Application showing SNAP clients by Municipality in Massachusetts. Data provided by the Department of Transitional Assistance as of September 2025. Population data sourced from the 2020 US Census.

Note: The Food Environment Atlas contains ERS's most recent and reliable data on food assistance programs, including participants in the SNAP Program. The Supplemental Nutrition Assistance Program (SNAP) Data System is no longer being updated due to inconsistencies and reliability issues in the source data. The Supplemental Nutrition Assistance Program (SNAP) Data System provides time-series data on State and county-level estimates of SNAP participation and benefit levels, combined with area estimates of total population and the number of persons in poverty.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Interactive map GIS API Services Data file For complete information, please visit https://data.gov.

Feature layer generated from running the Dissolve Boundaries solution.

National Hydrography Dataset - for snapping diversions and other riverine features. SEO stream names - for referencing proper water sources. SEO Districts - Hydrographer districts for assigning a unique ID (WDOSTRID) on modeling projects. Public Land Survey System (PLSS)

The Supplemental Nutrition Assistance Program (SNAP) offers nutrition assistance to millions of eligible, low-income individuals and families and provides economic benefits to communities. SNAP is the largest program in the domestic hunger safety net.To be eligible as a SNAP retailer, store(s) must sell food for home preparation and consumption and meets additional criteria regarding the sale of staple foods. For technical assistance, contact the Florida's Roadmap to Healthy Living Administrator

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL).

The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity.

The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns.

To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna.

The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems.

The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions.

The street network GIS-maps include motorised and non-motorised networks. The motorised networks exclude all streets that are pedestrian-only and were cars are excluded. The network layers are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015). The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM. In the final line-segment maps (GIS-layers) all roads are represented with one line irrespectively of the number of lanes, except from Motorways and Highways which are represented with two lines, one for each direction, again irrespectively of the number of lanes. We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax.

All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as overpasses and underpasses, bridges, tunnels, flyovers and the like. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Last update: February 3, 2022 (minor geometry cleanup: snapping, remove topology slivers, etc.)This dataset includes the political districts used for the Utah State Legislature. Utah House Districts 2022 to 2032 will be used for election purposes beginning January 1, 2022. Elected officials began representing these districts in January 2023. These boundaries supersede the State House Districts that were used in 2012-2021. Statewide Political District Boundaries are drawn by the Utah Legislature and adopted into state law as part of the decennial redistricting process that began in 2021. These districts represent the Utah House Districts, as per the Census Block Assignment file, enrolled with HB2005.For information and downloads on all political districts check UGRC data page https://gis.utah.gov/data/political/2022-2032-house-senate-congressional-districts/

The Arizona WIC program provides nutrition education, breastfeeding support, and nutritious foods. WIC serves pregnant, breastfeeding, and postpartum women, infants, and children under the age of five.

The Supplemental Nutrition Assistance Program (SNAP), formerly known as Food Stamps, helps low-income households buy the food they need for good health. Source: Maryland Department of Human Resources Years Available: 2019

Food stores that are certified to accept Supplemental Nutrition Assistance Program (SNAP – previously known as food stamps) Electronic Benefit Transfer (EBT) payments in Maryland.

Data source: United States Department of Agriculture, Food and Nutrition Service

Date: 2018

Explore where Supplemental Nutrition Assistance Program (SNAP) Retailers are located in the State of Michigan. Feature service/data (also found in the SNAP Retailer Locator application) is provided by the United States Department of Agriculture (USDA) and is updated every 2 weeks.2025 Food Environment Index — scored from 0 (least favorable) to 10 (most favorable) — is included in this dashboard. The data is sourced from County Health Rankings & Roadmaps, a program of the University of Wisconsin Population Health Institute. To explore further, visit www.countyhealthrankings.org.MDARD makes every attempt to ensure data accuracy but cannot guarantee the completeness or accuracy of the information contained within this dataset. For content related questions or to submit feedback, reach out to MDARD-GIS@Michigan.gov.

