SNAP Participation Data FY1989-2020

  SNAP participation data - FY 1989 through January 2020 by County About USDA Food Security and Allocations Data: Links to several different USDA food security and allocations datasets, including a Census-level Food Access Research Atlas, county-level SNAP participation data through FY2020, and state-level total participant counts from FY2015 through FY2019 for the Commodity Supplemental Food Program, Emergency Food Assistance Program, and Food Distribution Program.
  Geography Level: State, CountyItem Vintage: 1989-2020
  Update Frequency: N/AAgency: USDAAvailable File Type: Excel 

  Return to Other Federal Agency Datasets Page

SNAP Retail Locations https://services1.arcgis.com/RLQu0rK7h4kbsBq5/arcgis/rest/services/Store_Locations/FeatureServer/0

Salem SNAP Office - Massachusetts Office of Transitional Assitance

SNAP is the largest nutrition assistance program in the US. Understanding where SNAP dollars can be redeemed is thus a critical part of understanding food access environments. Data compiled from the USDA Food and Nutrition Service SNAP Retail locator downloaded from https://www.fns.usda.gov/snap/retailer-locatorXY coordinates for each retailer were re-verified using google maps due to geocoding inaccuracies in the USDA database.

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 The Supplemental Nutrition Assistance Program (SNAP) Data System is no longer being updated due to inconsistencies and reliability issues in the source data.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.

The Supplemental Nutrition Assistance Program (SNAP) is a United States Department of Agriculture (USDA) Food and Nutrition Service (FNS) program that helps low-income families and individuals buy healthy food. In Michigan, SNAP benefits are available through the Food Assistance Program (FAP), which is administered by the Michigan Department of Health and Human Services (MDHHS) and through MI Bridges. Participants in the program receive SNAP funds on an Electronic Benefits Transfer (EBT) card known as the "Michigan Bridge card", which works like a debit card. SNAP funds can be used to purchase nutritious fruit, vegetables, meat, dairy, bread and other products from participating retailers. Many different types of retailers accept the MI Bridges card, including grocery stores, convenience stores, farmers' markets, and more. Please see the MDHHS Food Assistance webpage or log into MI Bridges to learn about eligibility and to apply for the program.

This SNAP Retailer dataset includes records from the USDA SNAP Retailer Location dataset that have geographical latitude and longitude coordinates located within the City of Detroit Boundary. A few retailers located outside of Detroit may be included in this dataset if the latitude and longitudinal coordinates provided in the USDA dataset fall within the City of Detroit Boundary. The data is updated every 2 weeks.

Each record in the dataset contains data about a retail location, including the retailer's name, address, and whether they participate in the SNAP Healthy Incentive program. Retailer Type definitions are available from the USDA SNAP Store Type Definitions webpage and include convenience stores, farmers and markets, grocery stores, specialty stores, super stores, supermarkets, and restaurant meals programs.

Information about the federal program and data is available from the USDA/FNS at the Supplemental Nutrition Assistance Program (SNAP) website and the SNAP Retailer Data webpage.

{"definition": "2010 % of population participating in SNAP", "availableYears": "2010", "name": "2010 % of population participating in SNAP", "units": "Percent", "shortName": "PRG_POP10", "geographicLevel": "County", "dataSources": "SNAP participation and population estimates at the county level are provided the U.S. Census Bureau."}

© Percent This layer is a component of ERS SNAP Data System.

This map service contains maps and data relevant to SNAP (Supplemental Nutrition Assistance Program) participation and benefits

© Detailed documentation on data sources used in the ERS SNAP Data System map services is available here: http://www.ers.usda.gov/data-products/supplemental-nutrition-assistance-program-(snap)-data-system/documentation.aspx

In January 2002, Mayor Anthony A. Williams introduced his Home Again Initiative to transform vacant and abandoned residential properties into single-family homeownership opportunities for residents. By increasing homeownership and eliminating blight, Home Again helps to stabilize neighborhoods and contribute to local economic sustainability. Currently, Home Again focuses on neighborhoods with high concentrations of vacant and abandoned residential property: Columbia Heights, Ivy City/Trinidad, Near Northeast, Shaw/Ledroit Park, and Rosedale. The residents in these communities also identified rehabilitating vacant and abandoned properties as a high priority in their Strategic Neighborhood Action Plans (SNAPs).

