Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the GEOID for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block GEOIDs are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Local participants delineated most block groups as part of the Census Bureau's PSAP. The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and AIANNH areas.Block groups have a valid range of zero (0) through nine (9). Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS). The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/). Block Groups (BGs) are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering. A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number. For example, blocks 3001, 3002, 3003, . . . , 3999 in census tract 1210.02 belong to BG 3 in that census tract. Most BGs were delineated by local participants in the Census Bureau’s Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where a local or tribal government declined to participate in PSAP, and a regional organization or the State Data Center was not available to participate. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within the census tract. Within the standard census geographic hierarchy, BGs never cross state, county, or census tract boundaries, but may cross the boundaries of any other geographic entity. Tribal census tracts and tribal BGs are separate and unique geographic areas defined within federally recognized American Indian reservations and can cross state and county boundaries (see “Tribal Census Tract” and “Tribal Block Group”). The tribal census tracts and tribal block groups may be completely different from the standard county-based census tracts and block groups defined for the same area. Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/BG/ on June 22, 2023

Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.

All previously released versions of this data are available at the DCP Website: BYTES of the BIG APPLE.

A. SUMMARY Census tracts boundaries in San Francisco county. Census tracts are small, relatively permanent statistical subdivisions of a county. They are uniquely numbered in each county with a numeric code. Census tracts average about 4,000 inhabitants ranging from 1,200 – 8,000. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census tract boundaries are tracked in multiple datasets. See here for 2000 and 2010 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. E. RELATED DATASET 2020 Census Tracts and Analysis Neighborhoods

A. SUMMARY This dataset maps 2020 census tracts to Analysis Neighborhoods. The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department originally created the 41 Analysis Neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. They are not codified in Planning Code nor Administrative Code. B. HOW THE DATASET IS CREATED This dataset is produced by mapping the 2020 Census tracts to Analysis neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the census tract boundaries are tracked in multiple datasets. See here for the 2010 census tracts assigned to neighborhoods D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID, which is the primary key for census tracts in the dataset E. RELATED DATASET 2020 census tract boundaries for San Francisco can be found here

Census tracts are designated as urban, rural center, or rural through SB 1000 analysis. These designations are being used for the REV 2.0 and Community Charging in Urban Areas GFOs.

A. SUMMARY Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 and 2010 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

A. SUMMARY Census Block groups are the next level above census blocks in the geographic hierarchy. Block groups are a combination of census blocks that is a subdivision of a census tract.A block group consists of all census blocks whose numbers begin with the same digit in a given census tract; for example, block group 3 includes all census blocks numbered in the 300s. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

A. SUMMARY Census blocks with Pacific Ocean and San Francisco Bay water clipped out. Census Block groups are the next level above census blocks in the geographic hierarchy. Block groups are a combination of census blocks that is a subdivision of a census tract.A block group consists of all census blocks whose numbers begin with the same digit in a given census tract; for example, block group 3 includes all census blocks numbered in the 300s. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau and clipped using the water boundaries provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco

This service provides spatial data for all U.S. decennial census tracts eligible for designation as Qualified Opportunity Zones (QOZs) for purposes of §§ 1400Z–1 and 1400Z–2 of the Internal Revenue Code (the Code). In addition to identifying Opportunity Zone census tracts, included in this dataset are the qualification for a census tract to have potentially been nominated as an Opportunity Zone such as being a Low-Income Community census tract or Contiguous census tract, whether or not a census tract was added or subtracted from the list of potential Opportunity Zones, and census tracts that had interim GEOID changes between the decennial census and Opportunity Zone nomination.Section 1400Z–1(b)(1)(A) of the Code allowed the Chief Executive Officer (CEO) of each State to nominate a limited number of population census tracts to be designated as Zones for purposes of §§ 1400Z–1 and 1400Z–2. Revenue Procedure 2018–16, 2018–9 I.R.B. 383, provided guidance to State CEOs on the eligibility criteria and procedure for making these nominations. Section 1400Z–1(b)(1)(B) of the Code provides that after the Secretary receives notice of the nominations, the Secretary may certify the nominations and designate the nominated tracts as Zones.

