This map contains state and county boundaries for the United States for every decennial census from 1790-2020. Original data is from the NHGIS and was simplified to reduce file size using mapshaper.org

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

A web map used to access tax parcel, boundary, ownership, acreage, survey, zoning and tax information. Errors and Omissions Do Exist.The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.https://www.fayette-co-oh.com/Fayette County ProfileFayette County is a county located in the U.S. state of Ohio. Its county seat is Washington Court House. Fayette County was formed on March 1, 1810 from portions of Highland County and Ross County. It was named after Marie-Joseph Motier, Marquis de La Fayette, a French general and politician who took the side of the Colonials during the American Revolutionary War and who played an important role in the French Revolution.Fayette County is a part of the Virginia Military survey, which was reserved in 1783, to be allotted to Virginia soldiers. This district includes the entire counties of Adams, Brown, Clermont, Clinton, Highland, Fayette, Madison and Union; and a portion of the counties of Scioto, Pike, Ross, Pickaway, Franklin, Delaware, Marion, Hardin, Logan, Champaign, Clarke, Greene, Warren and Hamilton.Fayette County was formed January 19, 1810 (took effect March 1st) from Ross and Highland counties. Beginning at the southwest corner of Pickaway, running north “with the line of said county to the corner of Madison; thence west with the line of said Madison county to the line of Greene county; thence south with the line of Greene county to the southeast corner thereof; thence east five miles; thence south to the line of Highland county; thence east with said line to Paint Creek; thence in a straight line to the beginning.” All the lower portion was taken from Highland and the upper from Ross.The first portion of land entered within the territory of what is now Fayette county, was a part of original surveys Nos. 243 and 772, lying partly in Clinton county. The first survey lying wholly within Fayette county was No. 463, in what is now Madison township, surveyed for Thomas Overton by John O’Bannon June 30, 1776.The original townships were Jefferson, Greene, Wayne, Madison, Paint and Union. Concord township was formed in April 1818, from Greene. Marion township was formed in June, 1840 from Madison. Perry township was formed June 4, 1845, from Wayne and Greene. Jasper township was formed from Jefferson and Concord December 2, 1845.Washington C.H. was laid out originally on a part of entry 757, which contained 1200 acres and belonged to Benjamin Temple, of Logan county, Kentucky, who donated 150 acres to Fayette county, on condition that it be used as the site of the county seat. The deed of conveyance was made December 1, 1810, by Thomas S. Hind, attorney for Temple, to Robert Stewart, who was appointed by the legislature as director for the town of Washington. The town was laid off some time between December 1, 1810, and February 26, 1811, the latter being the date of the record of the town plat.Bloomingburg (originally called New Lexington) was laid out in 1815, by Solomon Bowers, and originally contained 34 and ¾ acres. On March 4, 1816, Bowers laid out and added twenty more lots. The name of the town was later changed to Bloomingburg by act of the legislature. The town was incorporated by act of the legislature, February 5, 1847.Jeffersonville was laid out March 1, 1831, by Walter B. Write and Chipman Robinson, on 100 acres of land belonging to them, they started selling the lots at $5 each. The town incorporated March 17, 1838. The first house was erected by Robert Wyley.The first railroad, now the C. & M. V., was completed in 1852; the second, now the Detroit Southern, in 1875; the third, now the C.H. & D. in 1879; and the fourth, now the B. & O. S. W., in 1884.The first permanent settler (probably) was a Mr. Wolf who settled in what is now Wayne township, in about the year 1796. - Circa 1886 - Map of Fayette County, Ohio. Issued by the Fayette County Record.

This layer shows Households by Type. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Average Household Size and the Total Households in a bi-variate map. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B11001, B25010, B25044, DP02, DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The 1998 Dress Rehearsal was conducted as a prelude to the United States Census of Population and Housing, 2000, in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. This collection contains map files showing various levels of geography (in the form of Census Tract Outline Maps, Voting District/State Legislative District Outline Maps, and County Block Maps), TIGER/Line digital files, and Corner Point files for the Census 2000 Dress Rehearsal sites. The Corner Point data files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal Public Law (P.L.) 94-171 map products. These files include a sheet identifier, minimum and maximum longitude, minimum and maximum latitude, and the map scale (integer value) for each map sheet. The latitude and longitude coordinates are in decimal degrees and expressed as integer values with six implied decimal places. There is a separate Corner Point File for each of the three map types: County Block Map, Census Tract Outline Map, and Voting District/State Legislative District Outline Map. Each of the three map file types is provided in two formats: Portable Document Format (PDF), for viewing, and Hewlett-Packard Graphics Language (HP-GL) format, for plotting. The County Block Maps show the greatest detail and the most complete set of geographic information of all the maps. These large-scale maps depict the smallest geographic entities for which the Census Bureau presents data -- the census blocks -- by displaying the features that delineate them and the numbers that identify them. These maps show the boundaries, names, and codes for American Indian/Alaska Native areas, county subdivisions, places, census tracts, and, for this series, the geographic entities that the states delineated in Phase 2, Voting District Project, of the Redistricting Data Program. The HP-GL version of the County Block Maps is broken down into index maps and map sheets. The map sheets cover a small area, and the index maps are composed of multiple map sheets, showing the entire area. The intent of the County Block Map series is to provide a map for each county on the smallest possible number of map sheets at the maximum practical scale, dependent on the area size of the county and the density of the block pattern. The latter affects the display of block numbers and feature identifiers. The Census Tract Outline Maps show the boundaries and numbers of census tracts, and name the features underlying the boundaries. These maps also show the boundaries and names of counties, county subdivisions, and places. They identify census tracts in relation to governmental unit boundaries. The mapping unit is the county. These large-format maps are produced to support the P.L. 94-171 program and all other 1998 Dress Rehearsal data tabulations. The Voting District/State Legislative District Outline Maps show the boundaries and codes for voting districts as delineated by the states in Phase 2, Voting District Project, of the Redistricting Data Program. The features underlying the voting district boundaries are shown, as well as the names of these features. Additionally, for states that submit the information, these maps show the boundaries and codes for state legislative districts and their underlying features. These maps also show the boundaries of and names of American Indian/Alaska Native areas, counties, county subdivisions, and places. The scale of the district maps is optimized to keep the number of map sheets for each area to a minimum, but the scale and number of map sheets will vary by the area size of the county and the voting districts and state legislative districts delineated by the states. The Census 2000 Dress Rehearsal TIGER/Line Files consist of line segments representing physical features and governmental and statistical boundaries. The files contain information distributed over a series of record types for the spatial objects of a county. These TIGER/Line Files are an extract of selected geographic and cartographic information from the Census TIGER (Topological

