,Average Household Size,Hh Weighted,Household Weighted,1.79 +RANCHO MARINA,Median Household Income,Hh Weighted,Household Weighted,38125 +RANCHO MARINA,Per Capita Income,Pop Weighted,Population Weighted,33103 +RANCHO MARINA,Housing Costs Over 30% Income,Percent,Households,28.02 +RANCHO MARINA,Housing Costs Over 50% Income,Percent,Households,23.19 +RANCHO MURIETA COMMUNITY SERVI,Population Total,Count,Population,3239 +RANCHO MURIETA COMMUNITY SERVI,Hispanic / Latino,Count,Population,661 +RANCHO MURIETA COMMUNITY SERVI,White,Count,Population,2157 +RANCHO MURIETA COMMUNITY SERVI,Black-/ African American,Count,Population,120 +RANCHO MURIETA COMMUNITY SERVI,Native American,Count,Population,7 +RANCHO MURIETA COMMUNITY SERVI,Asian,Count,Population,188 +RANCHO MURIETA COMMUNITY SERVI,Pacific Islander,Count,Population,0 +RANCHO MURIETA COMMUNITY SERVI,Other / Multiple,Count,Population,106 +RANCHO MURIETA COMMUNITY SERVI,Hispanic / Latino,Percent,Population,20.42 +RANCHO MURIETA COMMUNITY SERVI,White,Percent,Population,66.59 +RANCHO MURIETA COMMUNITY SERVI,Black-/ African American,Percent,Population,3.71 +RANCHO MURIETA COMMUNITY SERVI,Native American,Percent,Population,0.21 +RANCHO MURIETA COMMUNITY SERVI,Asian,Percent,Population,5.8 +RANCHO MURIETA COMMUNITY SERVI,Pacific Islander,Percent,Population,0 +RANCHO MURIETA COMMUNITY SERVI,Other / Multiple,Percent,Population,3.26 +RANCHO MURIETA COMMUNITY SERVI,Poverty Total Assessed,Count,Population,3239 +RANCHO MURIETA COMMUNITY SERVI,Poverty Below,Count,Population,199 +RANCHO MURIETA COMMUNITY SERVI,Poverty Above,Count,Population,3040 +RANCHO MURIETA COMMUNITY SERVI,Poverty Rate,Percent,Population,6.13 +RANCHO MURIETA COMMUNITY SERVI,Households Total,Count,Households,1402 +RANCHO MURIETA COMMUNITY SERVI,Income Below 10k,Count,Households,59 +RANCHO MURIETA COMMUNITY SERVI,Income 10k-15k,Count,Households,42 +RANCHO MURIETA COMMUNITY SERVI,Income 15k-20k,Count,Households,0 +RANCHO MURIETA COMMUNITY SERVI,Income 20k-25k,Count,Households,6 +RANCHO MURIETA COMMUNITY SERVI,Income 25k-30k,Count,Households,5 +RANCHO MURIETA COMMUNITY SERVI,Income 30k-35k,Count,Households,18 +RANCHO MURIETA COMMUNITY SERVI,Income 35k-40k,Count,Households,74 +RANCHO MURIETA COMMUNITY SERVI,Income 40k-45k,Count,Households,27 +RANCHO MURIETA COMMUNITY SERVI,Income 45k-50k,Count,Households,75 +RANCHO MURIETA COMMUNITY SERVI,Income 50k-60k,Count,Households,44 +RANCHO MURIETA COMMUNITY SERVI,Income 60k-75k,Count,Households,81 +RANCHO MURIETA COMMUNITY SERVI,Income 75k-100k,Count,Households,88 +RANCHO MURIETA COMMUNITY SERVI,Income 100k-125k,Count,Households,118 +RANCHO MURIETA COMMUNITY SERVI,Income 125k-150k,Count,Households,204 +RANCHO MURIETA COMMUNITY SERVI,Income 150k-200k,Count,Households,241 +RANCHO MURIETA COMMUNITY SERVI,Income Above 200k,Count,Households,319 +RANCHO MURIETA COMMUNITY SERVI,Income 0-25k,Count,Households,108 +RANCHO MURIETA COMMUNITY SERVI,Income 25k-50k,Count,Households,199 +RANCHO MURIETA COMMUNITY SERVI,Income 50k-75k,Count,Households,125 +RANCHO MURIETA COMMUNITY SERVI,Income 0-50k,Count,Households,307 +RANCHO MURIETA COMMUNITY SERVI,Income 50k-100k,Count,Households,213 +RANCHO MURIETA COMMUNITY SERVI,Income 100k-150k,Count,Households,323 +RANCHO MURIETA COMMUNITY SERVI,Mortgage Total,Count,Households,1029 +RANCHO MURIETA COMMUNITY SERVI,Mortgage Over 30% Income,Count,Households,205 +RANCHO MURIETA COMMUNITY SERVI,Mortgage Over 50% Income,Count,Households,103 +RANCHO MURIETA COMMUNITY SERVI,No Mortgage Total,Count,Households,270 +RANCHO MURIETA COMMUNITY SERVI,No Mortgage Over 30% Income,Count,Households,63 +RANCHO MURIETA COMMUNITY SERVI,No Mortgage Over 50% Income,Count,Households,57 +RANCHO MURIETA COMMUNITY SERVI,Rent Total,Count,Households,103 +RANCHO MURIETA COMMUNITY SERVI,Rent Over 30% Income,Count,Households,41 +RANCHO MURIETA COMMUNITY SERVI,Rent Over 50% Income,Count,Households,40 +RANCHO MURIETA COMMUNITY SERVI,Average Household Size,Hh Weighted,Household Weighted,2.307704065813508 +RANCHO MURIETA COMMUNITY SERVI,Median Household Income,Hh Weighted,Household Weighted,144993.80707581018 +RANCHO MURIETA COMMUNITY SERVI,Per Capita Income,Pop Weighted,Population Weighted,66451.34059033732 +RANCHO MURIETA COMMUNITY SERVI,Housing Costs Over 30% Income,Percent,Households,22.02 +RANCHO MURIETA COMMUNITY SERVI,Housing Costs Over 50% Income,Percent,Households,14.3 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Population Total,Count,Population,22 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Hispanic / Latino,Count,Population,6 +RIO COSUMNES CORRECTIONAL CENTER [SWS],White,Count,Population,8 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Black-/ African American,Count,Population,4 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Native American,Count,Population,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Asian,Count,Population,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Pacific Islander,Count,Population,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Other / Multiple,Count,Population,2 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Hispanic / Latino,Percent,Population,25.74 +RIO COSUMNES CORRECTIONAL CENTER [SWS],White,Percent,Population,37.49 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Black-/ African American,Percent,Population,16.82 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Native American,Percent,Population,2.97 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Asian,Percent,Population,4.5 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Pacific Islander,Percent,Population,1.81 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Other / Multiple,Percent,Population,10.66 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Poverty Total Assessed,Count,Population,4 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Poverty Below,Count,Population,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Poverty Above,Count,Population,4 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Poverty Rate,Percent,Population,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Households Total,Count,Households,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income Below 10k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 10k-15k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 15k-20k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 20k-25k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 25k-30k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 30k-35k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 35k-40k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 40k-45k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 45k-50k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 50k-60k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 60k-75k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 75k-100k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 100k-125k,Count,Households,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 125k-150k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 150k-200k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income Above 200k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 0-25k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 25k-50k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 50k-75k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 0-50k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 50k-100k,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Income 100k-150k,Count,Households,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Mortgage Total,Count,Households,1 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Mortgage Over 30% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Mortgage Over 50% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],No Mortgage Total,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],No Mortgage Over 30% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],No Mortgage Over 50% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Rent Total,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Rent Over 30% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Rent Over 50% Income,Count,Households,0 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Average Household Size,Hh Weighted,Household Weighted,3.45 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Median Household Income,Hh Weighted,Household Weighted,115897.00000000001 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Per Capita Income,Pop Weighted,Population Weighted,11095 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Housing Costs Over 30% Income,Percent,Households,23.75 +RIO COSUMNES CORRECTIONAL CENTER [SWS],Housing Costs Over 50% Income,Percent,Households,0 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Population Total,Count,Population,11831 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Hispanic / Latino,Count,Population,2585 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,White,Count,Population,7595 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Black-/ African American,Count,Population,337 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Native American,Count,Population,17 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Asian,Count,Population,765 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Pacific Islander,Count,Population,21 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Other / Multiple,Count,Population,512 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Hispanic / Latino,Percent,Population,21.85 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,White,Percent,Population,64.19 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Black-/ African American,Percent,Population,2.85 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Native American,Percent,Population,0.14 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Asian,Percent,Population,6.46 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Pacific Islander,Percent,Population,0.18 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Other / Multiple,Percent,Population,4.33 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Poverty Total Assessed,Count,Population,11829 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Poverty Below,Count,Population,1619 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Poverty Above,Count,Population,10210 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Poverty Rate,Percent,Population,13.69 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Households Total,Count,Households,3762 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income Below 10k,Count,Households,177 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 10k-15k,Count,Households,156 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 15k-20k,Count,Households,67 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 20k-25k,Count,Households,169 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 25k-30k,Count,Households,56 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 30k-35k,Count,Households,113 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 35k-40k,Count,Households,116 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 40k-45k,Count,Households,114 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 45k-50k,Count,Households,118 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 50k-60k,Count,Households,173 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 60k-75k,Count,Households,297 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 75k-100k,Count,Households,607 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 100k-125k,Count,Households,492 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 125k-150k,Count,Households,431 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 150k-200k,Count,Households,416 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income Above 200k,Count,Households,259 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 0-25k,Count,Households,569 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 25k-50k,Count,Households,518 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 50k-75k,Count,Households,470 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 0-50k,Count,Households,1087 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 50k-100k,Count,Households,1077 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Income 100k-150k,Count,Households,922 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Mortgage Total,Count,Households,1918 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Mortgage Over 30% Income,Count,Households,573 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Mortgage Over 50% Income,Count,Households,157 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,No Mortgage Total,Count,Households,773 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,No Mortgage Over 30% Income,Count,Households,114 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,No Mortgage Over 50% Income,Count,Households,47 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Rent Total,Count,Households,1070 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Rent Over 30% Income,Count,Households,519 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Rent Over 50% Income,Count,Households,340 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Average Household Size,Hh Weighted,Household Weighted,3.1230123203938827 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Median Household Income,Hh Weighted,Household Weighted,83603.04124462775 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Per Capita Income,Pop Weighted,Population Weighted,33734.48719704626 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Housing Costs Over 30% Income,Percent,Households,32.07 +RIO LINDA/ELVERTA COMMUNITY WATER DIST,Housing Costs Over 50% Income,Percent,Households,14.49 +RIVER'S EDGE MARINA & RESORT,Population Total,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Hispanic / Latino,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,White,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Black-/ African American,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Native American,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Asian,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Pacific Islander,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Other / Multiple,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Hispanic / Latino,Percent,Population,3.9 +RIVER'S EDGE MARINA & RESORT,White,Percent,Population,89.23 +RIVER'S EDGE MARINA & RESORT,Black-/ African American,Percent,Population,3.23 +RIVER'S EDGE MARINA & RESORT,Native American,Percent,Population,0 +RIVER'S EDGE MARINA & RESORT,Asian,Percent,Population,0 +RIVER'S EDGE MARINA & RESORT,Pacific Islander,Percent,Population,0 +RIVER'S EDGE MARINA & RESORT,Other / Multiple,Percent,Population,3.63 +RIVER'S EDGE MARINA & RESORT,Poverty Total Assessed,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Poverty Below,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Poverty Above,Count,Population,0 +RIVER'S EDGE MARINA & RESORT,Poverty Rate,Percent,Population,35.94 +RIVER'S EDGE MARINA & RESORT,Households Total,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income Below 10k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 10k-15k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 15k-20k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 20k-25k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 25k-30k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 30k-35k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 35k-40k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 40k-45k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 45k-50k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 50k-60k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 60k-75k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 75k-100k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 100k-125k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 125k-150k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 150k-200k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income Above 200k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 