Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Jaffe, Melissa R.
Originator: Scott, Joe H.
Originator: Callahan, Michael N.
Originator: Dillon, Gregory K.
Originator: Karau, Eva C.
Originator: Lazarz, Mitchell T.
Publication_Date: 2024
Title:
Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States
Edition: 2nd
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Other_Citation_Details:
Updated 10 September 2024
Online_Linkage: https://doi.org/10.2737/RDS-2020-0060-2
Description:
Abstract:
The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.

National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2020 estimates of housing units and 2021 estimates of population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.

The specific raster datasets included in this publication include:

Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.

Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).

Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.

Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.

Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).

Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.

Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).

Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.

Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.

Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.
Purpose:
The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products.
Supplemental_Information:
See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S.

This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. This second edition was originally published on 06/03/2024. On 09/10/2024, a minor correction was made to the abstract in this overall metadata document as well as the individual metadata documents associated with each raster dataset. The supplemental file containing data product descriptions was also updated. In addition, we separated the large CONUS download into a series of smaller zip files (one for each layer).

There are two companion data publications that are part of the WRC 2.0 data update: one that characterizes landscape-wide wildfire hazard and risk for the nation (Scott et al. 2024, https://doi.org/10.2737/RDS-2020-0016-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030).
Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 2021
Single_Date/Time:
Calendar_Date: 2022
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Description_of_Geographic_Extent:
Conterminous United States, Hawaii, and Alaska
Bounding_Coordinates:
West_Bounding_Coordinate: -180.00000
East_Bounding_Coordinate: -67.93318
North_Bounding_Coordinate: 63.90442
South_Bounding_Coordinate: 18.85415
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: environment
Theme_Keyword: geoscientificInformation
Theme_Keyword: society
Theme_Keyword: structure
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Ecology, Ecosystems, & Environment
Theme_Keyword: Fire
Theme_Keyword: Fire detection
Theme_Keyword: Fire ecology
Theme_Keyword: Fire effects on environment
Theme_Keyword: Fire suppression, pre-suppression
Theme_Keyword: Prescribed fire
Theme_Keyword: Environment and People
Theme_Keyword: Forest management
Theme_Keyword: Landscape management
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: burn probability
Theme_Keyword: hazard
Theme_Keyword: fuels management
Theme_Keyword: fire likelihood
Theme_Keyword: fire planning
Theme_Keyword: risk assessment
Theme_Keyword: wildfire hazard potential
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: conterminous United States
Place_Keyword: CONUS
Place_Keyword: Alaska
Place_Keyword: Hawaii
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Jaffe, Melissa R.; Scott, Joe H.; Callahan, Michael N.; Dillon, Gregory K.; Karau, Eva C.; Lazarz, Mitchell T. 2024. Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States. 2nd Edition. Updated 10 September 2024. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0060-2

The datasets presented here are the product of modeling, and as such carry an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. No warranty is made by the Originator as to the accuracy, reliability, or completeness of these data for individual use or aggregate use with other data, or for purposes not intended by the Originator. These datasets are intended to provide nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. Data included here are not intended to replace locally-calibrated state, regional, or local risk assessments where they exist. It is the responsibility of the user to be familiar with the value, assumptions, and limitations of these national data publications. Managers and planners must evaluate these data according to the scale and requirements specific to their needs. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Fire Modeling Institute (FMI)
Contact_Address:
Address_Type: mailing and physical
Address: Missoula Fire Sciences Laboratory
Address: 5775 US Hwy 10 W
City: Missoula
State_or_Province: MT
Postal_Code: 59808
Country: USA
Contact_Voice_Telephone: 406-329-4836
Contact_Electronic_Mail_Address: eva.c.karau@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
Funding for this project provided by USDA Forest Service, Fire and Aviation Management. Funding also provided by USDA Forest Service, Fire Modeling Institute, which is part of the Rocky Mountain Research Station, Fire, Fuel and Smoke Science Program. Work on dataset development was primarily completed by the USDA Forest Service, Fire Modeling Institute. Some salary was provided by FMI through an ORISE agreement under the U.S. Department of Energy (DE-SC0014664).