This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the 2020 Census place boundary file in a GIS system to produce maps for 40 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

The Florida Department of Revenue’s Property Tax Oversight(PTO) program collects parcel level Geographic Information System (GIS) data files every April from all of Florida’s 67 county property appraisers’ offices. This GIS data was exported from these file submissions in August 2025. The GIS parcel polygon features have been joined with thereal property roll (Name – Address – Legal, or NAL)file. No line work was adjusted between county boundaries.The polygon data set represents the information property appraisers gathered from the legal description on deeds, lot layout of recorded plats, declaration of condominium documents, recorded and unrecorded surveys.Individual parcel data is updated continually by each county property appraiser as needed. The GIS linework and related attributions for the statewide parcel map are updated annually by the Department every August. The dataset extends countywide and is attribute by Federal Information Processing Standards (FIPS) code.DOR reference with FIPS county codes and attribution definitions - https://fgio.maps.arcgis.com/home/item.html?id=55e830fd6c8948baae1601fbfc33a3b2If you discover the inadvertent release of a confidential record exempt from disclosure pursuant to Chapter 119, Florida Statutes, public records laws, immediately notify the Department of Revenue at 850-717-6570 and your local Florida Property Appraisers’ Office. Please contact the county property appraiser with any parcel specific questions: Florida Property Appraisers’ Offices:Alachua County Property Appraiser – https://www.acpafl.org/Baker County Property Appraiser – https://www.bakerpa.com/Bay County Property Appraiser – https://baypa.net/Bradford County Property Appraiser – https://www.bradfordappraiser.com/Brevard County Property Appraiser – https://www.bcpao.us/Broward County Property Appraiser – https://bcpa.net/Calhoun County Property Appraiser – https://calhounpa.net/Charlotte County Property Appraiser – https://www.ccappraiser.com/Citrus County Property Appraiser – https://www.citruspa.org/Clay County Property Appraiser – https://ccpao.com/Collier County Property Appraiser – https://www.collierappraiser.com/Columbia County Property Appraiser – https://columbia.floridapa.com/DeSoto County Property Appraiser – https://www.desotopa.com/Dixie County Property Appraiser – https://www.qpublic.net/fl/dixie/Duval County Property Appraiser – https://www.coj.net/departments/property-appraiser.aspxEscambia County Property Appraiser – https://www.escpa.org/Flagler County Property Appraiser – https://flaglerpa.com/Franklin County Property Appraiser – https://franklincountypa.net/Gadsden County Property Appraiser – https://gadsdenpa.com/Gilchrist County Property Appraiser – https://www.qpublic.net/fl/gilchrist/Glades County Property Appraiser – https://qpublic.net/fl/glades/Gulf County Property Appraiser – https://gulfpa.com/Hamilton County Property Appraiser – https://hamiltonpa.com/Hardee County Property Appraiser – https://hardeepa.com/Hendry County Property Appraiser – https://hendryprop.com/Hernando County Property Appraiser – https://hernandocountypa-florida.us/Highlands County Property Appraiser – https://www.hcpao.org/Hillsborough County Property Appraiser – https://www.hcpafl.org/Holmes County Property Appraiser – https://www.qpublic.net/fl/holmes/Indian River County Property Appraiser – https://www.ircpa.org/Jackson County Property Appraiser – https://www.qpublic.net/fl/jackson/Jefferson County Property Appraiser – https://jeffersonpa.net/Lafayette County Property Appraiser – https://www.lafayettepa.com/Lake County Property Appraiser – https://www.lakecopropappr.com/Lee County Property Appraiser – https://www.leepa.org/Leon County Property Appraiser – https://www.leonpa.gov/Levy County Property Appraiser – https://www.qpublic.net/fl/levy/Liberty County Property Appraiser – https://libertypa.org/Madison County Property Appraiser – https://madisonpa.com/Manatee County Property Appraiser – https://www.manateepao.gov/Marion County Property Appraiser – https://www.pa.marion.fl.us/Martin County Property Appraiser – https://www.pa.martin.fl.us/Miami-Dade County Property Appraiser – https://www.miamidade.gov/pa/Monroe County Property Appraiser – https://mcpafl.org/Nassau County Property Appraiser – https://ncpafl.com/Okaloosa County Property Appraiser – https://okaloosapa.com/Okeechobee County Property Appraiser – https://www.okeechobeepa.com/Orange County Property Appraiser – https://ocpaweb.ocpafl.org/Osceola County Property Appraiser – https://www.property-appraiser.org/Palm Beach County Property Appraiser – https://www.pbcgov.org/papa/index.htmPasco County Property Appraiser – https://pascopa.com/Pinellas County Property Appraiser – https://www.pcpao.org/Polk County Property Appraiser – https://www.polkpa.org/Putnam County Property Appraiser – https://pa.putnam-fl.com/Santa Rosa County Property Appraiser – https://srcpa.gov/Sarasota County Property Appraiser – https://www.sc-pa.com/Seminole County Property Appraiser – https://www.scpafl.org/St. Johns County Property Appraiser – https://www.sjcpa.gov/St. Lucie County Property Appraiser – https://www.paslc.gov/Sumter County Property Appraiser – https://www.sumterpa.com/Suwannee County Property Appraiser – https://suwannee.floridapa.com/Taylor County Property Appraiser – https://qpublic.net/fl/taylor/Union County Property Appraiser – https://union.floridapa.com/Volusia County Property Appraiser – https://vcpa.vcgov.org/Wakulla County Property Appraiser – https://mywakullapa.com/Walton County Property Appraiser – https://waltonpa.com/Washington County Property Appraiser – https://www.qpublic.net/fl/washington/Florida Department of Revenue Property Tax Oversight https://floridarevenue.com/property/Pages/Home.aspx