0-25k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 25k-50k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 50k-75k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 0-50k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 50k-100k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Income 100k-150k,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Mortgage Total,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Mortgage Over 30% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Mortgage Over 50% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,No Mortgage Total,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,No Mortgage Over 30% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,No Mortgage Over 50% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Rent Total,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Rent Over 30% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Rent Over 50% Income,Count,Households,0 +RIVER'S EDGE MARINA & RESORT,Average Household Size,Hh Weighted,Household Weighted,1.79 +RIVER'S EDGE MARINA & RESORT,Median Household Income,Hh Weighted,Household Weighted,38125 +RIVER'S EDGE MARINA & RESORT,Per Capita Income,Pop Weighted,Population Weighted,33103 +RIVER'S EDGE MARINA & RESORT,Housing Costs Over 30% Income,Percent,Households,28.02 +RIVER'S EDGE MARINA & RESORT,Housing Costs Over 50% Income,Percent,Households,23.19 +SAC CITY MOBILE HOME COMMUNITY LP,Population Total,Count,Population,229 +SAC CITY MOBILE HOME COMMUNITY LP,Hispanic / Latino,Count,Population,82 +SAC CITY MOBILE HOME COMMUNITY LP,White,Count,Population,17 +SAC CITY MOBILE HOME COMMUNITY LP,Black-/ African American,Count,Population,7 +SAC CITY MOBILE HOME COMMUNITY LP,Native American,Count,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Asian,Count,Population,123 +SAC CITY MOBILE HOME COMMUNITY LP,Pacific Islander,Count,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Other / Multiple,Count,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Hispanic / Latino,Percent,Population,35.66 +SAC CITY MOBILE HOME COMMUNITY LP,White,Percent,Population,7.5 +SAC CITY MOBILE HOME COMMUNITY LP,Black-/ African American,Percent,Population,3.27 +SAC CITY MOBILE HOME COMMUNITY LP,Native American,Percent,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Asian,Percent,Population,53.57 +SAC CITY MOBILE HOME COMMUNITY LP,Pacific Islander,Percent,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Other / Multiple,Percent,Population,0 +SAC CITY MOBILE HOME COMMUNITY LP,Poverty Total Assessed,Count,Population,229 +SAC CITY MOBILE HOME COMMUNITY LP,Poverty Below,Count,Population,110 +SAC CITY MOBILE HOME COMMUNITY LP,Poverty Above,Count,Population,119 +SAC CITY MOBILE HOME COMMUNITY LP,Poverty Rate,Percent,Population,48.14 +SAC CITY MOBILE HOME COMMUNITY LP,Households Total,Count,Households,89 +SAC CITY MOBILE HOME COMMUNITY LP,Income Below 10k,Count,Households,11 +SAC CITY MOBILE HOME COMMUNITY LP,Income 10k-15k,Count,Households,16 +SAC CITY MOBILE HOME COMMUNITY LP,Income 15k-20k,Count,Households,9 +SAC CITY MOBILE HOME COMMUNITY LP,Income 20k-25k,Count,Households,10 +SAC CITY MOBILE HOME COMMUNITY LP,Income 25k-30k,Count,Households,8 +SAC CITY MOBILE HOME COMMUNITY LP,Income 30k-35k,Count,Households,0 +SAC CITY MOBILE HOME COMMUNITY LP,Income 35k-40k,Count,Households,0 +SAC CITY MOBILE HOME COMMUNITY LP,Income 40k-45k,Count,Households,4 +SAC CITY MOBILE HOME COMMUNITY LP,Income 45k-50k,Count,Households,2 +SAC CITY MOBILE HOME COMMUNITY LP,Income 50k-60k,Count,Households,7 +SAC CITY MOBILE HOME COMMUNITY LP,Income 60k-75k,Count,Households,1 +SAC CITY MOBILE HOME COMMUNITY LP,Income 75k-100k,Count,Households,13 +SAC CITY MOBILE HOME COMMUNITY LP,Income 100k-125k,Count,Households,4 +SAC CITY MOBILE HOME COMMUNITY LP,Income 125k-150k,Count,Households,4 +SAC CITY MOBILE HOME COMMUNITY LP,Income 150k-200k,Count,Households,0 +SAC CITY MOBILE HOME COMMUNITY LP,Income Above 200k,Count,Households,0 +SAC CITY MOBILE HOME COMMUNITY LP,Income 0-25k,Count,Households,46 +SAC CITY MOBILE HOME COMMUNITY LP,Income 25k-50k,Count,Households,14 +SAC CITY MOBILE HOME COMMUNITY LP,Income 50k-75k,Count,Households,8 +SAC CITY MOBILE HOME COMMUNITY LP,Income 0-50k,Count,Households,60 +SAC CITY MOBILE HOME COMMUNITY LP,Income 50k-100k,Count,Households,21 +SAC CITY MOBILE HOME COMMUNITY LP,Income 100k-150k,Count,Households,8 +SAC CITY MOBILE HOME COMMUNITY LP,Mortgage Total,Count,Households,4 +SAC CITY MOBILE HOME COMMUNITY LP,Mortgage Over 30% Income,Count,Households,2 +SAC CITY MOBILE HOME COMMUNITY LP,Mortgage Over 50% Income,Count,Households,2 +SAC CITY MOBILE HOME COMMUNITY LP,No Mortgage Total,Count,Households,15 +SAC CITY MOBILE HOME COMMUNITY LP,No Mortgage Over 30% Income,Count,Households,2 +SAC CITY MOBILE HOME COMMUNITY LP,No Mortgage Over 50% Income,Count,Households,0 +SAC CITY MOBILE HOME COMMUNITY LP,Rent Total,Count,Households,71 +SAC CITY MOBILE HOME COMMUNITY LP,Rent Over 30% Income,Count,Households,41 +SAC CITY MOBILE HOME COMMUNITY LP,Rent Over 50% Income,Count,Households,30 +SAC CITY MOBILE HOME COMMUNITY LP,Average Household Size,Hh Weighted,Household Weighted,2.53 +SAC CITY MOBILE HOME COMMUNITY LP,Median Household Income,Hh Weighted,Household Weighted,22380 +SAC CITY MOBILE HOME COMMUNITY LP,Per Capita Income,Pop Weighted,Population Weighted,16689 +SAC CITY MOBILE HOME COMMUNITY LP,Housing Costs Over 30% Income,Percent,Households,48.95 +SAC CITY MOBILE HOME COMMUNITY LP,Housing Costs Over 50% Income,Percent,Households,35.43 +SACRAMENTO SUBURBAN WATER DISTRICT,Population Total,Count,Population,193126 +SACRAMENTO SUBURBAN WATER DISTRICT,Hispanic / Latino,Count,Population,43047 +SACRAMENTO SUBURBAN WATER DISTRICT,White,Count,Population,97872 +SACRAMENTO SUBURBAN WATER DISTRICT,Black-/ African American,Count,Population,17684 +SACRAMENTO SUBURBAN WATER DISTRICT,Native American,Count,Population,834 +SACRAMENTO SUBURBAN WATER DISTRICT,Asian,Count,Population,20602 +SACRAMENTO SUBURBAN WATER DISTRICT,Pacific Islander,Count,Population,624 +SACRAMENTO SUBURBAN WATER DISTRICT,Other / Multiple,Count,Population,12464 +SACRAMENTO SUBURBAN WATER DISTRICT,Hispanic / Latino,Percent,Population,22.29 +SACRAMENTO SUBURBAN WATER DISTRICT,White,Percent,Population,50.68 +SACRAMENTO SUBURBAN WATER DISTRICT,Black-/ African American,Percent,Population,9.16 +SACRAMENTO SUBURBAN WATER DISTRICT,Native American,Percent,Population,0.43 +SACRAMENTO SUBURBAN WATER DISTRICT,Asian,Percent,Population,10.67 +SACRAMENTO SUBURBAN WATER DISTRICT,Pacific Islander,Percent,Population,0.32 +SACRAMENTO SUBURBAN WATER DISTRICT,Other / Multiple,Percent,Population,6.45 +SACRAMENTO SUBURBAN WATER DISTRICT,Poverty Total Assessed,Count,Population,190984 +SACRAMENTO SUBURBAN WATER DISTRICT,Poverty Below,Count,Population,33399 +SACRAMENTO SUBURBAN WATER DISTRICT,Poverty Above,Count,Population,157585 +SACRAMENTO SUBURBAN WATER DISTRICT,Poverty Rate,Percent,Population,17.49 +SACRAMENTO SUBURBAN WATER DISTRICT,Households Total,Count,Households,72505 +SACRAMENTO SUBURBAN WATER DISTRICT,Income Below 10k,Count,Households,3817 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 10k-15k,Count,Households,3001 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 15k-20k,Count,Households,3069 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 20k-25k,Count,Households,2884 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 25k-30k,Count,Households,3205 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 30k-35k,Count,Households,3100 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 35k-40k,Count,Households,3337 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 40k-45k,Count,Households,2893 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 45k-50k,Count,Households,2342 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 50k-60k,Count,Households,5541 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 60k-75k,Count,Households,6792 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 75k-100k,Count,Households,10037 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 100k-125k,Count,Households,6480 