Section 1400Z–2 of the Code allows the temporary deferral of inclusion in gross income for certain realized gains to the extent that corresponding amounts are timely invested in a qualified opportunity fund. Investments in a qualified opportunity fund may also be eligible for additional tax benefits. Data Sources: Original list of Opportunity Zone eligible census tractsOriginal list of census tracts designated as Opportunity ZonesTo learn more about Qualified Opportunity Zones visit: https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx Data Dictionary: DD Opportunity Zone Eligible Census Tracts

Simple municipal name/GEOID lookup table. The table combines GEOID with census county names and municipal names. Stored as view in the demographics schema.

Mapping Layer Data Released: 06/15/2017, | Last Updated 04/20/2024Data Currency: This data is checked semi-annually from it's enterprise federal source fo 2010 CENSUS Data and will support mapping, analysis, data exports and the Open Geospatial Consortium (OGC) Application Programming Interface (API).Data Update Frequency: Twice, YearlyData Cycle | History (as required below)QA/QC Performed: December, 2024Next Scheduled Data QA/QC: July, 2024CDC PLACES (2010 CENSUS) FEATURE LAYERData Requester: Rhode Island Executive Office of Health and Human Service (OHHS) via Health Equity Institute (HEI).Data Requester: Rhode Island Department of Health, Maternal Child Health via Health Equity Institute (HEI).Data Request: Provide a database deliverable via download that contains both US CENSUS tracts and USPS Zip Code Tabulation Areas (ZCTA).HEALTH EQUITY INSTITUTE DATA CONNECT RI Using Modern GIS (Mapping)🡅 Click IT 🡅Facilitate transformative mapping visualizations that engage constituents and measure the impact of real-world solutions.Instructions to Join Your Data Provided Below STEP 1: Video (Pending)STEP 2: Video (Pending)STEP 3: Video (Pending)There are twenty-two U.S. CENSUS fields (download here) that you can join to your datasets. For additional insight, please contact the Center for Health Data and Analysis (CHDA) Rhode Island Department of Health (GIS) Mapping Department for assistance.Database Enhancement: This database contains two (2) additional data fields for consideration to be added to the existing 2020 State of Rhode Island Health Equity Map.Zip Code Tabulation Area (ZCTA)ZCTA/Tract Relationship (Singular ZCTAs per Tract, versus Multiple ZCTAs per Tract)Additional Information: While ZCTAs can be useful for certain qualitative purposes, such as broad or general high level analysis, they may not provide the level of granularity and accuracy required for in-depth demographic research which is required for policy mapping. ZCTAs can change frequently as the US Postal Service (USPS) adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.RIDOH GIS encourages analysts to make the appropriate choice of using census based data, with their consistent boundaries readily available for suitability for spatial analysis when conducting detailed demographic research.Here are a few reasons why you might want to consider using census based data (tracts, block groups, and blocks) instead of ZCTAs:1. Inaccurate Representations: ZCTAs are not designed for statistical analysis or demographic research. They are created by the United States Postal Service (USPS) for efficient mail delivery and can often span multiple cities, counties, or even states. As a result, ZCTAs may not accurately represent the actual geographic boundaries or demographic characteristics of a specific area.2. Lack of Granularity: ZCTAs are typically larger than census tracts, which are smaller, more homogeneous geographic units defined by the U.S. Census Bureau. Census tracts are designed to be relatively consistent in terms of population size, allowing for more detailed analysis at a local level. ZCTAs, on the other hand, can vary significantly in terms of population size, making it challenging to draw precise conclusions about specific neighborhoods or communities.3. Data Availability and Compatibility: Census tracts are used by the U.S. Census Bureau to collect and report demographic data. Consequently, a wide range of demographic information, such as population counts, age distribution, income levels, and education levels, is readily available at the census tract level. In contrast, data specifically tailored to ZCTAs may be more limited, making it difficult to obtain comprehensive and consistent data for demographic analysis.4. Changes Over Time: Census tracts are relatively stable over time, allowing for consistent longitudinal analysis. ZCTAs, however, can change frequently as the USPS adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.5. Spatial Analysis: Census tracts are designed to maintain a level of spatial proximity, adjacency, or connectedness of these data containers while providing consistency and continuity over time - making them useful for spatial analysis. Mapping. ZCTAs, on the other hand, may not exhibit the same level of spatial coherence due to their primary purpose being mail delivery efficiency rather than geographic representation.State Agencies - Contact RIDOH GIS - Learn More About Mapping Data Available at the Census Tract LevelRIDOH GIS releases this database with the caveats noted above and that the researcher can accurately align the ZCTAs with the corresponding census tracts. Careful consideration should be given to the comparability and compatibility of the data collected at different geographic levels to ensure valid and meaningful statistical conclusions. Data Dictionary: 2010 Decennial CensusOBJECT ID - the count of each census tract entity.GEOID (10) STATE,COUNTY,TRACT - Numeric US CENSUS Tract Description (2010) HEZ (10) - Health Equity Zone (2020)LOCATION (10) - Plain Language Census Tract Descriptor (2010)COUNTY (10) NAME - County Name (2010)STATE (10) NAME - State Name (2010)ZCTA (23) - Zip Code Tabulation Area - Numeric US CENSUS ZCTA Description (2023)ZCTA/TRACT CONTEXT - Number of ZCTAs (Singular/Multiple) that reside within a US CENSUS TractST (10) - Numeric US CENSUS Tract Description (2010) CO (10) - Numeric US CENSUS Tract Description (2010)ST (10) CO (10) - Numeric US CENSUS Tract Description (2010)TRACT (10) - Numeric US CENSUS Tract Description (2010)GEOID (10) - Numeric US CENSUS Tract Description (2010)TRIBAL TRACT (10) - Numeric US CENSUS Tract Description (2010)Additional Mapping DataThe user is provided authoritative Federal Information Processing Standards (FIPS) such as numeric descriptions of state, county and tract identification, in addition to shape and length measurements of each census tract for data joining purposes.STATE (10) - Federal Information Processing Standards (FIPS)COUNTY (10) - Federal Information Processing Standards (FIPS)STATE (10), COUNTY (10) - Federal Information Processing Standards (FIPS)TRACT (10) - Federal Information Processing Standards (FIPS)TRIBAL TRACT (10) - Federal Information Processing Standards (FIPS)ST ABBRV (10) - State AbbreviationShape_Length - Total length of the polygon's (census tract) perimeter, in the units used by the feature class' coordinate system.Shape_Area - Total area of the polygon's (census tract) in the units used by the feature class' coordinate system.Data Source: Series Information for 2020 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National TIGER/Line Shapefiles, Current Open Geospatial Consortium (OGC) Application Programming Interface (API) Census ZIP Code Tabulation Areas - OGC Features copy this link to embed it in OGC Compliant viewers. For more information, please visit: ZIP Code Tabulation Areas (ZCTAs)To Report Data Discrepancies Contact the Rhode Island Department of Health (RIDOH) GIS (mapping) OfficePlease Be Certain To --Provide a Brief Description of What the Discrepancy IsInclude Your, Name, Organization, Telephone NumberAttach the Complete .xlsx with the Discrepancy Highlighted