The average monthly dollar amount of SNAP (Supplemental Nutrition Assistance Program, previously called Food Stamp Program) benefits in a county divided by the county population.", "availableYears": "2015", "name": "SNAP benefits per capita, 2015", "units": "Dollars/capita", "shortName": "PC_SNAPBEN15", "geographicLevel": "County", "dataSources": "SNAP benefits are from the U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic Accounts Directorate. Population data are from the U.S. Census Bureau, Population Estimates.

This dataset is a combination of 2 data projects: 1- Data were updated within NYC watershed portions using 1m resolution LiDAR and 1ft orthoimagery collected in 2009 as part of the NYS Digital Ortho Program under contract with NYCDEP under CAT-371.For NYC reservoir areas only: NYCDEP BWS GIS Staff (T. Spies) edited all artificial path and stream transitions to snap exactly to polygon edges representing NYC reservoirs, where those areas were updated by NYCDEP for correct inundation area based on spillway elevation.QA edits to NHD hydrography, including this feature class, were also made where needed based on field verification and correction of the NYCbasin1m boundary.As an additional departure from standard NHD to meet DEP’s needs, DEP GIS staff attributed all flowlines by their respective NYC reservoir basin and NYC water supply “region” as defined in the feature class “NYCbasin1m”. This was done using the “select by location” tool rather than “identity” tool, so as not to split any flowlines across boundaries. Any flowlines crossing basin boundaries in error were corrected by splitting the lines and snapping their endpoints to the appropriate spillway or basin edge instead. After these edits were made, a new geometric network was built to test and ensure all flowlines in the entire dataset were correctly connected so that they can be used for routing.2- Data was updated within portions of Ulster County outside the NYC watershed using NYS 1ft orthoimagery collected in 2013 and multiple Elevation datasets (2013 NYS DEC 1m Lidar Hudson River, 2005 NYS DEC 3m Lidar Ulster Stream Corridors, 1992 USGS 10m Digital Elevation Model (DEM)).Primary quality control was performed visually using enhanced symbology and supporting reference data. A detailed QC checklist is provided in the QC report. Specific emphasis was placed on the areas bordering the NYC Watershed and the areas encompassed by the Town of Woodstock’s local hydrography data. To the extent connections occurred, the data captured on this project was “snapped” to the corresponding locations in the NYC Watershed so that the data could be seamlessly integrated. The hydrography data from the Town of Woodstock, however, was inconsistent when applied to the data capture protocol. Many locally derived features did not appear to be supported by the source data (i.e., they did not exist) and were not included. All visual inspections were made at 1:1000 scale or better. During data capture, the Data Capture Analyst used a separate point feature class named “Flags” to identify locations where there may have been some interpretation or confusion. Later, the QC Analyst also used additional bookmarks in ArcGIS to track locations where additional investigation or interpretation was required. Finally, after an initial pass through the data, the QC Analyst evaluated and resolved all such flags and bookmarks, collaborating with the Data Capture Analyst as necessary to discuss findings and resolve questions.As data was completed, naming convention and separate storage locations were used for data management to ensure that source and modified datasets were clearly separated. In addition, a detailed QC tracking spreadsheet was used to track and manage effort on completing QC and resolving any issues.Finally, after the initial data delivery, several rounds of QC review were performed by Ulster County to include: additional visual inspection of flow line connectivity, geometric network tracking, and utility network analysisMost of the issues that were not readily apparent in the manual QC process were attributed to minor errors in data capture and discovered here. Examples include digitizing lines in the wrong direction (not downstream), existence of multi-part features, and topology errors. In all cases, issues were evaluated and resolved

The percentage of each county population in Maryland that participate in the Supplemental Nutrition Assistance Program (SNAP) (previously known as food stamps), specifically among those that are eligible, i.e. they have an income low enough to qualify for benefits.

The number of eligible people not participating in SNAP in each county in state fiscal year 2017 was generated by comparing the number of people living below 185 percent of the federal poverty level - a proxy for those who are likely eligible for SNAP — to those who are currently participating in the program. Nationally, the eligibility cut-off for SNAP is 130 percent of the federal poverty level, but this dataset uses 185 percent of the federal poverty level due to the high cost of living in Maryland.