This dataset contains model-based county-level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2019 or 2018 county population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 29 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, gis@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

The Counties dataset was updated on October 31, 2023 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are mostly as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529015

This map layer portrays the State boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The map layer was created by extracting county polygon features from the CENSUS 2006 TIGER/Line files produced by the U.S. Census Bureau. These files were then merged into a single file and county boundaries within States were removed. This is a revised version of the July 2012 map layer.The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints section).

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

This web map created by the Colorado Governor's Office of Information Technology GIS team, serves as a basemap specific to the state of Colorado. The basemap includes general layers such as counties, municipalities, roads, waterbodies, state parks, national forests, national wilderness areas, and trails.Layers:Layer descriptions and sources can be found below. Layers have been modified to only represent features within Colorado and are not up to date. Layers last updated February 23, 2023. Colorado State Extent: Description: “This layer provides generalized boundaries for the 50 States and the District of Columbia.” Notes: This layer was filtered to only include the State of ColoradoSource: Esri Living Atlas USA States Generalized Boundaries Feature LayerState Wildlife Areas:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state wildlife areas layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer hosted in ArcGIS Online Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerMunicipal Boundaries:Description: "Boundaries data from the State Demography Office of Colorado Municipalities provided by the Department of Local Affairs (DOLA)"Source: Colorado Information Marketplace Municipal Boundaries in ColoradoCounties:Description: “This layer presents the USA 2020 Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as County (or County Equivalent) boundaries change. The geography is sources from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes. Geography last updated May 2022.” Notes: This layer was filtered to only include counties in the State of ColoradoSource: Esri USA Census Counties Feature LayerInterstates:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: Interstates are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointU.S. Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: U.S. Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointState Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: State Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointMajor Roads:Description: Authoritative data from the Colorado Department of Transportation representing major roads Source: Colorado Department of Transportation Major Roads REST EndpointLocal Roads:Description: Authoritative data from the Colorado Department of Transportation representing local roads Source: Colorado Department of Transportation Local Roads REST EndpointRail Lines:Description: Authoritative data from the Colorado Department of Transportation representing rail lines Source: Colorado Department of Transportation Rail Lines REST EndpointCOTREX Trails:Description: “The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently their are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.These data were last updated on 2/5/2019” Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerNHD Waterbodies:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include waterbodies in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerNHD Flowlines:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include flowline features in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerState Parks:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state parks layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerDenver Parks:Description: "This dataset should be used as a reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways. The data should be used for planning and design purposes and cartographic purposes only."Source: City and County of Denver Parks REST EndpointNational Wilderness Areas:Description: “A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area.”Notes: This layer was filtered to only include National Wilderness Areas in the State of ColoradoSource: United States Department of Agriculture National Wilderness Areas REST EndpointNational Forests: Description: “A depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.”Notes: This layer was filtered to only include National Forests in the State of ColoradoSource: United States Department of Agriculture Original Proclaimed National Forests REST Endpoint

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Publication Date: May 2025.