The Rooftop Energy Potential of Low Income Communities in America REPLICA data set provides estimates of residential rooftop solar technical potential at the tract-level with emphasis on estimates for Low and Moderate Income LMI populations. In addition to technical potential REPLICA is comprised of 10 additional datasets at the tract-level to provide socio-demographic and market context. The model year vintage of REPLICA is 2015. The LMI solar potential estimates are made at the tract level grouped by Area Median Income AMI income tenure and building type. These estimates are based off of LiDAR data of 128 metropolitan areas statistical modeling and ACS 2011-2015 demographic data. The remaining datasets are supplemental datasets that can be used in conjunction with the technical potential data for general LMI solar analysis planning and policy making. The core dataset is a wide-format CSV file seeds_ii_replica.csv that can be tagged to a tract geometry using the GEOID or GISJOIN fields. In addition users can download geographic shapefiles for the main or supplemental datasets. This dataset was generated as part of the larger NREL-led SEEDSII Solar Energy Evolution and Diffusion Studies project and specifically for the NREL technical report titled Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States by Sigrin and Mooney 2018. This dataset is intended to give researchers planners advocates and policy-makers access to credible data to analyze low-income solar issues and potentially perform cost-benefit analysis for program design. To explore the data in an interactive web mapping environment use the NREL SolarForAll app.