Author information:

Melissa R. Jaffe
Pyrologix, LLC
https://orcid.org/0009-0002-8623-407X

Joe H. Scott
Pyrologix, LLC
https://orcid.org/0009-0008-3246-1190

Michael N. Callahan
Pyrologix, LLC
https://orcid.org/0009-0009-4937-5405

Gregory K. Dillon
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0009-0006-6304-650X

Eva C. Karau
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0009-0009-6776-9387

Mitchell T. Lazarz
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0002-4558-4949
Cross_Reference:
Citation_Information:
Originator: Scott, Joe H.
Originator: Brough, April M.
Originator: Gilbertson-Day, Julie W.
Originator: Dillon, Gregory K.
Originator: Moran, Christopher
Publication_Date: 2020
Title:
Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States
Edition: 1st
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0060
Cross_Reference:
Citation_Information:
Originator: Scott, Joe H.
Originator: Dillon, Gregory K.
Originator: Jaffe, Melissa R.
Originator: Vogler, Kevin C.
Originator: Olszewski, Julia H.
Originator: Callahan, Michael N.
Originator: Karau, Eva C.
Originator: Lazarz, Mitchell T.
Originator: Short, Karen C.
Originator: Riley, Karin L.
Originator: Finney, Mark A.
Originator: Grenfell, Isaac C.
Publication_Date: 2024
Title:
Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States
Edition: 2nd
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0016-2
Cross_Reference:
Citation_Information:
Originator: Dillon, Gregory K.
Originator: Lazarz, Mitchell T.
Originator: Karau, Eva C.
Originator: Story, Scott J.
Originator: Pohl, Kelly A.
Publication_Date: 2024
Title:
Wildfire Risk to Communities: Community Wildfire Risk Reduction Zones for the United States
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2024-0030
Cross_Reference:
Citation_Information:
Originator: Dillon, Gregory K.
Originator: Scott, Joe H.
Originator: Jaffe, Melissa R.
Originator: Olszewski, Julia H.
Originator: Vogler, Kevin C.
Originator: Finney, Mark A.
Originator: Short, Karen C.
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Jolly, W. Matthew
Originator: Brittain, Stuart
Publication_Date: 2023
Title:
Spatial datasets of probabilistic wildfire risk components for the United States (270m)
Edition: 3rd
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2016-0034-3
Cross_Reference:
Citation_Information:
Originator: Scott, Joe H.
Publication_Date: 2020
Title:
A deterministic method for generating flame-length probabilities
Geospatial_Data_Presentation_Form: conference proceedings
Other_Citation_Details:
p. 195-205
Online_Linkage: https://research.fs.usda.gov/treesearch/62336
Larger_Work_Citation:
Citation_Information:
Originator: Hood, Sharon M. (ed.)
Originator: Drury, Stacy (ed.)
Originator: Steelman, Toddi (ed.)
Originator: Steffens, Ron (ed.)
Publication_Date: 2020
Title:
Proceedings of the Fire Continuum-Preparing for the future of wildland fire
Geospatial_Data_Presentation_Form: conference proceedings
Series_Information:
Series_Name: Proceedings
Issue_Identification: RMRS-P-78
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
21-24 May 2018 in Missoula, MT
Online_Linkage: https://research.fs.usda.gov/treesearch/60581
Analytical_Tool:
Analytical_Tool_Description:
FSim is often referred to as a "large fire simulator" because it attempts to model the ignition and growth of only those wildfires with a propensity to spread. Relatively large and generally fast-moving fires are the focus of this system designed to estimate Burn Probability (BP) and Fire Intensity Level (FIL) because they account for the majority (approximately 80-97%) of total area burned per simulation unit, etc., and thus contribute the greatest to the probability of a wildland fire burning a given parcel of land therein (i.e., wildfire hazard). Fire occurrence in FSim is stochastically modeled based on historical relationships between large fires (largest approximately 3-5% for each simulation unit) and Energy Release Component (ERC). Because its objective is to simulate the behavior of large, spreading fires, FSim restricts fire growth to days on which ERC reaches or exceeds the 80th percentile condition. On those days, the length of the simulated burning period is set at 1 hour, 3 hours, and 5 hours for the 80th, 90th, and 97th percentile ERC conditions, respectively. Fire growth and behavior is calculated using standard FlamMap routines and a minimum travel time (MTT) algorithm. Suppression influences on growth are accounted for by a statistical model that indicates probability of containment (cessation) based on spread rates and fuel types throughout each fire simulation. A ‘perimeter trimming’ function, which better reflects the influence of suppression activities on fire spread and improves modeled fire size distributions, is also included in the fire suppression module.