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 125k-150k,Count,Households,4342 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 150k-200k,Count,Households,5488 +SACRAMENTO SUBURBAN WATER DISTRICT,Income Above 200k,Count,Households,6177 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 0-25k,Count,Households,12771 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 25k-50k,Count,Households,14878 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 50k-75k,Count,Households,12333 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 0-50k,Count,Households,27649 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 50k-100k,Count,Households,22370 +SACRAMENTO SUBURBAN WATER DISTRICT,Income 100k-150k,Count,Households,10822 +SACRAMENTO SUBURBAN WATER DISTRICT,Mortgage Total,Count,Households,23467 +SACRAMENTO SUBURBAN WATER DISTRICT,Mortgage Over 30% Income,Count,Households,7204 +SACRAMENTO SUBURBAN WATER DISTRICT,Mortgage Over 50% Income,Count,Households,2837 +SACRAMENTO SUBURBAN WATER DISTRICT,No Mortgage Total,Count,Households,12037 +SACRAMENTO SUBURBAN WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,2087 +SACRAMENTO SUBURBAN WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,1160 +SACRAMENTO SUBURBAN WATER DISTRICT,Rent Total,Count,Households,37001 +SACRAMENTO SUBURBAN WATER DISTRICT,Rent Over 30% Income,Count,Households,21072 +SACRAMENTO SUBURBAN WATER DISTRICT,Rent Over 50% Income,Count,Households,10274 +SACRAMENTO SUBURBAN WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,2.635470822506937 +SACRAMENTO SUBURBAN WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,73746.51448559026 +SACRAMENTO SUBURBAN WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,35321.17943972356 +SACRAMENTO SUBURBAN WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,41.88 +SACRAMENTO SUBURBAN WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,19.68 +SAN JUAN WATER DISTRICT,Population Total,Count,Population,30122 +SAN JUAN WATER DISTRICT,Hispanic / Latino,Count,Population,3409 +SAN JUAN WATER DISTRICT,White,Count,Population,21349 +SAN JUAN WATER DISTRICT,Black-/ African American,Count,Population,831 +SAN JUAN WATER DISTRICT,Native American,Count,Population,287 +SAN JUAN WATER DISTRICT,Asian,Count,Population,2762 +SAN JUAN WATER DISTRICT,Pacific Islander,Count,Population,17 +SAN JUAN WATER DISTRICT,Other / Multiple,Count,Population,1467 +SAN JUAN WATER DISTRICT,Hispanic / Latino,Percent,Population,11.32 +SAN JUAN WATER DISTRICT,White,Percent,Population,70.87 +SAN JUAN WATER DISTRICT,Black-/ African American,Percent,Population,2.76 +SAN JUAN WATER DISTRICT,Native American,Percent,Population,0.95 +SAN JUAN WATER DISTRICT,Asian,Percent,Population,9.17 +SAN JUAN WATER DISTRICT,Pacific Islander,Percent,Population,0.06 +SAN JUAN WATER DISTRICT,Other / Multiple,Percent,Population,4.87 +SAN JUAN WATER DISTRICT,Poverty Total Assessed,Count,Population,30014 +SAN JUAN WATER DISTRICT,Poverty Below,Count,Population,1718 +SAN JUAN WATER DISTRICT,Poverty Above,Count,Population,28297 +SAN JUAN WATER DISTRICT,Poverty Rate,Percent,Population,5.72 +SAN JUAN WATER DISTRICT,Households Total,Count,Households,10750 +SAN JUAN WATER DISTRICT,Income Below 10k,Count,Households,389 +SAN JUAN WATER DISTRICT,Income 10k-15k,Count,Households,168 +SAN JUAN WATER DISTRICT,Income 15k-20k,Count,Households,100 +SAN JUAN WATER DISTRICT,Income 20k-25k,Count,Households,275 +SAN JUAN WATER DISTRICT,Income 25k-30k,Count,Households,128 +SAN JUAN WATER DISTRICT,Income 30k-35k,Count,Households,160 +SAN JUAN WATER DISTRICT,Income 35k-40k,Count,Households,111 +SAN JUAN WATER DISTRICT,Income 40k-45k,Count,Households,133 +SAN JUAN WATER DISTRICT,Income 45k-50k,Count,Households,127 +SAN JUAN WATER DISTRICT,Income 50k-60k,Count,Households,472 +SAN JUAN WATER DISTRICT,Income 60k-75k,Count,Households,684 +SAN JUAN WATER DISTRICT,Income 75k-100k,Count,Households,984 +SAN JUAN WATER DISTRICT,Income 100k-125k,Count,Households,854 +SAN JUAN WATER DISTRICT,Income 125k-150k,Count,Households,876 +SAN JUAN WATER DISTRICT,Income 150k-200k,Count,Households,1032 +SAN JUAN WATER DISTRICT,Income Above 200k,Count,Households,4256 +SAN JUAN WATER DISTRICT,Income 0-25k,Count,Households,932 +SAN JUAN WATER DISTRICT,Income 25k-50k,Count,Households,658 +SAN JUAN WATER DISTRICT,Income 50k-75k,Count,Households,1156 +SAN JUAN WATER DISTRICT,Income 0-50k,Count,Households,1591 +SAN JUAN WATER DISTRICT,Income 50k-100k,Count,Households,2141 +SAN JUAN WATER DISTRICT,Income 100k-150k,Count,Households,1730 +SAN JUAN WATER DISTRICT,Mortgage Total,Count,Households,6210 +SAN JUAN WATER DISTRICT,Mortgage Over 30% Income,Count,Households,1754 +SAN JUAN WATER DISTRICT,Mortgage Over 50% Income,Count,Households,724 +SAN JUAN WATER DISTRICT,No Mortgage Total,Count,Households,2883 +SAN JUAN WATER DISTRICT,No Mortgage Over 30% Income,Count,Households,528 +SAN JUAN WATER DISTRICT,No Mortgage Over 50% Income,Count,Households,357 +SAN JUAN WATER DISTRICT,Rent Total,Count,Households,1658 +SAN JUAN WATER DISTRICT,Rent Over 30% Income,Count,Households,726 +SAN JUAN WATER DISTRICT,Rent Over 50% Income,Count,Households,339 +SAN JUAN WATER DISTRICT,Average Household Size,Hh Weighted,Household Weighted,2.7838582261197615 +SAN JUAN WATER DISTRICT,Median Household Income,Hh Weighted,Household Weighted,160696.10105741228 +SAN JUAN WATER DISTRICT,Per Capita Income,Pop Weighted,Population Weighted,72978.42336271124 +SAN JUAN WATER DISTRICT,Housing Costs Over 30% Income,Percent,Households,27.98 +SAN JUAN WATER DISTRICT,Housing Costs Over 50% Income,Percent,Households,13.21 +SCWA - ARDEN PARK VISTA,Population Total,Count,Population,8086 +SCWA - ARDEN PARK VISTA,Hispanic / Latino,Count,Population,990 +SCWA - ARDEN PARK VISTA,White,Count,Population,6016 +SCWA - ARDEN PARK VISTA,Black-/ African American,Count,Population,270 +SCWA - ARDEN PARK VISTA,Native American,Count,Population,12 +SCWA - ARDEN PARK VISTA,Asian,Count,Population,396 +SCWA - ARDEN PARK VISTA,Pacific Islander,Count,Population,8 +SCWA - ARDEN PARK VISTA,Other / Multiple,Count,Population,395 +SCWA - ARDEN PARK VISTA,Hispanic / Latino,Percent,Population,12.24 +SCWA - ARDEN PARK VISTA,White,Percent,Population,74.4 +SCWA - ARDEN PARK VISTA,Black-/ African American,Percent,Population,3.33 +SCWA - ARDEN PARK VISTA,Native American,Percent,Population,0.15 +SCWA - ARDEN PARK VISTA,Asian,Percent,Population,4.9 +SCWA - ARDEN PARK VISTA,Pacific Islander,Percent,Population,0.1 +SCWA - ARDEN PARK VISTA,Other / Multiple,Percent,Population,4.88 +SCWA - ARDEN PARK VISTA,Poverty Total Assessed,Count,Population,8038 +SCWA - ARDEN PARK VISTA,Poverty Below,Count,Population,523 +SCWA - ARDEN PARK VISTA,Poverty Above,Count,Population,7515 +SCWA - ARDEN PARK VISTA,Poverty Rate,Percent,Population,6.51 +SCWA - ARDEN PARK VISTA,Households Total,Count,Households,3303 +SCWA - ARDEN PARK VISTA,Income Below 10k,Count,Households,79 +SCWA - ARDEN PARK VISTA,Income 10k-15k,Count,Households,36 +SCWA - ARDEN PARK VISTA,Income 15k-20k,Count,Households,48 +SCWA - ARDEN PARK VISTA,Income 20k-25k,Count,Households,77 +SCWA - ARDEN PARK VISTA,Income 25k-30k,Count,Households,65 +SCWA - ARDEN PARK VISTA,Income 30k-35k,Count,Households,38 +SCWA - ARDEN PARK VISTA,Income 35k-40k,Count,Households,18 +SCWA - ARDEN PARK VISTA,Income 40k-45k,Count,Households,49 +SCWA - ARDEN PARK VISTA,Income 45k-50k,Count,Households,162 +SCWA - ARDEN PARK VISTA,Income 50k-60k,Count,Households,139 +SCWA - ARDEN PARK VISTA,Income 60k-75k,Count,Households,187 +SCWA - ARDEN PARK VISTA,Income 75k-100k,Count,Households,253 +SCWA - ARDEN PARK VISTA,Income 100k-125k,Count,Households,465 +SCWA - ARDEN PARK VISTA,Income 125k-150k,Count,Households,208 +SCWA - ARDEN PARK VISTA,Income 150k-200k,Count,Households,416 +SCWA - ARDEN PARK VISTA,Income Above 200k,Count,Households,1065 +SCWA - ARDEN PARK VISTA,Income 0-25k,Count,Households,241 +SCWA - ARDEN PARK VISTA,Income 25k-50k,Count,Households,330 +SCWA - ARDEN PARK VISTA,Income 50k-75k,Count,Households,326 +SCWA - ARDEN PARK VISTA,Income 0-50k,Count,Households,571 +SCWA - ARDEN PARK VISTA,Income 50k-100k,Count,Households,579 +SCWA - ARDEN PARK VISTA,Income 100k-150k,Count,Households,673 +SCWA - ARDEN PARK VISTA,Mortgage Total,Count,Households,1823 +SCWA - ARDEN PARK VISTA,Mortgage Over 30% Income,Count,Households,520 +SCWA - ARDEN PARK VISTA,Mortgage Over 50% Income,Count,Households,112 +SCWA - ARDEN PARK VISTA,No Mortgage Total,Count,Households,673 +SCWA - ARDEN PARK VISTA,No Mortgage Over 30% Income,Count,Households,76 +SCWA - ARDEN PARK VISTA,No Mortgage Over 50% Income,Count,Households,23 +SCWA - ARDEN PARK VISTA,Rent Total,Count,Households,807 +SCWA - ARDEN PARK VISTA,Rent Over 30% Income,Count,Households,384 +SCWA - ARDEN PARK VISTA,Rent Over 50% Income,Count,Households,225 +SCWA - ARDEN PARK VISTA,Average Household Size,Hh Weighted,Household Weighted,2.424845516612799 +SCWA - ARDEN PARK VISTA,Median Household Income,Hh Weighted,Household Weighted,139081.65196802403 +SCWA - ARDEN PARK VISTA,Per Capita Income,Pop Weighted,Population Weighted,84548.46138496802 +SCWA - ARDEN PARK VISTA,Housing Costs Over 30% Income,Percent,Households,29.69 +SCWA - ARDEN PARK VISTA,Housing Costs Over 50% Income,Percent,Households,10.9 +SCWA - LAGUNA/VINEYARD,Population Total,Count,Population,145495 +SCWA - LAGUNA/VINEYARD,Hispanic / Latino,Count,Population,27502 +SCWA - LAGUNA/VINEYARD,White,Count,Population,38496 +SCWA - LAGUNA/VINEYARD,Black-/ African American,Count,Population,16568 +SCWA - LAGUNA/VINEYARD,Native American,Count,Population,246 +SCWA - LAGUNA/VINEYARD,Asian,Count,Population,50411 +SCWA - LAGUNA/VINEYARD,Pacific Islander,Count,Population,2220 +SCWA - LAGUNA/VINEYARD,Other / Multiple,Count,Population,10052 +SCWA - LAGUNA/VINEYARD,Hispanic / Latino,Percent,Population,18.9 +SCWA - LAGUNA/VINEYARD,White,Percent,Population,26.46 +SCWA - LAGUNA/VINEYARD,Black-/ African American,Percent,Population,11.39 +SCWA - LAGUNA/VINEYARD,Native American,Percent,Population,0.17 +SCWA - LAGUNA/VINEYARD,Asian,Percent,Population,34.65 +SCWA - LAGUNA/VINEYARD,Pacific Islander,Percent,Population,1.53 +SCWA - LAGUNA/VINEYARD,Other / Multiple,Percent,Population,6.91 +SCWA - LAGUNA/VINEYARD,Poverty Total Assessed,Count,Population,145198 +SCWA - LAGUNA/VINEYARD,Poverty Below,Count,Population,14710 +SCWA - LAGUNA/VINEYARD,Poverty Above,Count,Population,130489 +SCWA - LAGUNA/VINEYARD,Poverty Rate,Percent,Population,10.13 +SCWA - LAGUNA/VINEYARD,Households Total,Count,Households,45137 +SCWA - LAGUNA/VINEYARD,Income Below 10k,Count,Households,1692 +SCWA - LAGUNA/VINEYARD,Income 10k-15k,Count,Households,666 +SCWA - LAGUNA/VINEYARD,Income 15k-20k,Count,Households,742 +SCWA - LAGUNA/VINEYARD,Income 20k-25k,Count,Households,878 +SCWA - LAGUNA/VINEYARD,Income 25k-30k,Count,Households,839 +SCWA - LAGUNA/VINEYARD,Income 30k-35k,Count,Households,1336 +SCWA - LAGUNA/VINEYARD,Income 35k-40k,Count,Households,850 +SCWA - LAGUNA/VINEYARD,Income 40k-45k,Count,Households,788 +SCWA - LAGUNA/VINEYARD,Income 45k-50k,Count,Households,752 +SCWA - LAGUNA/VINEYARD,Income 50k-60k,Count,Households,2363 +SCWA - LAGUNA/VINEYARD,Income 60k-75k,Count,Households,3198 +SCWA - LAGUNA/VINEYARD,Income 75k-100k,Count,Households,6037 +SCWA - LAGUNA/VINEYARD,Income 100k-125k,Count,Households,5323 +SCWA - LAGUNA/VINEYARD,Income 125k-150k,Count,Households,5057 +SCWA - LAGUNA/VINEYARD,Income 150k-200k,Count,Households,6578 +SCWA - LAGUNA/VINEYARD,Income Above 200k,Count,Households,8038 +SCWA - LAGUNA/VINEYARD,Income 0-25k,Count,Households,3978 +SCWA - LAGUNA/VINEYARD,Income 25k-50k,Count,Households,4565 +SCWA - LAGUNA/VINEYARD,Income 50k-75k,Count,Households,5561 +SCWA - LAGUNA/VINEYARD,Income 0-50k,Count,Households,8543 +SCWA - LAGUNA/VINEYARD,Income 50k-100k,Count,Households,11598 +SCWA - LAGUNA/VINEYARD,Income 100k-150k,Count,Households,10380 +SCWA - LAGUNA/VINEYARD,Mortgage Total,Count,Households,24581 +SCWA - LAGUNA/VINEYARD,Mortgage Over 30% Income,Count,Households,7232 +SCWA - LAGUNA/VINEYARD,Mortgage Over 50% Income,Count,Households,2916 +SCWA - LAGUNA/VINEYARD,No Mortgage Total,Count,Households,7878 +SCWA - LAGUNA/VINEYARD,No Mortgage Over 30% Income,Count,Households,861 +SCWA - LAGUNA/VINEYARD,No Mortgage Over 50% Income,Count,Households,471 +SCWA - LAGUNA/VINEYARD,Rent Total,Count,Households,12677 +SCWA - LAGUNA/VINEYARD,Rent Over 30% Income,Count,Households,6368 +SCWA - LAGUNA/VINEYARD,Rent Over 50% Income,Count,Households,3337 +SCWA - LAGUNA/VINEYARD,Average Household Size,Hh Weighted,Household Weighted,3.2074469367308294 +SCWA - LAGUNA/VINEYARD,Median Household Income,Hh Weighted,Household Weighted,114494.03008793297 +SCWA - LAGUNA/VINEYARD,Per Capita Income,Pop Weighted,Population Weighted,41415.71495309661 +SCWA - LAGUNA/VINEYARD,Housing Costs Over 30% Income,Percent,Households,32.04 +SCWA - LAGUNA/VINEYARD,Housing Costs Over 50% Income,Percent,Households,14.9 +SCWA MATHER-SUNRISE,Population Total,Count,Population,18249 +SCWA MATHER-SUNRISE,Hispanic / Latino,Count,Population,2708 +SCWA MATHER-SUNRISE,White,Count,Population,8114 +SCWA MATHER-SUNRISE,Black-/ African American,Count,Population,1553 +SCWA MATHER-SUNRISE,Native American,Count,Population,23 +SCWA MATHER-SUNRISE,Asian,Count,Population,4507 +SCWA MATHER-SUNRISE,Pacific Islander,Count,Population,164 +SCWA MATHER-SUNRISE,Other / Multiple,Count,Population,1180 +SCWA MATHER-SUNRISE,Hispanic / Latino,Percent,Population,14.84 +SCWA MATHER-SUNRISE,White,Percent,Population,44.47 +SCWA MATHER-SUNRISE,Black-/ African American,Percent,Population,8.51 +SCWA MATHER-SUNRISE,Native American,Percent,Population,0.12 +SCWA MATHER-SUNRISE,Asian,Percent,Population,24.7 +SCWA MATHER-SUNRISE,Pacific Islander,Percent,Population,0.9 +SCWA MATHER-SUNRISE,Other / Multiple,Percent,Population,6.47 +SCWA MATHER-SUNRISE,Poverty Total Assessed,Count,Population,18211 +SCWA MATHER-SUNRISE,Poverty Below,Count,Population,1005 +SCWA MATHER-SUNRISE,Poverty Above,Count,Population,17206 +SCWA MATHER-SUNRISE,Poverty Rate,Percent,Population,5.52 +SCWA MATHER-SUNRISE,Households Total,Count,Households,5503 +SCWA MATHER-SUNRISE,Income Below 10k,Count,Households,228 +SCWA MATHER-SUNRISE,Income 10k-15k,Count,Households,35 +SCWA MATHER-SUNRISE,Income 15k-20k,Count,Households,97 +SCWA MATHER-SUNRISE,Income 20k-25k,Count,Households,57 +SCWA MATHER-SUNRISE,Income 25k-30k,Count,Households,68 +SCWA MATHER-SUNRISE,Income 30k-35k,Count,Households,39 +SCWA MATHER-SUNRISE,Income 35k-40k,Count,Households,12 +SCWA MATHER-SUNRISE,Income 40k-45k,Count,Households,20 +SCWA MATHER-SUNRISE,Income 45k-50k,Count,Households,36 +SCWA MATHER-SUNRISE,Income 50k-60k,Count,Households,189 +SCWA MATHER-SUNRISE,Income 60k-75k,Count,Households,320 +SCWA MATHER-SUNRISE,Income 75k-100k,Count,Households,533 +SCWA MATHER-SUNRISE,Income 100k-125k,Count,Households,645 +SCWA MATHER-SUNRISE,Income 125k-150k,Count,Households,755 +SCWA MATHER-SUNRISE,Income 150k-200k,Count,Households,1003 +SCWA MATHER-SUNRISE,Income Above 200k,Count,Households,1469 +SCWA MATHER-SUNRISE,Income 0-25k,Count,Households,416 +SCWA MATHER-SUNRISE,Income 25k-50k,Count,Households,174 +SCWA MATHER-SUNRISE,Income 50k-75k,Count,Households,509 +SCWA MATHER-SUNRISE,Income 0-50k,Count,Households,590 +SCWA MATHER-SUNRISE,Income 50k-100k,Count,Households,1042 +SCWA MATHER-SUNRISE,Income 100k-150k,Count,Households,1399 +SCWA MATHER-SUNRISE,Mortgage Total,Count,Households,3756 +SCWA MATHER-SUNRISE,Mortgage Over 30% Income,Count,Households,881 +SCWA MATHER-SUNRISE,Mortgage Over 50% Income,Count,Households,266 +SCWA MATHER-SUNRISE,No Mortgage Total,Count,Households,855 +SCWA MATHER-SUNRISE,No Mortgage Over 30% Income,Count,Households,60 +SCWA MATHER-SUNRISE,No Mortgage Over 50% Income,Count,Households,43 +SCWA MATHER-SUNRISE,Rent Total,Count,Households,893 +SCWA MATHER-SUNRISE,Rent Over 30% Income,Count,Households,318 +SCWA MATHER-SUNRISE,Rent Over 50% Income,Count,Households,167 +SCWA MATHER-SUNRISE,Average Household Size,Hh Weighted,Household Weighted,3.296327128817464 +SCWA MATHER-SUNRISE,Median Household Income,Hh Weighted,Household Weighted,147818.00762610507 +SCWA MATHER-SUNRISE,Per Capita Income,Pop Weighted,Population Weighted,47448.3691116171 +SCWA MATHER-SUNRISE,Housing Costs Over 30% Income,Percent,Households,22.89 +SCWA MATHER-SUNRISE,Housing Costs Over 50% Income,Percent,Households,8.66 +SEQUOIA WATER ASSOC,Population Total,Count,Population,0 +SEQUOIA WATER ASSOC,Hispanic / Latino,Count,Population,0 +SEQUOIA WATER ASSOC,White,Count,Population,0 +SEQUOIA WATER ASSOC,Black-/ African American,Count,Population,0 +SEQUOIA WATER ASSOC,Native American,Count,Population,0 +SEQUOIA WATER ASSOC,Asian,Count,Population,0 +SEQUOIA WATER ASSOC,Pacific Islander,Count,Population,0 +SEQUOIA WATER ASSOC,Other / Multiple,Count,Population,0 +SEQUOIA WATER ASSOC,Hispanic / Latino,Percent,Population,44.6 +SEQUOIA WATER ASSOC,White,Percent,Population,45.84 +SEQUOIA WATER ASSOC,Black-/ African American,Percent,Population,0 +SEQUOIA WATER ASSOC,Native American,Percent,Population,0 +SEQUOIA WATER ASSOC,Asian,Percent,Population,5.93 +SEQUOIA WATER ASSOC,Pacific Islander,Percent,Population,0 +SEQUOIA WATER ASSOC,Other / Multiple,Percent,Population,3.63 +SEQUOIA WATER ASSOC,Poverty Total Assessed,Count,Population,0 +SEQUOIA WATER ASSOC,Poverty Below,Count,Population,0 +SEQUOIA WATER ASSOC,Poverty Above,Count,Population,0 +SEQUOIA WATER ASSOC,Poverty Rate,Percent,Population,15.75 +SEQUOIA WATER ASSOC,Households Total,Count,Households,0 +SEQUOIA WATER ASSOC,Income Below 10k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 10k-15k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 15k-20k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 20k-25k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 25k-30k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 30k-35k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 35k-40k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 40k-45k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 45k-50k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 50k-60k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 60k-75k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 75k-100k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 100k-125k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 125k-150k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 150k-200k,Count,Households,0 +SEQUOIA WATER ASSOC,Income Above 200k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 0-25k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 25k-50k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 50k-75k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 0-50k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 50k-100k,Count,Households,0 +SEQUOIA WATER ASSOC,Income 100k-150k,Count,Households,0 +SEQUOIA WATER ASSOC,Mortgage Total,Count,Households,0 +SEQUOIA WATER ASSOC,Mortgage Over 30% Income,Count,Households,0 +SEQUOIA WATER ASSOC,Mortgage Over 50% Income,Count,Households,0 +SEQUOIA WATER ASSOC,No Mortgage Total,Count,Households,0 +SEQUOIA WATER ASSOC,No Mortgage Over 30% Income,Count,Households,0 +SEQUOIA WATER ASSOC,No Mortgage Over 50% Income,Count,Households,0 +SEQUOIA WATER ASSOC,Rent Total,Count,Households,0 +SEQUOIA WATER ASSOC,Rent Over 30% Income,Count,Households,0 +SEQUOIA WATER ASSOC,Rent Over 50% Income,Count,Households,0 +SEQUOIA WATER ASSOC,Average Household Size,Hh Weighted,Household Weighted,2.49 +SEQUOIA WATER ASSOC,Median Household Income,Hh Weighted,Household Weighted,68248 +SEQUOIA WATER ASSOC,Per Capita Income,Pop Weighted,Population Weighted,38950 +SEQUOIA WATER ASSOC,Housing Costs Over 30% Income,Percent,Households,24.49 +SEQUOIA WATER ASSOC,Housing Costs Over 50% Income,Percent,Households,14.65 +SOUTHWEST TRACT W M D [SWS],Population Total,Count,Population,174 +SOUTHWEST TRACT W M D [SWS],Hispanic / Latino,Count,Population,29 +SOUTHWEST TRACT W M D [SWS],White,Count,Population,42 +SOUTHWEST TRACT W M D [SWS],Black-/ African American,Count,Population,24 +SOUTHWEST TRACT W M D [SWS],Native American,Count,Population,3 +SOUTHWEST TRACT W M D [SWS],Asian,Count,Population,75 +SOUTHWEST TRACT W M D [SWS],Pacific Islander,Count,Population,1 +SOUTHWEST TRACT W M D [SWS],Other / Multiple,Count,Population,0 +SOUTHWEST TRACT W M D [SWS],Hispanic / Latino,Percent,Population,16.58 +SOUTHWEST TRACT W M D [SWS],White,Percent,Population,24.48 +SOUTHWEST TRACT W M D [SWS],Black-/ African American,Percent,Population,13.69 +SOUTHWEST TRACT W M D [SWS],Native American,Percent,Population,1.55 +SOUTHWEST TRACT W M D [SWS],Asian,Percent,Population,43.11 +SOUTHWEST TRACT W M D [SWS],Pacific Islander,Percent,Population,0.6 +SOUTHWEST TRACT W M D [SWS],Other / Multiple,Percent,Population,0 +SOUTHWEST TRACT W M D [SWS],Poverty Total Assessed,Count,Population,174 +SOUTHWEST TRACT W M D [SWS],Poverty Below,Count,Population,38 +SOUTHWEST TRACT W M D [SWS],Poverty Above,Count,Population,136 +SOUTHWEST TRACT W M D [SWS],Poverty Rate,Percent,Population,21.83 +SOUTHWEST TRACT W M D [SWS],Households Total,Count,Households,57 +SOUTHWEST TRACT W M D [SWS],Income Below 10k,Count,Households,1 +SOUTHWEST TRACT W M D [SWS],Income 10k-15k,Count,Households,2 +SOUTHWEST TRACT W M D [SWS],Income 15k-20k,Count,Households,7 +SOUTHWEST TRACT W M D [SWS],Income 20k-25k,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],Income 25k-30k,Count,Households,7 +SOUTHWEST TRACT W M D [SWS],Income 30k-35k,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],Income 35k-40k,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],Income 40k-45k,Count,Households,10 +SOUTHWEST TRACT W M D [SWS],Income 45k-50k,Count,Households,12 +SOUTHWEST TRACT W M D [SWS],Income 50k-60k,Count,Households,3 +SOUTHWEST TRACT W M D [SWS],Income 60k-75k,Count,Households,2 +SOUTHWEST TRACT W M D [SWS],Income 75k-100k,Count,Households,5 +SOUTHWEST TRACT W M D [SWS],Income 100k-125k,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],Income 125k-150k,Count,Households,1 +SOUTHWEST TRACT W M D [SWS],Income 150k-200k,Count,Households,2 +SOUTHWEST TRACT W M D [SWS],Income Above 200k,Count,Households,4 +SOUTHWEST TRACT W M D [SWS],Income 0-25k,Count,Households,10 +SOUTHWEST TRACT W M D [SWS],Income 25k-50k,Count,Households,29 +SOUTHWEST TRACT W M D [SWS],Income 50k-75k,Count,Households,6 +SOUTHWEST TRACT W M D [SWS],Income 0-50k,Count,Households,39 +SOUTHWEST TRACT W M D [SWS],Income 50k-100k,Count,Households,10 +SOUTHWEST TRACT W M D [SWS],Income 100k-150k,Count,Households,1 +SOUTHWEST TRACT W M D [SWS],Mortgage Total,Count,Households,3 +SOUTHWEST TRACT W M D [SWS],Mortgage Over 30% Income,Count,Households,1 +SOUTHWEST TRACT W M D [SWS],Mortgage Over 50% Income,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],No Mortgage Total,Count,Households,8 +SOUTHWEST TRACT W M D [SWS],No Mortgage Over 30% Income,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],No Mortgage Over 50% Income,Count,Households,0 +SOUTHWEST TRACT W M D [SWS],Rent Total,Count,Households,45 +SOUTHWEST TRACT W M D [SWS],Rent Over 30% Income,Count,Households,29 +SOUTHWEST TRACT W M D [SWS],Rent Over 50% Income,Count,Households,7 +SOUTHWEST TRACT W M D [SWS],Average Household Size,Hh Weighted,Household Weighted,3.04 +SOUTHWEST TRACT W M D [SWS],Median Household Income,Hh Weighted,Household Weighted,45671 +SOUTHWEST TRACT W M D [SWS],Per Capita Income,Pop Weighted,Population Weighted,36348 +SOUTHWEST TRACT W M D [SWS],Housing Costs Over 30% Income,Percent,Households,52.53 +SOUTHWEST TRACT W M D [SWS],Housing Costs Over 50% Income,Percent,Households,12.4 +SPINDRIFT MARINA,Population Total,Count,Population,0 +SPINDRIFT MARINA,Hispanic / Latino,Count,Population,0 +SPINDRIFT MARINA,White,Count,Population,0 +SPINDRIFT MARINA,Black-/ African American,Count,Population,0 +SPINDRIFT MARINA,Native American,Count,Population,0 +SPINDRIFT MARINA,Asian,Count,Population,0 +SPINDRIFT MARINA,Pacific Islander,Count,Population,0 +SPINDRIFT MARINA,Other / Multiple,Count,Population,0 +SPINDRIFT MARINA,Hispanic / Latino,Percent,Population,3.9 +SPINDRIFT MARINA,White,Percent,Population,89.23 +SPINDRIFT MARINA,Black-/ African American,Percent,Population,3.23 +SPINDRIFT MARINA,Native American,Percent,Population,0 +SPINDRIFT MARINA,Asian,Percent,Population,0 +SPINDRIFT MARINA,Pacific Islander,Percent,Population,0 +SPINDRIFT MARINA,Other / Multiple,Percent,Population,3.63 +SPINDRIFT