The 2020 American Community Survey Census Tract Data combines boundary information for Census Tracts with demographic data from the Census Table (B01003) sourced from the United States Census Bureau's American Community Survey (ACS). This dataset includes total population counts per tract, allowing for the analysis of population distribution and demographic characteristics within Montgomery County, Texas.Data Fields Included:STATEFP, alias State IDCOUNTYFP, alias County IDTRACTCE, alias Census Tract ID (short)GEOID, alias Full Census Tract ID (long)NAME, alias Census Tract NameNAMELSAD, alias Census Tract Name LabelPOPTOTAL, alias Estimated TotalDATASRCDT, alias Data Source DateDATASRCNM, alias Data Source NameDATASRCURL, alias Data Source WebsiteThis dataset is sourced from the United States Census Bureau.Data source: American Community Survey (ACS)

This data set contains estimates of the percentage smoke detector utilization at the Census Tract level for the United States. Development of this data set is described in NIST TN 2020 (see references below). The zip file contains the data in shapefile format. Each record is a single census tract (using the 2013 Tiger files for census tracts) with associated data. Fields contained in the data set are: geoid: Geographic ID of the census tract. Format is '14000USXXYYYZZZZZZ', where XX is the FIPS code for the state, YYY is the FIPS code for the county, and ZZZZZZ is the census tract number. This field serves as a unique ID for the dataset. state: FIPS code for the state. county: FIPS code for the county. tract: Tract number. smsa: Standard Metropolitan Statistical Area as used in the American Housing Survey. PUMA: Public Use Microdata Area ID. region: Census region. dtctrs: Estimated fraction of households in the census tract with smoke detectors installed.

The 2015 American Community Survey Census Tract Data combines boundary information for Census Tracts with demographic data from the Census Table (B01003) sourced from the United States Census Bureau's American Community Survey (ACS). This dataset includes total population counts per tract, allowing for the analysis of population distribution and demographic characteristics within Montgomery County, Texas.Data Fields Included:GEOID, alias Full Census IDNAMELSAD, alias Census Tract Name LabelPOPTOTAL, alias Estimated TotalDATASRCDT, alias Data Source DateDATASRCNM, alias Data Source NameDATASRCURL, alias Data Source WebsiteThis dataset is sourced from the United States Census Bureau.Data source: American Community Survey (ACS)