The fire growth simulations, when run repeatedly with different ignition locations and weather streams, generate burn probabilities and fire behavior distributions at each landscape location (i.e., cell or pixel). Results are objectively evaluated through comparison with historical fire patterns and statistics, including the mean annual burn probability and fire size distribution, for each simulation unit. This evaluation is part of the FSim calibration process, whereby simulation inputs are adjusted until the slopes of the historical and modeled fire size distributions are similar and the modeled average burn probability falls within an acceptable range of the historical reference value (i.e., the 95% confidence interval for the mean).

For a technical overview of the Fire Simulation (FSim) system developed by the USDA Forest Service, Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk, see Finney et al. (2011). For further information about the FSim Burn Probability dataset used in this data publication, see Dillon et al. (2023).

Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3
Tool_Access_Information:
Online_Linkage: https://www.firelab.org/project/fsim-wildfire-risk-simulation-software
Tool_Access_Instructions:
Please send requests to: Fire Modeling Institute, USFS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, Montana, 59808; SM.FS.mso_fmi@usda.gov
Tool_Citation:
Citation_Information:
Originator: Finney, Mark A.
Originator: McHugh, Charles W.
Originator: Grenfell, Isaac C.
Originator: Riley, Karin L.
Originator: Short, Karen C.
Publication_Date: 2011
Title:
A simulation of probabilistic wildfire risk components for the continental United States
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Stochastic Environmental Research and Risk Assessment
Issue_Identification: 25: 973-1000
Online_Linkage: https://doi.org/10.1007/s00477-011-0462-z
Online_Linkage: https://research.fs.usda.gov/treesearch/39312
Analytical_Tool:
Analytical_Tool_Description:
FlamMap is a fire analysis desktop application that ONLY runs in a 64-bit Windows Operating System environment. It is a fire mapping and analysis system that describes potential fire behavior for constant environmental conditions (weather and fuel moisture). Fire behavior is calculated for each pixel within the landscape file independently. Potential fire behavior calculations include surface fire spread, flame length, crown fire activity type, crown fire initiation, and crown fire spread. Dead fuel moisture and conditioning of dead fuels in each pixel is based on slope, shading, elevation, aspect, and weather. With the inclusion of FARSITE, FlamMap can now compute wildfire growth and behavior for longer time periods under heterogeneous conditions of terrain, fuels, fuel moistures and weather.)
Tool_Access_Information:
Online_Linkage: https://www.firelab.org/project/flammap
Tool_Access_Instructions:
see website for more details
Tool_Citation:
Citation_Information:
Originator: Finney, Mark A.
Publication_Date: 2006
Title:
An overview of FlamMap modeling capabilities
Geospatial_Data_Presentation_Form: conference proceedings
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
p. 213-220
Online_Linkage: https://research.fs.usda.gov/treesearch/25948
Larger_Work_Citation:
Citation_Information:
Originator: Andrews, Patricia L. (comp.)
Originator: Butler, Bret W. (comp.)
Publication_Date: 2006
Title:
Fuels management-how to measure success: conference proceedings
Geospatial_Data_Presentation_Form: conference proceedings
Series_Information:
Series_Name: Proceedings
Issue_Identification: RMRS-P-41
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
28-30 March 2006 in Portland, OR
Online_Linkage: https://research.fs.usda.gov/treesearch/24476
Back to Top
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Several of the datasets described here are derived from wildfire simulation modeling, and their exact accuracy cannot be measured. These include Building Exposure Type, Housing Unit Exposure, Housing Unit Impact, and Housing Unit Risk. They are intended to be relative measures of wildfire risk for planning purposes. The FSim burn probability dataset was objectively evaluated and calibrated against historical wildfire occurrence statistics within 136 distinct regions of contemporary wildfire activity (pyromes) across the United States (Short et al. 2020). See Dillon et al. (2023) for a more detailed description of FSim calibration. Some LANDFIRE fuels and vegetation data used as inputs have also been evaluated for efficacy and calibrated to meet the objectives of LANDFIRE. More information can be found at: https://www.landfire.gov/data/quality_assessments. No explicit evaluation or calibration is possible for the WildEST (FlamMap-based) intensity datasets, however the LANDFIRE FBFM is an important input which has been evaluated, and the WildEST modeling system is rooted in established fire behavior models and methods (see Finney 2006, and Scott et al. 2020). As such, modeled intensity is considered a robust characterization as input to the flame length probability products.

