The SNAP participation rate shows how many households in Champaign County receive SNAP benefits, as a percentage of the total number of households in the county. The SNAP participation rate can serve as an indicator of poverty and need in the area, as income-based thresholds establish SNAP eligibility. However, not every household in poverty receives SNAP benefits, as can be determined by comparing the poverty rate between 2005 and 2023 and the percentage of households receiving SNAP benefits between 2005 and 2023.

The number of households and the percentage of households receiving SNAP benefits was higher in 2023 than in 2005, but we cannot establish a trend based on year-to-year changes, as in many years these changes are not statistically significant.

SNAP participation data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Receipt of Food Stamps/SNAP in the Past 12 Months by Presence of Children Under 18 Years for Households.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (26 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (5 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Analysis of ‘US Public Food Assistance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jpmiller/publicassistance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset focuses on public assistance programs in the United States that provide food, namely SNAP and WIC. If you are interested in a broader picture of food security across the world, please see Food Security Indicators for the World 2016-2020.

Initial coverage was for the Special Supplemental Nutrition Program for Women, Infants, and Children Program, or simply WIC. The program allocates Federal and State funds to help low-income women and children up to age five who are at nutritional risk. Funds are used to provide supplemental foods, baby formula, health care, and nutrition education.

Starting with version 5, the dataset also covers the US Supplemental Nutrition Assistance Program, more commonly known as SNAP. The program is the successor to the Food Stamps program previously in place. The program provides food assistance to low-income families in the form of a debit card. A 2016 study using POS data from SNAP-eligible vendors showed the three most purchased types of food to be meats, sweetened beverages, and vegetables.

Content

Files may include participation data and spending for state programs, and poverty data for each state. Data for WIC covers fiscal years 2013-2016, which is actually October 2012 through September 2016. Data for SNAP covers 2015 to 2020.

Motivation

My original purpose here is two-fold:

• Explore various aspects of US Public Assistance. Show trends over recent years and better understand differences across state agencies. Although the federal government sponsors the program and provides funding, program are administered at the state level and can widely vary. Indian nations (native Americans) also administer their own programs.

• Share with the Kaggle Community the joy - and pain - of working with government data. Data is often spread across numerous agency sites and comes in a variety of formats. Often the data is provided in Excel, with the files consisting of multiple tabs. Also, files are formatted as reports and contain aggregated data (sums, averages, etc.) along with base data.

As of March 2nd, I am expanding the purpose to support the M5 Forecasting Challenges here on Kaggle. Store sales are partly driven by participation in Public Assistance programs. Participants typically receive the items free of charge. The store then recovers the sale price from the state agencies administering the program.

Additional Content Ideas

The dataset can benefit greatly from additional content. Economics, additional demographics, administrative costs and more. I'd like to eventually explore the money trail from taxes and corporate subsidies, through the government agencies, and on to program participants. All community ideas are welcome!

--- Original source retains full ownership of the source dataset ---

The National School Lunch Program (NSLP) is a federally assisted meal program operating in public and nonprofit private schools and residential child care institutions. It provides nutritionally balanced, low-cost or free lunches to children each school day. Information in this dataset consists of participation and lunches served.

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

The Current Population Survey Food Security Supplement (CPS-FSS) is a nationally representative dataset created by the U.S. Census Bureau under sponsorship by the U.S. Department of Agriculture (USDA) Economic Research Service (ERS). It has been collected annually since 1995 as a supplement to the monthly CPS, which surveys approximately 50,000 households on labor force and socioeconomic topics. The dataset measures food security, including indicators like food expenditure, access to food, quality, safety, and participation in federal food assistance programs. It uses a standardized 18-item module to assess household-level food insecurity severity. Key features include state-level estimates (since 1998) and alignment with USDA’s annual reports on food security. Its primary purpose is to monitor hunger and food insecurity trends in the U.S., informing policy decisions and programs like SNAP (Supplemental Nutrition Assistance Program). Researchers and policymakers use it to analyze disparities by demographic factors (e.g., income, race, geography) and evaluate the impact of economic or public health crises (e.g., the COVID-19 pandemic). Unique aspects include its longitudinal consistency, integration with labor force data, and public accessibility via platforms like the USDA ERS and Census Bureau, enabling robust analyses of food insecurity’s social and economic determinants. (Source: USDA ERS, Census Bureau, and academic analyses cited in search results.)

Replication data for "Do Welfare Benefits Pay Electoral Dividends? Evidence from the National Food Stamp Program Rollout." Abstract: Growing evidence suggests that pocketbook considerations influence voting behavior in the U.S. and other developed countries and that incumbents can use targeted government benefits to win voter support. It remains unclear whether the general relationship between government spending and incumbent support also holds for means-tested welfare programs, however. I contribute to this empirical literature by taking advantage of the decade-long rollout of the American Food Stamp Program. The staggered timing of local program implementation allows me to credibly estimate the causal effect of this new benefit on election outcomes. Overall, I find that Democrats -- at the center of the program's enacting coalition -- gained votes when the program was implemented locally, apparently through mobilization of new supporters rather than the conversion of political opponents.