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field) Field Alias Description Source year IPD analysis year DVRPC geoid20 11-digit tract GEOID Census tract identifier ACS 5-year statefp 2-digit state GEOID FIPS Code for State ACS 5-year countyfp 3-digit county GEOID FIPS Code for County ACS 5-year tractce Tract number Tract Number ACS 5-year name Tract number Census tract identifier with decimal places ACS 5-year namelsad Tract name Census tract name with decimal places ACS 5-year d_class Disabled percentile class Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average calculated d_est Disabled count estimate Estimated count of disabled population ACS 5-year d_est_moe Disabled count margin of error Margin of error for estimated count of disabled population ACS 5-year d_pct Disabled percent estimate Estimated percentage of disabled population ACS 5-year d_pct_moe Disabled percent margin of error Margin of error for percentage of disabled population ACS 5-year d_pctile Disabled percentile Tract's regional percentile for percentage disabled calculated d_score Disabled percentile score Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4 calculated em_class Ethnic minority percentile class Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average calculated em_est Ethnic minority count estimate Estimated count of Hispanic/Latino population ACS 5-year em_est_moe Ethnic minority count margin of error Margin of error for estimated count of Hispanic/Latino population ACS 5-year em_pct Ethnic minority percent estimate Estimated percentage of Hispanic/Latino population calculated em_pct_moe Ethnic minority percent margin of error Margin of error for percentage of Hispanic/Latino population calculated em_pctile Ethnic minority percentile Tract's regional percentile for percentage Hispanic/Latino calculated em_score Ethnic minority percentile score Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4 calculated f_class Female percentile class Classification of tract's female percentage as: well below average, below average, average, above average, or well above average calculated f_est Female count estimate Estimated count of female population ACS 5-year f_est_moe Female count margin of error Margin of error for estimated count of female population ACS 5-year f_pct Female percent estimate Estimated percentage of female population ACS 5-year f_pct_moe Female percent margin of error Margin of error for percentage of female population ACS 5-year f_pctile Female percentile Tract's regional percentile for percentage female calculated f_score Female percentile score Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4 calculated fb_class Foreign-born percentile class Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average calculated fb_est Foreign-born count estimate Estimated count of foreign born population ACS 5-year fb_est_moe Foreign-born count margin of error Margin of error for estimated count of foreign born population ACS 5-year fb_pct Foreign-born percent estimate Estimated percentage of foreign born population calculated fb_pct_moe Foreign-born percent margin of error Margin of error for percentage of foreign born population calculated fb_pctile Foreign-born percentile Tract's regional percentile for percentage foreign born calculated fb_score Foreign-born percentile score Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4 calculated le_class Limited English proficiency percentile class Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average calculated le_est Limited English proficiency count estimate Estimated count of limited english proficiency population ACS 5-year le_est_moe Limited English proficiency count margin of error Margin of error for estimated count of limited english proficiency population ACS 5-year le_pct Limited English proficiency percent estimate Estimated percentage of limited english proficiency population ACS 5-year le_pct_moe Limited English proficiency percent margin of error Margin of error for percentage of limited english proficiency population ACS 5-year le_pctile Limited English proficiency percentile Tract's regional percentile for percentage limited english proficiency calculated le_score Limited English proficiency percentile score Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4 calculated li_class Low-income percentile class Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average calculated li_est Low-income count estimate Estimated count of low income (below 200% of poverty level) population ACS 5-year li_est_moe Low-income count margin of error Margin of error for estimated count of low income population ACS 5-year li_pct Low-income percent estimate Estimated percentage of low income (below 200% of poverty level) population calculated li_pct_moe Low-income percent margin of error Margin of error for percentage of low income population calculated li_pctile Low-income percentile Tract's regional percentile for percentage low income calculated li_score Low-income percentile score Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4 calculated oa_class Older adult percentile class Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average calculated oa_est Older adult count estimate Estimated count of older adult population (65 years or older) ACS 5-year oa_est_moe Older adult count margin of error Margin of error for estimated count of older adult population ACS 5-year oa_pct Older adult percent estimate Estimated percentage of older adult population (65 years or older) ACS 5-year oa_pct_moe Older adult percent margin of error Margin of error for percentage of older adult population ACS 5-year oa_pctile Older adult percentile Tract's regional percentile for percentage older adult calculated oa_score Older adult percentile score Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4 calculated rm_class Racial minority percentile class Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average calculated rm_est Racial minority count estimate Estimated count of non-white population ACS 5-year rm_est_moe Racial minority count margin of error Margin of error for estimated count of non-white population ACS 5-year rm_pct Racial minority percent estimate Estimated percentage of non-white population calculated rm_pct_moe Racial minority percent margin of error Margin of error for percentage of non-white population calculated rm_pctile Racial minority percentile Tract's regional percentile for percentage non-white calculated rm_score Racial minority percentile score Corresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4 calculated tot_pp Total population estimate Estimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born) ACS 5-year tot_pp_moe Total population margin of error Margin of error for estimated total population of tract ACS 5-year y_class Youth percentile class Classification of tract's youth percentage as: well below average, below average, average, above average, or well above average calculated y_est Youth count

