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ACS Educational Attainment Variables - Boundaries

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows education level for adults 25+. Counts broken down by sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released 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 by the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B15002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This 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. For more information about ACS layers, visit the FAQ. 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:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). 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 file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Society · esri_livingatlas
ACS Median Household Income Variables - Boundaries

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released 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. Median income and income source is based on income in past 12 months of survey.  This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This 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. For more information about ACS layers, visit the FAQ. 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:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). 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 file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Boundaries · esri_livingatlas
OPERA Dynamic Surface Water eXtent (DSWx) for Hurricane Helene in September 2024

Date of Images: Syn-Event: 2024-09-26 23:38:04 (UTC) or 7:38 PM EDT Pre-Event: 2024-09-14 23:37:54 (UTC) or 7:38 PM EDT Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory and California Institute of Technology derived the surface water extent maps using the OPERA Dynamic Surface Water eXtent from Sentinel-1 (DSWx-S1) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who want to have a rough first look at the water extent. The ARIA-share website has always focused on posting preliminary results as fast as possible for disaster response. OPERA DSWx-S1 The OPERA DSWx-S1 data identifies surface water and inundated vegetation. We provide the Water (WTR) and the Binary Water (BWTR) layers. Images are provided from 1) September 14, 2024 and 2) September 26, 2024. Each image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file. ARIA/OPERA water change map derived from OPERA DSWx-S1 The ARIA/OPERA water change map is derived from two OPERA DSWx-S1 Binary Water (BWTR) images taken on September 14, 2024 and September 26, 2024. The BTWR combines inundated vegetation and open water into a single water class.These maps depict areas of new water detection (or loss). The change map includes values of: (0) indicate no change between images, (1) absence of water pre-event, presence of water syn-event, and (-1) presence of water pre-event, absence of water syn-event. Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A satellite was used for both the September 14 and September 26 images. Resolution: 30 meters The DSWx-S1 products have these flags: 250 (light gray) and 251 (dark gray) represent HAND and layover/shadow masks, respectively. HAND mask (light gray, value 250) delineates regions where the terrain's elevation exceeds a specified threshold relative to the height above the nearest drainage point, indicating areas less likely to be subject to direct inundation. Layover/shadow mask (dark gray, value 251) identifies zones that are either occluded by topographic features taller than the surrounding landscape (layover) or are not illuminated by the radar signal due to obstruction by these elevated features (shadow), leading to potential data voids in SAR imagery. OPERA DSWx-S1 data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20240926-Hurricane_Helene/DSWx/. The OPERA DSWx-S1 products have been in production since September 2024, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the OPERA project and other products, visit https://www.jpl.nasa.gov/go/opera. For more information about the Dynamic Surface Water eXtent product suite, please refer to the DSWx Product page: https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/ Suggested Use DSWx-S1 The OPERA DSWx-S1 products classifies the OPERA Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) input imagery into: not water, water, and inundated vegetation with the masks such as layover/shadow mask and HAND mask. The WTR layer includes all classes. The BWTR layer merges water and inundated vegetation into a single water layer. Open water and inundated vegetation are represented in blue and green in WTR and blue in BWTR. Areas with masks are gray. The masks include the layover/shadow mask and HAND mask. Areas with no water detected are transparent. DSWx-S1 change map The ARIA/OPERA water extent change map classifies water extent into change/no change. Increased in water represented in blue, no change in water represented in transparent, decrease in water represented in red. RTC-S1 OPERA Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) image was converted to a false color image. In this color scale, vegetated areas appear green, urban areas appear white/pink, calm water appears black, and rough water appears purple or magenta.” Credits: Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2024), processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA and OPERA team. NASA JPL-Caltech ARIA/OPERA Team ================== Files: 20240914_DSWx-S1_BWTR.tif: The September 14, 2024 binary water map is derived from the WTR layer as a union of water classes (open water and inundated vegetation) into a binary map indicating areas with and without water. 20240926_DSWx-S1_BWTR.tif: The September 26, 2024 binary water map. 20240926_DSWx-S1_WTR.tif: Masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water and inundated vegetation), masks (HAND mask and layover/shadow mask), or no data classes. OPERA_DSWx-S1_BWTR_ChngMap_20240926-20240914_v2.tif: The ARIA/OPERA flood change map is derived from two OPERA DSWx-HLS images taken on September 14, 2024 and September 26, 2024. These maps depict areas of new water detection that is interpreted as flood. Track121_Florida_DSWx-S1-overview.png: An overview of the 20240926_DSWx-S1_WTR product with a satellite image background. These files have the same GeoTIFF format as the OPERA DSWx-S1 images described above and are in the UTM Zone 16N.

Water · patrick_rea
Central State Power Plant

uav imagery by Ben Scholer, acquired 2025-05-07 to 2025-05-07, 5.0 cm per pixel, published on OpenAerialMap.