The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs.

SNAP benefits are one of the data sources used in producing SAIPE program estimates. The Supplemental Nutrition Assistance Program (SNAP) is the name for what was formerly known as the federal Food Stamp Program, as of October 1, 2008. The SNAP benefits data represent the number of participants in the Supplemental Nutrition Assistance Program for each county, state, and the District of Columbia from 1981 to the latest available year.

See more details about SAIPE Model Input Data (https://www.census.gov/data/datasets/time-series/demo/saipe/model-tables.html).

We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search data statistically outperform the competing models at all considered horizons. These results hold also with several robustness checks, considering alternative keywords, a falsification test, different out-of-samples, directional accuracy and forecasts at the state-level.

The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs.

SNAP benefits are one of the data sources used in producing SAIPE program estimates. The Supplemental Nutrition Assistance Program (SNAP) is the name for what was formerly known as the federal Food Stamp Program, as of October 1, 2008. The SNAP benefits data represent the number of participants in the Supplemental Nutrition Assistance Program for each county, state, and the District of Columbia from 1981 to the latest available year.

See more details about SAIPE Model Input Data (https://www.census.gov/data/datasets/time-series/demo/saipe/model-tables.html).

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).

{"definition": "The State has been granted a waiver to use a telephone interview in lieu of a face-to-face interview at initial certification and/or at recertification, without having to document household hardship, that is valid in at least 1 month of the calendar year.", "availableYears": "2005", "name": "SNAP face interview waiver, 2005*", "units": "Classification", "shortName": "SNAP_FACEWAIVER05", "geographicLevel": "State", "dataSources": "SNAP Policy Database, Economic Research Service (ERS), U.S. Department of Agriculture (USDA). SNAP Policy Database. (http://www.ers.usda.gov/data-products/snap-policy-database.aspx)."}

© SNAP_FACEWAIVER05

This layer is sourced from gis.ers.usda.gov.

Use of Force incidents at the Benefits Access Centers and SNAP Centers, disaggregated by: (a) The date the use of force incident occurred; (b) The job center or SNAP center where the use of force incident occurred; (c) The category of the use of force incident; (d) The number and category of injuries to a peace officer or security guard; (e) The number and category of injuries to any other individual; (f) The basis for the encounter; (g) Whether or not an arrest was made.

Individuals receiving monthly benefits through the U.S. Supplemental Nutrition Assistance Program (SNAP) often fall short of food at the end of the month and some report feelings of hunger. To investigate this situation, we used time diaries from the 2006–08 American Time Use Survey and Eating & Health Module to identify the timing of days where respondents reported no eating occurrences. Analysis includes descriptive statistics, a logit model, and a simulated benefit month. We found that SNAP participants were increasingly more likely than nonparticipants to report a day with no eating occurrences over the benefit issuance cycle. This supports the view that there is a monthly cycle in food consumption associated with the SNAP monthly benefit issuance policy.

This online supplement contains data files and computer code, enabling the public to reproduce the results of the analysis described in the report titled “Thrifty Food Plan Cost Estimates for Alaska and Hawaii” published by USDA FNS in July 2023. The report is available at: https://www.fns.usda.gov/cnpp/tfp-akhi. The online supplement contains a user guide, which describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis; a data file in CSV format, which contains the most detailed information on food price differentials between the mainland U.S. and Alaska and Hawaii derived from Circana (formerly Information Resources Inc) retail scanner data as could be released without disclosing proprietary information; SAS and R code, which use the provided data file to reproduce the results of the report; and an excel spreadsheet containing the reproduced results from the SAS or R code. For technical inquiries, contact: FNS.FoodPlans@usda.gov. Resources in this dataset:

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement User Guide File name: TFPCostEstimatesForAlaskaAndHawaii-UserGuide.pdf Resource description: The online supplement user guide describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Data File File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementDataFile.csv Resource description: The online supplement data file contains food price differentials between the mainland United States and Anchorage and Honolulu derived from Circana (formerly Information Resources Inc) retail scanner data. The data was aggregated to prevent disclosing proprietary information.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement R Code File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementRCode.R Resource description: The online supplement R code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the R programming language.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement SAS Code (zipped) File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementSASCode.zip Resource description: The online supplement SAS code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the SAS programming language. This SAS file is provided in zip format for compatibility with Ag Data Commons; users will need to unzip the file prior to its use.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Reproduced Results File name: TFPCostEstimatesforAlaskaandHawaii-ReproducedResults.xlsx Resource description: The online supplement reproduced results are output from either the online supplement R or SAS code and contain the results of the analysis described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report.

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.fed.us/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.fed.us/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

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The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

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).