The Sustainable Community Program focuses on creating healthy, livable, connected, and resilient neighbourhoods - both new and existing. Sustainable Neighbourhood Action Plans (SNAPs) are partnership initiatives between the City of Brampton, TRCA, CVC, and Region of Peel. SNAPs focus on environmental improvements and urban renewal of existing neighbourhoods, and promote widespread adoption of sustainable technologies, practices and lifestyle in the community. Brampton currently has two SNAPs, the County Court SNAP and the Fletchers Creek SNAP.Sustainable neighbourhood action plan areas delineate locations where special programs are offered to residents to upgrade their homes to make them energy/water efficient; special attention is made to improving the function of infrastructure and to promote sustainable habits.For more information, navigate to the City of Brampton website for this program.

Feature layer generated from running the Buffer Features solution. Input from SNAP Retailers were buffered by [1] Miles

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). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged 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).

{"definition": "% change in participation between 2000 and 2005", "availableYears": "2000-2005", "name": "% change in participation between 2000 and 2005", "units": "% change", "shortName": "PRG00_05", "geographicLevel": "County", "dataSources": "SNAP participation and population estimates at the county level are provided the U.S. Census Bureau."}

© % change This layer is a component of ERS SNAP Data System.

This map service contains maps and data relevant to SNAP (Supplemental Nutrition Assistance Program) participation and benefits

© Detailed documentation on data sources used in the ERS SNAP Data System map services is available here: http://www.ers.usda.gov/data-products/supplemental-nutrition-assistance-program-(snap)-data-system/documentation.aspx

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 contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.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). 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).

This dataset shows selected streamflow gaging stations of the United States, Puerto Rico, and the U.S. Virgin Islands, in 2014. Gaging stations, or gages, measure the height (stage) and volume of flow at a point location on a water feature. Gages in this map layer were drawn from the National Hydrography Dataset Plus (NHDPlus) Version 1 Stream Gage Events, NHDPlus Version 2 National Data Gage Locations, and from the National Water Information System (NWIS) Web Interface. The gage positions were adjusted (snapped) using ArcGIS so that the gages were located directly on streams in the National Atlas map layer, 1:1,000,000-Scale Streams of the United States. The gages and streams also share attributes that link them together.

SNAP/EBT & Da Bux data from Mālama Kauaʻi (https://malamakauai.org/).

Data related to the PGH SNAP project. Please note that this data is not up to date. It was created with 2010 census data and early 2010's data for non-census related data. Historic data is considered to be accurate at the time it was captured but is not intended to reflect current conditions.

This data set contains all Virginia Railway Express (VRE) stations in Virginia. The data set was originally obtained from the Metro Washington Council of Governments (MWCOG) GIS data clearinghouse and snapped to the Fairfax County railroad data set using 2002 aerial photography as a background. This data set was created to display all VRE stations in Virginia only and can be used in conjunction with the railroad data set.

For purposes of this Decree only, the parties reserving the issue of the eastern boundary of the 1836 Treaty waters in Lake Huron, Grids 507 through 512, 606 through 611, 709, and those portions of grids 612, 613, 710, 711, and 810 which are north of a line from the mouth of the Thunder Bay River in a straight line northeast through the northeast corner of grid 711 to the international border. Regulations: Tribal commercial fishing by any method other than trap nets shall be prohibited except fishing for chubs with small mesh gill nets or small mesh trap nets. Trap net fishing in this zone shall be open only to Bay Mills and Sault Tribe trap net fishers. A total of four (4) trap net operations shall initially be authorized for this zone. Each trap net operation will be limited to twelve (12) trap nets within the zone. Additional operations may be authorized in the future by CORA based on data collected in previous years. Maps for general reference only: refer to text of Consent Decree 2000 for exact locations and provisions.Created a new point shapefile in ArcGIS 8.1. A point was located on the USGS Alpena county 1:24,000 DRG as outlined in the Consent Decree 2000 documentation. The point was generated at the mouth of the Thunder Bay River. We then created a new polygon shapefile in ArcGIS 8.1. The new pollygon layer was created using the snapping tool in ArcMap. Starting form the above point location and heading in a clockwise direction snapping to the vertices (as outlined in the Consent Decree 2000 documentation) of the MDNR (University of Michigan) Statistical Grid layer, extending the polygon boundaries beyond the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer, and finishing the sketch at the starting point. The new polygon feature was then commbined with the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected, exported as a new shapefile, and reprojected from Michigan georef to Decimal Degrees to create the final Southern Lake Huron Trap Net Zone layer.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