Datasets included here describing population, buildings and housing units were creating using the combination of U.S. Census datasets and building footprints. Accuracy analyses have not been completed for WRC 2.0 Populated Areas datasets. We did, however, perform an analysis on population and housing unit data created for WRC 1.0 (which included Scott et al. 2020, https://doi.org/10.2737/RDS-2020-0060 and Scott et al. 2020, https://doi.org/10.2737/RDS-2020-0016), carefully checking to ensure that numbers of homes and people derived similarly to the datasets in this publication generally agreed with published population estimates (Scott et. al 2020).

Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3

Finney, Mark A. 2006. An overview of FlamMap fire modeling capabilities. In: Andrews, Patricia L.; Butler, Bret W., comps. 2006. Fuels management-how to measure success: conference proceedings. 28-30 March 2006 in Portland, OR. Proceedings. RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 213-220. https://research.fs.usda.gov/treesearch/25948

Scott, Joe H. 2020. A deterministic method for generating flame-length probabilities. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, eds. 2020. Proceedings of the fire continuum-preparing for the future of wildland fire. 21-24 May 2018 in Missoula, MT. Proceedings. RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 195-205. https://research.fs.usda.gov/treesearch/62336

Scott, Joe H.; Brough, April M.; Gilbertson-Day, Julie W.; Dillon, Gregory K.; Moran, Christopher. 2020. Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0060

Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data Archive. Updated 25 November 2020. https://doi.org/10.2737/RDS-2020-0016

Short, Karen C.; Grenfell, Isaac C.; Riley, Karin L.; Vogler, Kevin C. 2020. Pyromes of the conterminous United States. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0020
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Quantitative accuracy cannot be evaluated.
Logical_Consistency_Report:
Pixel values in these Wildfire Risk to Communities datasets should be within the following ranges:
Building Count: Integer values between 0 and 50.
Building Density: Integer values between 0 and 8,054.
Building Coverage: Integer values between 0 and 100.
Population Count: Floating point values between 0 and 7,571.
Population Density: Integer values between 0 and 109,512.
Housing Unit Count: Floating point values between 0 and 3,030.4.
Housing Unit Density: Integer values between 0 and 62,264.
Housing Unit Exposure: Floating point values between 0 and 0.13.
Housing Unit Impact: Integer values between 0 and 1,952,625,152.
Housing Unit Risk: Integer values between 0 and 7,556,012.
Completeness_Report:
For Building Count, Building Density, Building Coverage, Population Count, Population Density: All pixels that are part of the land and water of the United States have valid non-negative values.