Imagery and land cover · Ben Scholer
4.19 Carbon Neutrality (summary)

This page provides data for the Carbon Neutrality performance measure.   The City of Tempe is committed to protecting the environment. Carbon emissions are a significant contributor to the pollution of our atmosphere. Sources of carbon emissions include the energy used in city buildings and facilities, and in the fuel used in transit, city vehicles, and employee commuting. This performance measure puts the City on a path to being a carbon-neutral city. The municipal carbon footprint includes buildings, streetlights, water treatment, electricity, and fuel usage for fleet, transit, solid waste, and employee commute. The performance measure dashboard is available at 4.19 Carbon Neutrality     Data Dictionary   Additional Information   Source: City pisions (is this word correct?): fleet, WUD, solid waste. APS, SRP, MAG, SHROG, and Valley Metro Contact:  Grace Kelly Contact E-Mail: Grace_Kelly@tempe.gov Data Source Type:  CSV Preparation Method:  It is part of a larger data set and uses proprietary software. Publish Frequency:  Every 5 years Publish Method: Manual

Environment · TempeData
Hayne Street - 5/11/2025

uav imagery by DSHQ Drone Mapping, acquired 2025-05-11 to 2025-05-11, 5.0 cm per pixel, published on OpenAerialMap.

Imagery and land cover · DSHQ Drone Mapping
Longwood High School - 5/12/2025

uav imagery by WebODM, acquired 2025-05-12 to 2025-05-12, 5.0 cm per pixel, published on OpenAerialMap.

Imagery and land cover · WebODM
ACS 1-Year Demographic Characteristics of DC 2023

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 3, 2025. National Figures: data.census.gov. 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: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), 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 census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. 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). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Population · DCGISopendata
State Designated Tribal Statistical Areas Labels (tigerWMS Census2000)

Legal and Statistical Entities Web Map Service; January 1, 2000 vintage

Population · US Census Bureau
Brewer Hill, Star City, WV

uav imagery by Crayton Litton (Crayton Technologies), acquired 2025-05-24 to 2025-05-25, 5.0 cm per pixel, published on OpenAerialMap.

Imagery and land cover · Crayton Litton (Crayton Technologies)
Western Sandpiper Predicted Habitat - CWHR B183 [ds2135]

CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.

Biodiversity · BIOS_Admin
Secondary School Districts Labels (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Boundaries · US Census Bureau
State Designated Tribal Statistical Areas (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Boundaries · US Census Bureau
State Designated Tribal Statistical Areas Labels (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Boundaries · US Census Bureau
Census Divisions (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Population · US Census Bureau
Palisade Park Amphitheater

uav imagery by North Metro Fire Rescue District, acquired 2026-06-01 to 2026-06-01, 2.5 cm per pixel, published on OpenAerialMap.

Imagery and land cover · North Metro Fire Rescue District
Census Divisions Labels (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Population · US Census Bureau
Census Regions (tigerWMS ACS2019)

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

Population · US Census Bureau
American Indian Joint-Use Areas Labels (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
116th Congressional Districts Labels (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
2010 Census Urbanized Areas 500K (Urban)

Urbanized Areas, Urban Clusters; 2010 Census - January 1, 2010 vintage; Generalized

Population · US Census Bureau
Census Regions Labels (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Population · US Census Bureau
Combined New England City and Town Areas (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
Combined New England City and Town Areas Labels (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
New England City and Town Area Divisions (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
Metropolitan Statistical Areas Labels (tigerWMS ACS2021)

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Boundaries · US Census Bureau
2020 Census ZIP Code Tabulation Areas Labels (tigerWMS ACS2022)

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

Population · US Census Bureau
Tribal Census Tracts Labels (tigerWMS ACS2022)

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

Population · US Census Bureau
Census Designated Places Labels (tigerWMS ACS2022)

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

Population · US Census Bureau
Alaska Native Regional Corporations Labels (tigerWMS ACS2022)

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

Boundaries · US Census Bureau
Tribal Subdivisions (tigerWMS ACS2022)

ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage

Boundaries · US Census Bureau
States Labels (tigerWMS ACS2023)

ACS 2023 Legal and Statistical Entities Web Map Service; January 1, 2023 vintage

Boundaries · US Census Bureau
2020 Census ZIP Code Tabulation Areas Labels (tigerWMS ACS2024)

ACS 2024 Legal and Statistical Entities Web Map Service; January 1, 2024 vintage

Population · US Census Bureau
Tribal Census Tracts Labels (tigerWMS ACS2024)

ACS 2024 Legal and Statistical Entities Web Map Service; January 1, 2024 vintage

Population · US Census Bureau
Secondary School Districts (tigerWMS ACS2024)

ACS 2024 Legal and Statistical Entities Web Map Service; January 1, 2024 vintage

Boundaries · US Census Bureau
Secondary School Districts Labels (tigerWMS ACS2024)

ACS 2024 Legal and Statistical Entities Web Map Service; January 1, 2024 vintage

Boundaries · US Census Bureau