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

Stamp Out COVID-19An apple a day keeps the doctor away.Linda Angulo LopezDecember 3, 2020https://theconversation.com/coronavirus-where-do-new-viruses-come-from-136105SNAP Participation Rates, was explored and analysed on ArcGIS Pro, the results of which can help decision makers set up further SNAP-D initiatives.In the USA foods are stored in every State and U.S. territory and may be used by state agencies or local disaster relief organizations to provide food to shelters or people who are in need.US Food Stamp Program has been ExtendedThe Supplemental Nutrition Assistance Program, SNAP, is a State Organized Food Stamp Program in the USA and was put in place to help individuals and families during this exceptional time. State agencies may request to operate a Disaster Supplemental Nutrition Assistance Program (D-SNAP) .D-SNAP Interactive DashboardAlmost all States have set up Food Relief Programs, in response to COVID-19.Scroll Down to Learn more about the SNAP Participation Analysis & ResultsSNAP Participation AnalysisInitial results of yearly participation rates to geography show statistically significant trends, to get acquainted with the results, explore the following 3D Time Cube Map:Visualize A Space Time Cube in 3Dhttps://arcg.is/1q8LLPnetCDF ResultsWORKFLOW: a space-time cube was generated as a netCDF structure with the ArcGIS Pro Space-Time Mining Tool : Create a Space Time Cube from Defined Locations, other tools were then used to incorporate the spatial and temporal aspects of the SNAP County Participation Rate Feature to reveal and render statistically significant trends about Nutrition Assistance in the USA.Hot Spot Analysis Explore the results in 2D or 3D.2D Hot Spotshttps://arcg.is/1Pu5WH02D Hot Spot ResultsWORKFLOW: Hot Spot Analysis, with the Hot Spot Analysis Tool shows that there are various trends across the USA for instance the Southeastern States have a mixture of consecutive, intensifying, and oscillating hot spots.3D Hot Spotshttps://arcg.is/1b41T43D Hot Spot ResultsThese trends over time are expanded in the above 3D Map, by inspecting the stacked columns you can see the trends over time which give result to the overall Hot Spot Results.Not all counties have significant trends, symbolized as Never Significant in the Space Time Cubes.Space-Time Pattern Mining AnalysisThe North-central areas of the USA, have mostly diminishing cold spots.2D Space-Time Mininghttps://arcg.is/1PKPj02D Space Time Mining ResultsWORKFLOW: Analysis, with the Emerging Hot Spot Analysis Tool shows that there are various trends across the USA for instance the South-Eastern States have a mixture of consecutive, intensifying, and oscillating hot spots.Results ShowThe USA has counties with persistent malnourished populations, they depend on Food Aide.3D Space-Time Mininghttps://arcg.is/01fTWf3D Space Time Mining ResultsIn addition to obvious planning for consistent Hot-Hot Spot Areas, areas oscillating Hot-Cold and/or Cold-Hot Spots can be identified for further analysis to mitigate the upward trend in food insecurity in the USA, since 2009 which has become even worse since the outbreak of the COVID-19 pandemic.After Notes:(i) The Johns Hopkins University has an Interactive Dashboard of the Evolution of the COVID-19 Pandemic.Coronavirus COVID-19 (2019-nCoV)(ii) Since March 2020 in a Response to COVID-19, SNAP has had to extend its benefits to help people in need. The Food Relief is coordinated within States and by local and voluntary organizations to provide nutrition assistance to those most affected by a disaster or emergency.Visit SNAPs Interactive DashboardFood Relief has been extended, reach out to your state SNAP office, if you are in need.(iii) Follow these Steps to build an ArcGIS Pro StoryMap:Step 1: [Get Data][Open An ArcGIS Pro Project][Run a Hot Spot Analysis][Review analysis parameters][Interpret the results][Run an Outlier Analysis][Interpret the results]Step 2: [Open the Space-Time Pattern Mining 2 Map][Create a space-time cube][Visualize a space-time cube in 2D][Visualize a space-time cube in 3D][Run a Local Outlier Analysis][Visualize a Local Outlier Analysis in 3DStep 3: [Communicate Analysis][Identify your Audience & Takeaways][Create an Outline][Find Images][Prepare Maps & Scenes][Create a New Story][Add Story Elements][Add Maps & Scenes] [Review the Story][Publish & Share]A submission for the Esri MOOCSpatial Data Science: The New Frontier in AnalyticsLinda Angulo LopezLauren Bennett . Shannon Kalisky . Flora Vale . Alberto Nieto . Atma Mani . Kevin Johnston . Orhun Aydin . Ankita Bakshi . Vinay Viswambharan . Jennifer Bell & Nick Giner