This is a dataset of over 70,000 United States Census tracts enriched with over 25 demographic and environmental variables. These tracts cover the conterminous United States. The tract-level data were used to calculate and map climate resiliency indices.Data SourcesThis data product were first published in January 2022.United States (US) Census Bureau: American Community Survey (ACS) layers for all demographic and housing variables, TIGER/Line Shapefiles USA 2021 for national roads,US Centers for Disease Control and Prevention (CDC) for Daily Census Tract-Level PM2.5 Concentrations, 2016,US CDC PLACES: Local Data for Better Health for Current Asthma Prevalence,US Forest Service Wildfire Risk to Communities layers for Average Wildfire Exposure Type, Average Wildfire Risk to Homes, Average Housing Density, and Wildfire Hazard Potential.Processing NotesThe polygon features underwent several processing steps as part of the enrichment process. The tools used were dependent on the type of input data.All table joins used the attribute GEOID as a unique identifier for tracts.PM2.5 Concentrations were provided as coordinates for census tract centroids as well as census tract FIPS which was joined to polygon GEOIDs.The Zonal Statistics as Table geoprocessing tool was used on raster data types including Wildfire Exposure Type, Risk to Potential Structures, and Wildfire Hazard Potential inputs. Mean values for these inputs was calculated using the census tract as the zone and the raster as the value. Output was then joined back to the features.The Join Field geoprocessing tool was used with ACS input variables.The Egress Score was derived by intersecting TIGER/Line roads with tract boundaries. Roads were first filtered to include only Primary, Secondary, and Local roads. The number of intersections per tract was counted and normalized by the area of the tract. The inverse of this measure is called "Egress Score" and is used as a proxy for ranking tracts based on the number of routes into or out of each tract.*Note: This measure is intended for planning purposes only and should not be used for tactical decision making.Process OverviewFor every census tract, a Z-score was calculated that compares the value of each variable for the tract to the mean value for all tracts in the same county and is expressed as standard deviation from that mean. The Z-scores were than standardized into breaks ranging from 1 to 5 and averaged to create an overall wildfire resiliency index (WRI) for each tract. The WRIs and methodology were developed in collaboration with partners at the Centers for Disease Control and Prevention, UC Davis Department of Public Health, and the US Forest Service's Fire Lab.The tract Egress Score was derived by intersecting US Census Bureau TIGER/Line feature data with census tract polygon features to generate multipoint features. Because the TIGER/Line data may contain multiple coincident road segments that represent different road names, the multipoint features were dissolved using the unique GEOID and generated as point features. This result was summarized on GEOID and counted. The intersection point counts were joined back to the original tract features using GEOID. The counts were normalized by the area of the tracts and the reciprocal was calculated to get the Egress Score for the tract, higher Egress Score means fewer roads intersecting the tract and greater benefit from the intervention.Related WRI maps include “Where Will Better Air Filtration Improve Wildfire Resilience?”, “Where Will Home Hardening Improve Wildfire Resilience?”, and “Where Will Better Evacuation Routes Improve Wildfire Resilience?”.

Field Name Data Type Description

Statefp Number US Census Bureau unique identifier of the state

Countyfp Number US Census Bureau unique identifier of the county

Countynm Text County name

Tractce Number US Census Bureau unique identifier of the census tract

Geoid Number US Census Bureau unique identifier of the state + county + census tract

Aland Number US Census Bureau defined land area of the census tract

Awater Number US Census Bureau defined water area of the census tract

Asqmi Number Area calculated in square miles from the Aland

MSSAid Text ID of the Medical Service Study Area (MSSA) the census tract belongs to

MSSAnm Text Name of the Medical Service Study Area (MSSA) the census tract belongs to

Definition Text Type of MSSA, possible values are urban, rural and frontier.

TotalPovPop Number US Census Bureau total population for whom poverty status is determined of the census tract, taken from the 2020 ACS 5 YR S1701

The 2017 American Community Survey Census Tract Data combines boundary information for Census Tracts with demographic data from the Census Table (B01003) sourced from the United States Census Bureau's American Community Survey (ACS). This dataset includes total population counts per tract, allowing for the analysis of population distribution and demographic characteristics within Montgomery County, Texas.Data Fields Included:GEOID, alias Census Tract ID HD01_VD01, alias Estimated TotalHD02_VD01, alias Margin of Error TotalThis dataset is sourced from the United States Census Bureau.Data source: American Community Survey (ACS)