Data source: United States Census Bureau, Maryland Department of Human Resources

Date: 2017

The Grocery Stores web map represents grocery stores within the city of Dallas as of 2024. The data is sourced from the USDA SNAP database, Google Maps, ReferenceSolutions, and AtoZDatabase.The dataset categorizes stores accepting SNAP into four types:Grocery Stores: Retailers offering a variety of fresh food products. While they may sell non-food items, their primary focus is on food.Wholesale Clubs: Large warehouse-style stores that sell a wide range of merchandise, often in bulk quantities.General Merchandise Stores with Grocery: Retail outlets that sell a variety of everyday items, including groceries.Convenience Stores (SNAP-eligible): Smaller retail locations offering a limited selection of basic packaged foods and other essentials, typically open for extended hours.Each entry in the dataset includes the store's name, street address, city, state, or ZIP code.This Grocery Stores web map was created on September 24, 2024, by Ridvan Kirimli. For any inquiries regarding the grocery store layer or web map, please contact Heather Lepeska or Ridvan Kirimli.

This dataset is a combination of 2 data projects: 1- Data were updated within NYC watershed portions using 1m resolution LiDAR and 1ft orthoimagery collected in 2009 as part of the NYS Digital Ortho Program under contract with NYCDEP under CAT-371.For NYC reservoir areas only: NYCDEP BWS GIS Staff (T. Spies) edited all artificial path and stream transitions to snap exactly to polygon edges representing NYC reservoirs, where those areas were updated by NYCDEP for correct inundation area based on spillway elevation.QA edits to NHD hydrography, including this feature class, were also made where needed based on field verification and correction of the NYCbasin1m boundary.As an additional departure from standard NHD to meet DEP’s needs, DEP GIS staff attributed all flowlines by their respective NYC reservoir basin and NYC water supply “region” as defined in the feature class “NYCbasin1m”. This was done using the “select by location” tool rather than “identity” tool, so as not to split any flowlines across boundaries. Any flowlines crossing basin boundaries in error were corrected by splitting the lines and snapping their endpoints to the appropriate spillway or basin edge instead. After these edits were made, a new geometric network was built to test and ensure all flowlines in the entire dataset were correctly connected so that they can be used for routing.2- Data was updated within portions of Ulster County outside the NYC watershed using NYS 1ft orthoimagery collected in 2013 and multiple Elevation datasets (2013 NYS DEC 1m Lidar Hudson River, 2005 NYS DEC 3m Lidar Ulster Stream Corridors, 1992 USGS 10m Digital Elevation Model (DEM)).Primary quality control was performed visually using enhanced symbology and supporting reference data. A detailed QC checklist is provided in the QC report. Specific emphasis was placed on the areas bordering the NYC Watershed and the areas encompassed by the Town of Woodstock’s local hydrography data. To the extent connections occurred, the data captured on this project was “snapped” to the corresponding locations in the NYC Watershed so that the data could be seamlessly integrated. The hydrography data from the Town of Woodstock, however, was inconsistent when applied to the data capture protocol. Many locally derived features did not appear to be supported by the source data (i.e., they did not exist) and were not included. All visual inspections were made at 1:1000 scale or better. During data capture, the Data Capture Analyst used a separate point feature class named “Flags” to identify locations where there may have been some interpretation or confusion. Later, the QC Analyst also used additional bookmarks in ArcGIS to track locations where additional investigation or interpretation was required. Finally, after an initial pass through the data, the QC Analyst evaluated and resolved all such flags and bookmarks, collaborating with the Data Capture Analyst as necessary to discuss findings and resolve questions.As data was completed, naming convention and separate storage locations were used for data management to ensure that source and modified datasets were clearly separated. In addition, a detailed QC tracking spreadsheet was used to track and manage effort on completing QC and resolving any issues.Finally, after the initial data delivery, several rounds of QC review were performed by Ulster County to include: additional visual inspection of flow line connectivity, geometric network tracking, and utility network analysisMost of the issues that were not readily apparent in the manual QC process were attributed to minor errors in data capture and discovered here. Examples include digitizing lines in the wrong direction (not downstream), existence of multi-part features, and topology errors. In all cases, issues were evaluated and resolved

U.S. Department of Agriculture, Food and Nutrition Service SNAP LocationsSource: PolicyMap, USDA, Food and Nutrition Service, 2021.

TRCA owned land. Geography based on Teranet parcel data.