MARINA,Poverty Total Assessed,Count,Population,0 +SPINDRIFT MARINA,Poverty Below,Count,Population,0 +SPINDRIFT MARINA,Poverty Above,Count,Population,0 +SPINDRIFT MARINA,Poverty Rate,Percent,Population,35.94 +SPINDRIFT MARINA,Households Total,Count,Households,0 +SPINDRIFT MARINA,Income Below 10k,Count,Households,0 +SPINDRIFT MARINA,Income 10k-15k,Count,Households,0 +SPINDRIFT MARINA,Income 15k-20k,Count,Households,0 +SPINDRIFT MARINA,Income 20k-25k,Count,Households,0 +SPINDRIFT MARINA,Income 25k-30k,Count,Households,0 +SPINDRIFT MARINA,Income 30k-35k,Count,Households,0 +SPINDRIFT MARINA,Income 35k-40k,Count,Households,0 +SPINDRIFT MARINA,Income 40k-45k,Count,Households,0 +SPINDRIFT MARINA,Income 45k-50k,Count,Households,0 +SPINDRIFT MARINA,Income 50k-60k,Count,Households,0 +SPINDRIFT MARINA,Income 60k-75k,Count,Households,0 +SPINDRIFT MARINA,Income 75k-100k,Count,Households,0 +SPINDRIFT MARINA,Income 100k-125k,Count,Households,0 +SPINDRIFT MARINA,Income 125k-150k,Count,Households,0 +SPINDRIFT MARINA,Income 150k-200k,Count,Households,0 +SPINDRIFT MARINA,Income Above 200k,Count,Households,0 +SPINDRIFT MARINA,Income 0-25k,Count,Households,0 +SPINDRIFT MARINA,Income 25k-50k,Count,Households,0 +SPINDRIFT MARINA,Income 50k-75k,Count,Households,0 +SPINDRIFT MARINA,Income 0-50k,Count,Households,0 +SPINDRIFT MARINA,Income 50k-100k,Count,Households,0 +SPINDRIFT MARINA,Income 100k-150k,Count,Households,0 +SPINDRIFT MARINA,Mortgage Total,Count,Households,0 +SPINDRIFT MARINA,Mortgage Over 30% Income,Count,Households,0 +SPINDRIFT MARINA,Mortgage Over 50% Income,Count,Households,0 +SPINDRIFT MARINA,No Mortgage Total,Count,Households,0 +SPINDRIFT MARINA,No Mortgage Over 30% Income,Count,Households,0 +SPINDRIFT MARINA,No Mortgage Over 50% Income,Count,Households,0 +SPINDRIFT MARINA,Rent Total,Count,Households,0 +SPINDRIFT MARINA,Rent Over 30% Income,Count,Households,0 +SPINDRIFT MARINA,Rent Over 50% Income,Count,Households,0 +SPINDRIFT MARINA,Average Household Size,Hh Weighted,Household Weighted,1.7899999999999998 +SPINDRIFT MARINA,Median Household Income,Hh Weighted,Household Weighted,38125 +SPINDRIFT MARINA,Per Capita Income,Pop Weighted,Population Weighted,33103 +SPINDRIFT MARINA,Housing Costs Over 30% Income,Percent,Households,28.02 +SPINDRIFT MARINA,Housing Costs Over 50% Income,Percent,Households,23.19 +TOKAY PARK WATER CO,Population Total,Count,Population,652 +TOKAY PARK WATER CO,Hispanic / Latino,Count,Population,214 +TOKAY PARK WATER CO,White,Count,Population,134 +TOKAY PARK WATER CO,Black-/ African American,Count,Population,37 +TOKAY PARK WATER CO,Native American,Count,Population,0 +TOKAY PARK WATER CO,Asian,Count,Population,239 +TOKAY PARK WATER CO,Pacific Islander,Count,Population,0 +TOKAY PARK WATER CO,Other / Multiple,Count,Population,28 +TOKAY PARK WATER CO,Hispanic / Latino,Percent,Population,32.8 +TOKAY PARK WATER CO,White,Percent,Population,20.55 +TOKAY PARK WATER CO,Black-/ African American,Percent,Population,5.61 +TOKAY PARK WATER CO,Native American,Percent,Population,0 +TOKAY PARK WATER CO,Asian,Percent,Population,36.69 +TOKAY PARK WATER CO,Pacific Islander,Percent,Population,0 +TOKAY PARK WATER CO,Other / Multiple,Percent,Population,4.35 +TOKAY PARK WATER CO,Poverty Total Assessed,Count,Population,652 +TOKAY PARK WATER CO,Poverty Below,Count,Population,113 +TOKAY PARK WATER CO,Poverty Above,Count,Population,539 +TOKAY PARK WATER CO,Poverty Rate,Percent,Population,17.29 +TOKAY PARK WATER CO,Households Total,Count,Households,173 +TOKAY PARK WATER CO,Income Below 10k,Count,Households,2 +TOKAY PARK WATER CO,Income 10k-15k,Count,Households,2 +TOKAY PARK WATER CO,Income 15k-20k,Count,Households,3 +TOKAY PARK WATER CO,Income 20k-25k,Count,Households,21 +TOKAY PARK WATER CO,Income 25k-30k,Count,Households,0 +TOKAY PARK WATER CO,Income 30k-35k,Count,Households,0 +TOKAY PARK WATER CO,Income 35k-40k,Count,Households,13 +TOKAY PARK WATER CO,Income 40k-45k,Count,Households,13 +TOKAY PARK WATER CO,Income 45k-50k,Count,Households,10 +TOKAY PARK WATER CO,Income 50k-60k,Count,Households,18 +TOKAY PARK WATER CO,Income 60k-75k,Count,Households,27 +TOKAY PARK WATER CO,Income 75k-100k,Count,Households,36 +TOKAY PARK WATER CO,Income 100k-125k,Count,Households,14 +TOKAY PARK WATER CO,Income 125k-150k,Count,Households,4 +TOKAY PARK WATER CO,Income 150k-200k,Count,Households,10 +TOKAY PARK WATER CO,Income Above 200k,Count,Households,0 +TOKAY PARK WATER CO,Income 0-25k,Count,Households,27 +TOKAY PARK WATER CO,Income 25k-50k,Count,Households,36 +TOKAY PARK WATER CO,Income 50k-75k,Count,Households,45 +TOKAY PARK WATER CO,Income 0-50k,Count,Households,64 +TOKAY PARK WATER CO,Income 50k-100k,Count,Households,81 +TOKAY PARK WATER CO,Income 100k-150k,Count,Households,18 +TOKAY PARK WATER CO,Mortgage Total,Count,Households,81 +TOKAY PARK WATER CO,Mortgage Over 30% Income,Count,Households,38 +TOKAY PARK WATER CO,Mortgage Over 50% Income,Count,Households,11 +TOKAY PARK WATER CO,No Mortgage Total,Count,Households,44 +TOKAY PARK WATER CO,No Mortgage Over 30% Income,Count,Households,0 +TOKAY PARK WATER CO,No Mortgage Over 50% Income,Count,Households,0 +TOKAY PARK WATER CO,Rent Total,Count,Households,48 +TOKAY PARK WATER CO,Rent Over 30% Income,Count,Households,32 +TOKAY PARK WATER CO,Rent Over 50% Income,Count,Households,12 +TOKAY PARK WATER CO,Average Household Size,Hh Weighted,Household Weighted,3.7579731008260437 +TOKAY PARK WATER CO,Median Household Income,Hh Weighted,Household Weighted,62802.23785121953 +TOKAY PARK WATER CO,Per Capita Income,Pop Weighted,Population Weighted,19400.04804878149 +TOKAY PARK WATER CO,Housing Costs Over 30% Income,Percent,Households,40.57 +TOKAY PARK WATER CO,Housing Costs Over 50% Income,Percent,Households,13.58 +TUNNEL TRAILER PARK,Population Total,Count,Population,0 +TUNNEL TRAILER PARK,Hispanic / Latino,Count,Population,0 +TUNNEL TRAILER PARK,White,Count,Population,0 +TUNNEL TRAILER PARK,Black-/ African American,Count,Population,0 +TUNNEL TRAILER PARK,Native American,Count,Population,0 +TUNNEL TRAILER PARK,Asian,Count,Population,0 +TUNNEL TRAILER PARK,Pacific Islander,Count,Population,0 +TUNNEL TRAILER PARK,Other / Multiple,Count,Population,0 +TUNNEL TRAILER PARK,Hispanic / Latino,Percent,Population,49.74 +TUNNEL TRAILER PARK,White,Percent,Population,34.94 +TUNNEL TRAILER PARK,Black-/ African American,Percent,Population,0 +TUNNEL TRAILER PARK,Native American,Percent,Population,0 +TUNNEL TRAILER PARK,Asian,Percent,Population,4.65 +TUNNEL TRAILER PARK,Pacific Islander,Percent,Population,0 +TUNNEL TRAILER PARK,Other / Multiple,Percent,Population,10.67 +TUNNEL TRAILER PARK,Poverty Total Assessed,Count,Population,0 +TUNNEL TRAILER PARK,Poverty Below,Count,Population,0 +TUNNEL TRAILER PARK,Poverty Above,Count,Population,0 +TUNNEL TRAILER PARK,Poverty Rate,Percent,Population,0 +TUNNEL TRAILER PARK,Households Total,Count,Households,0 +TUNNEL TRAILER PARK,Income Below 10k,Count,Households,0 +TUNNEL TRAILER PARK,Income 10k-15k,Count,Households,0 +TUNNEL TRAILER PARK,Income 15k-20k,Count,Households,0 +TUNNEL TRAILER PARK,Income 20k-25k,Count,Households,0 +TUNNEL TRAILER PARK,Income 25k-30k,Count,Households,0 +TUNNEL TRAILER PARK,Income 30k-35k,Count,Households,0 +TUNNEL TRAILER PARK,Income 35k-40k,Count,Households,0 +TUNNEL TRAILER PARK,Income 40k-45k,Count,Households,0 +TUNNEL TRAILER PARK,Income 45k-50k,Count,Households,0 +TUNNEL TRAILER PARK,Income 50k-60k,Count,Households,0 +TUNNEL TRAILER PARK,Income 60k-75k,Count,Households,0 +TUNNEL TRAILER PARK,Income 75k-100k,Count,Households,0 +TUNNEL TRAILER PARK,Income 100k-125k,Count,Households,0 +TUNNEL TRAILER PARK,Income 125k-150k,Count,Households,0 +TUNNEL TRAILER PARK,Income 150k-200k,Count,Households,0 +TUNNEL TRAILER PARK,Income Above 200k,Count,Households,0 +TUNNEL TRAILER PARK,Income 0-25k,Count,Households,0 +TUNNEL TRAILER PARK,Income 25k-50k,Count,Households,0 +TUNNEL TRAILER PARK,Income 50k-75k,Count,Households,0 +TUNNEL TRAILER PARK,Income 0-50k,Count,Households,0 +TUNNEL TRAILER PARK,Income 50k-100k,Count,Households,0 +TUNNEL TRAILER PARK,Income 100k-150k,Count,Households,0 +TUNNEL TRAILER PARK,Mortgage Total,Count,Households,0 +TUNNEL TRAILER PARK,Mortgage Over 30% Income,Count,Households,0 +TUNNEL TRAILER PARK,Mortgage Over 50% Income,Count,Households,0 +TUNNEL TRAILER PARK,No Mortgage Total,Count,Households,0 +TUNNEL TRAILER PARK,No Mortgage Over 30% Income,Count,Households,0 +TUNNEL TRAILER PARK,No Mortgage Over 50% Income,Count,Households,0 +TUNNEL TRAILER PARK,Rent Total,Count,Households,0 +TUNNEL TRAILER PARK,Rent Over 30% Income,Count,Households,0 +TUNNEL TRAILER PARK,Rent Over 50% Income,Count,Households,0 +TUNNEL TRAILER PARK,Average Household Size,Hh Weighted,Household Weighted,2.95 +TUNNEL TRAILER PARK,Median Household Income,Hh Weighted,Household Weighted,153092 +TUNNEL TRAILER PARK,Per Capita Income,Pop Weighted,Population Weighted,42507 +TUNNEL TRAILER PARK,Housing Costs Over 30% Income,Percent,Households,20.3 +TUNNEL TRAILER PARK,Housing Costs Over 50% Income,Percent,Households,0 +"VIEIRA'S RESORT, INC",Population Total,Count,Population,4 +"VIEIRA'S RESORT, INC",Hispanic / Latino,Count,Population,2 +"VIEIRA'S RESORT, INC",White,Count,Population,2 +"VIEIRA'S RESORT, INC",Black-/ African American,Count,Population,0 +"VIEIRA'S RESORT, INC",Native American,Count,Population,0 +"VIEIRA'S RESORT, INC",Asian,Count,Population,0 +"VIEIRA'S RESORT, INC",Pacific Islander,Count,Population,0 +"VIEIRA'S RESORT, INC",Other / Multiple,Count,Population,0 +"VIEIRA'S RESORT, INC",Hispanic / Latino,Percent,Population,41.43 +"VIEIRA'S RESORT, INC",White,Percent,Population,52.47 +"VIEIRA'S RESORT, INC",Black-/ African American,Percent,Population,0 +"VIEIRA'S RESORT, INC",Native American,Percent,Population,0 +"VIEIRA'S RESORT, INC",Asian,Percent,Population,4.55 +"VIEIRA'S RESORT, INC",Pacific Islander,Percent,Population,0 +"VIEIRA'S RESORT, INC",Other / Multiple,Percent,Population,1.56 +"VIEIRA'S RESORT, INC",Poverty Total Assessed,Count,Population,4 +"VIEIRA'S RESORT, INC",Poverty Below,Count,Population,1 +"VIEIRA'S RESORT, INC",Poverty Above,Count,Population,3 +"VIEIRA'S RESORT, INC",Poverty Rate,Percent,Population,22.6 +"VIEIRA'S RESORT, INC",Households Total,Count,Households,2 +"VIEIRA'S RESORT, INC",Income Below 10k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 10k-15k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 15k-20k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 20k-25k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 25k-30k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 30k-35k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 35k-40k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 40k-45k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 45k-50k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 50k-60k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 60k-75k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 75k-100k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 100k-125k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 125k-150k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 150k-200k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income Above 200k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 0-25k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 25k-50k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 50k-75k,Count,Households,0 +"VIEIRA'S RESORT, INC",Income 0-50k,Count,Households,1 +"VIEIRA'S RESORT, INC",Income 50k-100k,Count,Households,1 +"VIEIRA'S RESORT, INC",Income 100k-150k,Count,Households,1 +"VIEIRA'S RESORT, INC",Mortgage Total,Count,Households,1 +"VIEIRA'S RESORT, INC",Mortgage Over 30% Income,Count,Households,1 +"VIEIRA'S RESORT, INC",Mortgage Over 50% Income,Count,Households,0 +"VIEIRA'S RESORT, INC",No Mortgage Total,Count,Households,1 +"VIEIRA'S RESORT, INC",No Mortgage Over 30% Income,Count,Households,0 +"VIEIRA'S RESORT, INC",No Mortgage Over 50% Income,Count,Households,0 +"VIEIRA'S RESORT, INC",Rent Total,Count,Households,0 +"VIEIRA'S RESORT, INC",Rent Over 30% Income,Count,Households,0 +"VIEIRA'S RESORT, INC",Rent Over 50% Income,Count,Households,0 +"VIEIRA'S RESORT, INC",Average Household Size,Hh Weighted,Household Weighted,2.03 +"VIEIRA'S RESORT, INC",Median Household Income,Hh Weighted,Household Weighted,51977 +"VIEIRA'S RESORT, INC",Per Capita Income,Pop Weighted,Population Weighted,40522 +"VIEIRA'S RESORT, INC",Housing Costs Over 30% Income,Percent,Households,40.53 +"VIEIRA'S RESORT, INC",Housing Costs Over 50% Income,Percent,Households,21.84 +WESTERNER MOBILE HOME PARK,Population Total,Count,Population,32 +WESTERNER MOBILE HOME PARK,Hispanic / Latino,Count,Population,6 +WESTERNER MOBILE HOME PARK,White,Count,Population,6 +WESTERNER MOBILE HOME PARK,Black-/ African American,Count,Population,9 +WESTERNER MOBILE HOME PARK,Native American,Count,Population,0 +WESTERNER MOBILE HOME PARK,Asian,Count,Population,10 +WESTERNER MOBILE HOME PARK,Pacific Islander,Count,Population,0 +WESTERNER MOBILE HOME PARK,Other / Multiple,Count,Population,1 +WESTERNER MOBILE HOME PARK,Hispanic / Latino,Percent,Population,17.59 +WESTERNER MOBILE HOME PARK,White,Percent,Population,17.62 +WESTERNER MOBILE HOME PARK,Black-/ African American,Percent,Population,28.31 +WESTERNER MOBILE HOME PARK,Native American,Percent,Population,0.55 +WESTERNER MOBILE HOME PARK,Asian,Percent,Population,31.36 +WESTERNER MOBILE HOME PARK,Pacific Islander,Percent,Population,0 +WESTERNER MOBILE HOME PARK,Other / Multiple,Percent,Population,4.57 +WESTERNER MOBILE HOME PARK,Poverty Total Assessed,Count,Population,31 +WESTERNER MOBILE HOME PARK,Poverty Below,Count,Population,7 +WESTERNER MOBILE HOME PARK,Poverty Above,Count,Population,24 +WESTERNER MOBILE HOME PARK,Poverty Rate,Percent,Population,23.76 +WESTERNER MOBILE HOME PARK,Households Total,Count,Households,10 +WESTERNER MOBILE HOME PARK,Income Below 10k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 10k-15k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 15k-20k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 20k-25k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 25k-30k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 30k-35k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 35k-40k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 40k-45k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 45k-50k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 50k-60k,Count,Households,2 +WESTERNER MOBILE HOME PARK,Income 60k-75k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 75k-100k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 100k-125k,Count,Households,2 +WESTERNER MOBILE HOME PARK,Income 125k-150k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 150k-200k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income Above 200k,Count,Households,0 +WESTERNER MOBILE HOME PARK,Income 0-25k,Count,Households,1 +WESTERNER MOBILE HOME PARK,Income 25k-50k,Count,Households,2 +WESTERNER MOBILE HOME PARK,Income 50k-75k,Count,Households,3 +WESTERNER MOBILE HOME PARK,Income 0-50k,Count,Households,3 +WESTERNER MOBILE HOME PARK,Income 50k-100k,Count,Households,4 +WESTERNER MOBILE HOME PARK,Income 100k-150k,Count,Households,2 +WESTERNER MOBILE HOME PARK,Mortgage Total,Count,Households,4 +WESTERNER MOBILE HOME PARK,Mortgage Over 30% Income,Count,Households,2 +WESTERNER MOBILE HOME PARK,Mortgage Over 50% Income,Count,Households,1 +WESTERNER MOBILE HOME PARK,No Mortgage Total,Count,Households,1 +WESTERNER MOBILE HOME PARK,No Mortgage Over 30% Income,Count,Households,0 +WESTERNER MOBILE HOME PARK,No Mortgage Over 50% Income,Count,Households,0 +WESTERNER MOBILE HOME PARK,Rent Total,Count,Households,5 +WESTERNER MOBILE HOME PARK,Rent Over 30% Income,Count,Households,3 +WESTERNER MOBILE HOME PARK,Rent Over 50% Income,Count,Households,2 +WESTERNER MOBILE HOME PARK,Average Household Size,Hh Weighted,Household Weighted,3.16 +WESTERNER MOBILE HOME PARK,Median Household Income,Hh Weighted,Household Weighted,59296.00000000001 +WESTERNER MOBILE HOME PARK,Per Capita Income,Pop Weighted,Population Weighted,23437 +WESTERNER MOBILE HOME PARK,Housing Costs Over 30% Income,Percent,Households,56.87 +WESTERNER MOBILE HOME PARK,Housing Costs Over 50% Income,Percent,Households,29.49