The gross domestic product (GDP) of California was about 4.1 trillion U.S. dollars in 2024, meaning that it contributed the most out of any state to the country’s GDP in that year. In contrast, Vermont had the lowest GDP in the United States, with 45.71 billion U.S. dollars. What is GDP? Gross domestic product, or GDP, is the total monetary value of all goods and services produced by an economy within a certain time period. GDP is used by economists to determine the economic health of an area, as well as to determine the size of the economy. GDP can be determined for countries, states and provinces, and metropolitan areas. While GDP is a good measure of the absolute size of a country's economy and economic activity, it does account for many other factors, making it a poor indicator for measuring the cost or standard of living in a country, or for making cross-country comparisons. GDP of the United States The United States has the largest gross domestic product in the world as of 2023, with China, Japan, Germany, and India rounding out the top five. The GDP of the United States has almost quadrupled since 1990, when it was about 5.9 trillion U.S. dollars, to about 25.46 trillion U.S. dollars in 2022.

This web map shows annual Gross Domestic Product (GDP) by state and metro area in the USA for 2015. Clicking on the map reveals information about how the GDP has changed over time since 2001.The overlay of metro areas over states helps to put emphasis on where the country's GDP is coming from. The darkest green states produce the largest amount of GDP, and the largest circles show which major metropolitan areas contribute the most GDP within each state. Data is from the US Bureau of Economic Analysis and was downloaded from here. The state boundaries are generalized 2010 state boundaries from the Census Bureau's 2010 MAF/TIGER database. Note-- NAICS Industry detail is based on the 2007 North American Industry Classification System (NAICS).

This dataset identifies selected economic characteristics by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The economic characteristics include employment status, commuting to work, occupation, class of worker, income and benefits, health insurance coverage, and percentage of families and people whose income in the past 12 months is below the poverty level.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

This web map represents geographic entities, defined by the United States Office of Management and Budget for use by Federal statistical agencies, based on the concept of a core area with a large population nucleus, plus adjacent communities having a high degree of economic and social integration with that core.A Core-Based Statistical Area consists of a county containing an Incorporated Place or Census Designated Place with a population of at least 10,000 along with any adjacent counties that have at least 25 percent of employed residents of the county who work in the CBSA's core or central county. CBSAs are categorized as being either Metropolitan or Micropolitan. Each Metropolitan Statistical Area must have at least one urbanized area of 50,000 or more inhabitants. Each Micropolitan Statistical Area must have at least one urban cluster of at least 10,000 but less than 50,000 population.The largest scale the layer is suitable for display is 1:100,000.

A mesh of regular hexagons is created using a geoprocessing tool (http://www.arcgis.com/home/item.html?id=03388990d3274160afe240ac54763e57). This tool creates a mesh of hexagons overlapping a study area. The study area is the Gulf of Mexico region for GCOOS. The data is available at http://gis.gcoos.org:8080/arcgis/rest/services/Boundary/GoM_Regions/MapServer

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on September 2, 2025. Database that provides access to population, housing, economic, and geographic data from several censuses and surveys about the United States, Puerto Rico and the Island Areas. Census data may be compiled into tables, maps and downloadable files, which can be viewed or printed. A large selection of pre-made tables and maps satisfies many information requests. By law, no one is permitted to reveal information from these censuses and surveys that could identify any person, household, or business. The following data are available: * American Community Survey * ACS Content Review * American Housing Survey * Annual Economic Surveys * Annual Surveys of Governments * Census of Governments * Decennial Census * Economic Census * Equal Employment Opportunity (EEO) Tabulation * Population Estimates Program * Puerto Rico Community Survey

United States GDP: PCE: 1996p: DG: Others: Books & Maps data was reported at 36.437 USD bn in Oct 2003. This records an increase from the previous number of 36.225 USD bn for Sep 2003. United States GDP: PCE: 1996p: DG: Others: Books & Maps data is updated monthly, averaging 14.997 USD bn from Jan 1967 (Median) to Oct 2003, with 442 observations. The data reached an all-time high of 38.497 USD bn in Jan 2002 and a record low of 8.736 USD bn in Feb 1977. United States GDP: PCE: 1996p: DG: Others: Books & Maps data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.

United States GDP: PCE: DG: Others: Books & Maps data was reported at 38.362 USD bn in Oct 2003. This records an increase from the previous number of 38.219 USD bn for Sep 2003. United States GDP: PCE: DG: Others: Books & Maps data is updated monthly, averaging 7.195 USD bn from Jan 1959 (Median) to Oct 2003, with 538 observations. The data reached an all-time high of 40.328 USD bn in Jan 2002 and a record low of 1.055 USD bn in Feb 1959. United States GDP: PCE: DG: Others: Books & Maps data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Transportation Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:ARE at or above the 90th percentile for diesel particulate matter exposure OR transportation barriers OR traffic proximity and volumeAND are at or above the 65th percentile for low income"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.

This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent
This subdataset covers the Great Plains CONUS, i.e.

• KS
• ND
• NE
• OK
• SD

For the remaining CONUS, see the related identifiers.

Temporal extent
The map is representative for ca. 2018.

Data format
The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

• t at 10m x 10m
• kt at 100m x 100m
• Mt at 1km x 1km
• Gt at 10km x 10km

For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.

