These are the data used for the Racial and Ethnic Diversity for the Austin MSA story map. The story map was published July 2024 but displays data from 2000, 2010, and 2020. Decennial census data were used for all three years. 2000: DEC Summary File 1, P004 2010: DEC Redistricting Data (PL 94-171), P2 2020: DEC Redistricting Data (PL 94-171), P2 Geographic crosswalks were used to harmonize 2000, 2010, and 2020 geographies. Racial and Ethnic Diversity Index for the Austin MSA Storymap: https://storymaps.arcgis.com/stories/88ee265f00934af7a750b57f7faebd2c City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

The diversity index measures the likelihood of two randomly selected people belonging to different racial and ethnic groups. A score of 0 represents that everyone in that area shares the same racial and ethnic background. A score nearing 100 represents that almost everyone of the population in that area has different racial and ethnic backgrounds.1The following racial and ethnic groups were used to calculate the diversity index:2American Indian and Alaska Native (AIAN)Asian/Asian AmericanBlack/African AmericanHispanic/Latina/o/xNative Hawaiian and Other Pacific Islander (NHPI)WhiteMultiracialOf Another Race

This interactive map displays the locations of Austin Public Health (APH) facilities, the nearest CapMetro bus stops, compared with detailed demographic data from the 2020 Census, highlighting racial and ethnic distributions in the surrounding areas. Use this tool to explore how APH facilities are distributed relative to the diverse communities they serve. This allows you to visualize demographic changes and understand the accessibility of public health services across different neighborhoods, providing a clear picture of resource allocation and community coverage.

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics.

This measure answers the question of percentage of zip codes that are comprised of 70 percent of more of the composition or race of residents. This indicator calculated the mix by dividing a racial category by total population. Data collected from the U.S. Census Bureau, American Communities Survey (5yr), Race (Table B02001). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/scuh-nqaj

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics. This measure answers the question of what is the rate of change for the share of the total city population that is African-American. Calculated the difference of percentage of share over reporting period. Data collected from the U.S. Census Bureau, American Communities Survey (ACS) (1-yr), Race (table B02001), except for 2020 data, which are from the 2020 Decennial Census Count. American Communities Survey is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/6p8t-s826

White to Non-White Racial Dissimilarity (5-year estimate) Index for Austin County, TX was 18.07% in January of 2023, according to the United States Federal Reserve. Historically, White to Non-White Racial Dissimilarity (5-year estimate) Index for Austin County, TX reached a record high of 23.60 in January of 2010 and a record low of 18.07 in January of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for White to Non-White Racial Dissimilarity (5-year estimate) Index for Austin County, TX - last updated from the United States Federal Reserve on July of 2025.

This layer shows poverty in the past 12 months by race/ethnicity in Austin, Texas. This is shown by censustract and place boundaries. Tract data contains the most currently released American Community Survey (ACS) 5-year data for all tracts within Bastrop, Caldwell, Hays, Travis, and Williamson Counties in Texas. Place data contains the most recent ACS 1-year estimate for the City of Austin, Texas. Data contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. 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: 2019-2023 (Tract), 2023 (Place)ACS Table(s): B17020, B17020B, B17020C, B17020D, B17020E, B17020F, B17020G, B17020H, B17020I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 12, 2025National 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. 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 2020 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 RicoCensus 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.

This layer shows poverty in the past 12 months by race/ethnicity in Austin, Texas. This is shown by censustract and place boundaries. Tract data contains the most currently released American Community Survey (ACS) 5-year data for all tracts within Bastrop, Caldwell, Hays, Travis, and Williamson Counties in Texas. Place data contains the most recent ACS 1-year estimate for the City of Austin, Texas. Data contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. 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: 2019-2023 (Tract), 2023 (Place)ACS Table(s): B17020, B17020B, B17020C, B17020D, B17020E, B17020F, B17020G, B17020H, B17020I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 12, 2025National 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. 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 2020 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 RicoCensus 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.

This web map contains ZIP Code points, ZIP Code boundaries, and 2020 U.S. Census Tract boundaries for the United States, including all 50 states and the District of Columbia. The data is maintained by Esri Demographics (Esri DM) and sourced from ArcGIS Data and Maps, providing a comprehensive view of geographic and demographic distributions across the country. Data Layers Included:

Census Tracts (2020) – Defined polygon boundaries containing 2020 Census population data, Census codes, and Esri Updated Demographics. These tracts serve as small, stable geographic areas used for demographic analysis. ZIP Code Points – Point-based locations representing U.S. ZIP Codes, including postal names, ZIP Code types, population estimates, and area size. The points are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics. This layer is updated annually. ZIP Code Boundaries – Polygon representations of ZIP Code areas, useful for mapping service areas, jurisdictional boundaries, and demographic studies. This layer is updated annually. The boundaries are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics.

Data Source and Usage Rights: The data used in this hosted feature layer is provided via ArcGIS Data and Maps. The datasets within ArcGIS Data and Maps are subject to varying redistribution rights granted by Esri’s third-party data suppliers. Users must consult the Redistribution Rights document to determine permitted uses, applicable disclaimers, attribution requirements, and other conditions of use. This dataset may be used and redistributed with proper metadata and attribution in accordance with the Esri Master License Agreement. A downloadable layer package for 2020 Census Tracts is available here: 🔗 2020 Census Tract Layer Package Public Information Access: The Texas Public Information Act grants the public the right to access government records, except where certain exceptions apply. The public information officer may not ask why the records are requested. 🔗 Request public records online: Austin Public Information Requests This work is licensed under the Esri Master License Agreement.

The deployment of public electric vehicle charging stations (EVCS) is a critical component of transportation electrification. Recent studies have highlighted growing concerns about disparities in accessibility to public chargers between different demographic groups. This research expands ongoing equity concerns by contextualizing existing transportation equity discourse and analyzing public charger access disparities in Austin, Texas. Using threshold equity toolkits, we investigated public EVCS access disparity across different races and income groups. We conducted a generalized additive model regression to measure and visualize the effects of possible determinants on public EVCS access. The analysis results revealed that a public EVCS access disparity exists in Austin, with most chargers being installed in areas where the majority of the population is Non-Hispanic White. There was a more equal distribution of public EVCSs across income quartiles when compared with race. However, middle- and high-income groups had better access than lower-income communities in terms of distance to the nearest public EVCSs. Our regression analysis found that regional and socio-demographic factors, such as race and income, have a statistically significant impact on public charger access. The regression analysis also revealed that Austin’s current public EVCS deployment seems to favor communities above the poverty level and with higher numbers of registered electric vehicles. Local policymakers should reflect on the findings of this study to develop an equitable transportation electrification plan. Federal environmental justice plans such as the Justice40 initiative can benefit from incorporating more local contexts to better invest in disadvantaged communities.

To determine displacement risk, researchers at the University of Texas conducted a three-part analysis: the presence of vulnerable populations, residential market appreciation, and demographic change. To determine vulnerable populations, the authors used indicators to identify residents who, according to academic research, are least able to absorb housing costs, which includes: communities of color, low-income households, heads of households without a bachelor's degree or higher, families with children in poverty, and renters.

In 2019, 2020, 2021, and 2024 the City of Austin Housing and Planning staff updated the data and simplified the categories below.

Vulnerable: Vulnerable populations present, no significant demographic change, some tracts are near or contain high-value and high-appreciation areas. Active Displacement Risk: Vulnerable populations present, active demographic change, accelerating or appreciating housing market. Chronic Displacement Risk: Vulnerable populations have been displaced, demographic change has occurred and the housing market is high value and appreciated or appreciating. Historic Displacement: Tracts previously identified as at-risk to displacement in earlier Uprooted models (2016, 2019)

The deployment of public electric vehicle charging stations (EVCS) is a critical component of transportation electrification. Recent studies have highlighted growing concerns about disparities in accessibility to public chargers between different demographic groups. This research expands ongoing equity concerns by contextualizing existing transportation equity discourse and analyzing public charger access disparities in Austin, Texas. Using threshold equity toolkits, we investigated public EVCS access disparity across different races and income groups. We conducted a generalized additive model regression to measure and visualize the effects of possible determinants on public EVCS access. The analysis results revealed that a public EVCS access disparity exists in Austin, with most chargers being installed in areas where the majority of the population is Non-Hispanic White. There was a more equal distribution of public EVCSs across income quartiles when compared with race. However, middle- and high-income groups had better access than lower-income communities in terms of distance to the nearest public EVCSs. Our regression analysis found that regional and socio-demographic factors, such as race and income, have a statistically significant impact on public charger access. The regression analysis also revealed that Austin’s current public EVCS deployment seems to favor communities above the poverty level and with higher numbers of registered electric vehicles. Local policymakers should reflect on the findings of this study to develop an equitable transportation electrification plan. Federal environmental justice plans such as the Justice40 initiative can benefit from incorporating more local contexts to better invest in disadvantaged communities.