Material layers
Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):

A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337.

Further information
For further information, please see the publication.
A web-visualization of this dataset is available here.
Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication
D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep

Funding
This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.

Acknowledgments
We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Content

2018 unemployment rate in percent, per county in the USA.

Acknowledgements

This data was downloaded on March 23, 2019 from GeoFRED https://geofred.stlouisfed.org/map/?th=rdpu&cc=5&rc=false&im=fractile&sb&lng=-90.000&lat=40.028&zm=5&sl&sv&am=Average&at=Not%20Seasonally%20Adjusted,%20Monthly,%20Percent&sti=1224&fq=Annual&rt=county&un=lin&dt=2018-01-01

How Can I Use the Data? In https://research.stlouisfed.org/fred_terms.html states that: "As long as you don’t engage in a prohibited/restricted use and do adhere to any applicable copyright restrictions, you are free to use FRED for your own non-commercial, educational, and personal uses."

This story map illustrates the different ocean economies of Puerto Rico and the U.S. Virgin Islands. The story map highlights NOAA's Economics: National Ocean Watch (ENOW) dataset. This story map addresses the caveats and limiting factors faced when collecting economic information in the Caribbean territories. For more information, please see the ENOW website.

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.

This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent
This subdataset covers the Mid West CONUS, i.e.

• IA
• IL
• IN
• MI
• MN
• MO
• OH
• WI

For the remaining CONUS, see the related identifiers.

Temporal extent
The map is representative for ca. 2018.

Data format
The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

• t at 10m x 10m
• kt at 100m x 100m
• Mt at 1km x 1km
• Gt at 10km x 10km

For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.

Material layers
Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):

A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337.

Further information
For further information, please see the publication.
A web-visualization of this dataset is available here.
Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication
D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep

Funding
This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.

Acknowledgments
We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

United States Exports: Maps, Charts, Atlases etc n.e.s.o.i. Printed, Book Form data was reported at 0.030 USD mn in Dec 2021. This records a decrease from the previous number of 0.053 USD mn for Nov 2021. United States Exports: Maps, Charts, Atlases etc n.e.s.o.i. Printed, Book Form data is updated monthly, averaging 0.098 USD mn from Jan 2002 (Median) to Dec 2021, with 240 observations. The data reached an all-time high of 0.510 USD mn in May 2019 and a record low of 0.007 USD mn in May 2020. United States Exports: Maps, Charts, Atlases etc n.e.s.o.i. Printed, Book Form data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA024: Exports: by Commodity: 6 Digit HS Code: HS 36 to 51.

United States PCE: sa: DG: Others: Books and Maps data was reported at 47.148 USD bn in May 2009. This records a decrease from the previous number of 47.560 USD bn for Apr 2009. United States PCE: sa: DG: Others: Books and Maps data is updated monthly, averaging 9.739 USD bn from Jan 1959 (Median) to May 2009, with 605 observations. The data reached an all-time high of 48.547 USD bn in Aug 2008 and a record low of 1.055 USD bn in Feb 1959. United States PCE: sa: DG: Others: Books and Maps data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A183: NIPA 2003: Personal Consumption Expenditure.

United States PCE: 2000p: sa: DG: Other: Books and Maps data was reported at 44.779 USD bn in May 2009. This records a decrease from the previous number of 44.955 USD bn for Apr 2009. United States PCE: 2000p: sa: DG: Other: Books and Maps data is updated monthly, averaging 31.655 USD bn from Jan 1990 (Median) to May 2009, with 233 observations. The data reached an all-time high of 46.330 USD bn in Apr 2008 and a record low of 18.630 USD bn in Oct 1991. United States PCE: 2000p: sa: DG: Other: Books and Maps data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A183: NIPA 2003: Personal Consumption Expenditure.

Shapefile contains census tracts identified as disadvantaged in the Climate and Economic Justice Screening Tool (CEJST) for the five counties that are included in the Bay Area Regional Climate Action Planning Initiative Frontline Communities Map.

The original shapefile was downloaded from the The White House Council on Environmental Quality, Climate and Economic Justice Screening Tool (CEJST), Methodology & Data webpage.. The “Clip” tool in ArcMap was used to select only those features which are located within the boundaries of the five Bay Area counties included in the Frontline Communities Map. Only those census tracts where SN_C column is equal to 1 are displayed. Where, SN_C is defined as "Identified as disadvantaged" in the original codebook and 1 is equivalent to a true statement. To learn more about the methodology behind the original dataset, please visit: https://screeningtool.geoplatform.gov/en/methodology#3/33.47/-97.5

The Frontline Communities Map is meant to help identify communities that are considered frontline communities for the purpose of the USEPA’s Climate Pollution Reduction Grant (CPRG) program’s planning effort, which is a five-county climate action planning process led by the Air District. USEPA refers to these communities as low-income and disadvantaged communities (LIDACs).

As outlined in Executive Order 14008 on Tackling the Climate Crisis at Home and Abroad, the Climate and Economic Justice Screening Tool (CEJST) is a geospatial mapping tool designed to identify disadvantaged communities that are marginalized and overburdened by pollution and underinvestment, for the purposes of Justice40 Initiative.