For Housing Unit Exposure, Housing Unit Impact, and Housing Unit Risk: All pixels in areas in populated areas (where HUDen > 0) have valid non-negative values. Pixels outside of populated areas are NoData.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Scott, Joe H.
Originator: Dillon, Gregory K.
Originator: Jaffe, Melissa R.
Originator: Vogler, Kevin C.
Originator: Olszewski, Julia H.
Originator: Callahan, Michael N.
Originator: Karau, Eva C.
Originator: Lazarz, Mitchell T.
Originator: Short, Karen C.
Originator: Riley, Karin L.
Originator: Finney, Mark A.
Originator: Grenfell, Isaac C.
Publication_Date: 2024
Title:
Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States
Edition: 2nd
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0016-2
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20201231
Single_Date/Time:
Calendar_Date: 20221231
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
WRC Landscape-wide hazard and risk rasters (Scott et al. 2024)
Source_Contribution:
Burn probability (BP) and/or flame-length probabilities (FLPs) modeled with FSim and WildEST were primary spatial inputs to exposure and risk datasets presented here. BP provided information about the overall probability of any 30-meter pixel experiencing a large fire of any intensity. FLPs provided information about the conditional probability of particular fire intensity levels (i.e., likelihood of a particular intensity level, given a fire) for every 30-meter pixel.
Source_Information:
Source_Citation:
Citation_Information:
Originator: ONEGEO
Publication_Date: Unknown
Title:
Building Footprints
Geospatial_Data_Presentation_Form: vector digital data
Other_Citation_Details:
Dataset acquired January 2023, Building footprints represent 2022 conditions
Online_Linkage: https://onegeo.co
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2022
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
ONEGEO Building Footprints
Source_Contribution:
Building footprint data were first processed to remove buildings unlikely to be primary residential, commercial or industrial buildings. The locations of remaining building points were used to spatially distribute population within each Census Block. Building footprint data were also a primary input to the building coverage raster.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Oak Ridge National Laboratory (ORNL)
Originator: Federal Emergency Management Agency (FEMA)
Publication_Date: Unknown
Title:
USA Structures
Geospatial_Data_Presentation_Form: vector digital data
Other_Citation_Details:
Dataset acquired December 2022. Building footprints represent 2022 conditions.
Online_Linkage: https://gis-fema.hub.arcgis.com/pages/usa-structures
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2022
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
USA Structures
Source_Contribution:
Building footprint data were first processed to remove buildings unlikely to be primary residential, commercial or industrial buildings. The locations of remaining building points were used to spatially distribute population within each Census Block. Building footprint data were also a primary input to the building coverage raster.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Agriculture, Forest Service
Originator: U.S. Department of the Interior
Publication_Date: 2022
Title:
LANDFIRE 2.2.0 40 Scott and Burgan Fire Behavior Fuel Models layer
Edition: 2.2.0
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details:
Scott, Joe H.; Burgan, Robert E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 p. https://doi.org/10.2737/RMRS-GTR-153
Online_Linkage: https://www.landfire.gov/fuel.php
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2022
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
LANDFIRE FBFM40
Source_Contribution:
The FBFM40 raster was used to identify pixels with habitable land cover (all surface fuel model types except open water or permanent snow/ice).
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Automated Lands Program (ALP)
Publication_Date: Unknown
Title:
S_USA.Wilderness
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: USDA Forest Service
Other_Citation_Details:
BoundaryDesignated_Wilderness_EDW
Online_Linkage: https://data.fs.usda.gov/geodata/edw/edw_resources/fc/S_USA.Wilderness.gdb.zip
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2015
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
USDA Forest Service Designated Wilderness boundaries
Source_Contribution:
Wilderness boundaries were included as inputs to a protected areas mask used to filter out pixels that were erroneously identified as buildings in building footprint datasets.
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Automated Lands Program (ALP)
Publication_Date: Unknown
Title:
S_USA.OtherNationalDesignatedArea
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: USDA Forest Service
Other_Citation_Details:
BoundaryDesignated_OtherNationalDesignatedArea_EDW
Online_Linkage: https://data.fs.usda.gov/geodata/edw/edw_resources/fc/S_USA.OtherNationalDesignatedArea.gdb.zip
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2015
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
USDA Forest Service Other National Designated Area boundaries
Source_Contribution:
Designated area boundaries were included as inputs to a protected areas mask used to filter out pixels that were erroneously identified as buildings in building footprint datasets.
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service
Publication_Date: 2014
Title:
USA.RoadlessArea_2001
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: USDA Forest Service
Online_Linkage: https://data.fs.usda.gov/geodata/edw/edw_resources/fc/S_USA.RoadlessArea_2001.gdb.zip
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2014
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
USDA Forest Service Roadless Areas
Source_Contribution:
Roadless area boundaries were included as inputs to a protected areas mask used to filter out pixels that were erroneously identified as buildings in building footprint datasets. Visual inspection revealed that we could reduce false positives by removing pixels mapped as buildings in roadless areas in the following states: Arizona, California, Colorado, Florida, Idaho, Kentucky, Montana, Nevada, New Mexico, North Dakota, Oregon, Pennsylvania, South Dakota, Texas, Utah, Washington, and Wyoming.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Interior, Bureau of Land Management
Publication_Date: Unknown
Title:
BLM National Landscape Conservation System (NLCS) Wilderness Areas and Other Related Lands
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: U.S. Department of Interior, Bureau of Land Management
Online_Linkage: https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-nlcs-wilderness-areas-polygons
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2023
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
BLM NLCS Wilderness Area boundaries
Source_Contribution:
Wilderness boundaries were included as inputs to a protected areas mask used to filter out pixels that were erroneously identified as buildings in building footprint datasets.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Interior
Publication_Date: Unknown
Title:
Department of Interior wilderness areas
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: U.S. Department of Interior
Other_Citation_Details:
Dataset acquired in 2023 from Craig Thompson, Department of Interior Office of Wildland Fire.
Type_of_Source_Media: Personal communication
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2023
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
DOI Wilderness Area boundaries
Source_Contribution:
Wilderness boundaries were included as inputs to a protected areas mask used to filter out pixels that were erroneously identified as buildings in building footprint datasets.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
U.S. Census Bureau: American Community Survey Block Group Population Count 5-yr Estimates
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census Bureau
Online_Linkage: https://www.census.gov/data/developers/data-sets/acs-5year.html
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20160101
Ending_Date: 20201231
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2021 ACS 5-yr Estimates
Source_Contribution:
ACS Block Group 5-yr population estimates provide population counts at the Block Group level.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
U.S. Census Bureau: Population Estimates Program
Edition: 2021
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census Bureau
Online_Linkage: https://www.census.gov/programs-surveys/popest.html
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20200401
Ending_Date: 20210701
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2021 PEP
Source_Contribution:
We used county level population estimates for the vintage year 2021 that capture changes from April 1, 2020 to July 1, 2021 to improve currency of population estimates.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
Decennial Census Total Population Estimates
Edition: 2020
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census Bureau
Other_Citation_Details:
2020 Census: P.L. 94-171 Redistricting Data Total Population
Online_Linkage: https://www.nhgis.org/tabular-data-sources#2020
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2020 Decennial Census Total Population Estimates
Source_Contribution:
2020 Decennial Census Total Population Estimates enable downscaling of ACS estimates from Block Group to Block level.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census
Publication_Date: Unknown
Title:
Occupancy Status dataset within the Decennial Census Redistricting Dataset
Edition: 2020
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census
Other_Citation_Details:
H1OCCUPANCY STATUS 2020:DEC Redistricting Data (PL 94-171)
Online_Linkage: https://data.census.gov/table/DECENNIALPL2020.H1?q=&y=2020&d=DEC%20Redistricting%20Data%20(PL%2094-171)
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2020 Occupancy Status
Source_Contribution:
The total housing units by Block, as reported in the Occupancy Status dataset published in the Decennial Census Redistricting Dataset provides the basis for Housing Unit Count.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
Group Quarters Population by Major Population Type table of the 2020 Redistricting dataset
Edition: 2020
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census Bureau
Online_Linkage: https://data.census.gov/all?q=Group+Quarters+Population
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2020 Group Quarters Population
Source_Contribution:
We used the Group Quarters Population by Major Population Type table of the 2020 Redistricting dataset to represent populations that live in group quarters, both institutional (i.e. prisons, juvenile detention centers, and skilled nursing facilities) or noninstitutional (i.e. military housing, university housing).
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
Current Population Survey (CPS) Annual Social and Economic Supplement – Historical Households Table
Edition: 2022
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: U.S. Census Bureau
Online_Linkage: https://www2.census.gov/programs-surveys/demo/tables/families/time-series/households/hh6.xls
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2022
Source_Currentness_Reference:
Observed
Source_Citation_Abbreviation:
2022 CPS Annual Social and Economic Supplement
Source_Contribution:
The CPS Annual Social and Economic Supplement provides the estimate of people per household to compute population per housing unit for group quarters.
Process_Step:
Process_Description:
The steps listed below describe the process used to produce the spatial datasets for populated areas for the WRC 2.0 data release. This includes methods for: 1) producing spatial datasets of populated areas, and 2) creating spatial datasets that represent wildfire exposure and risk to populated areas in the United States.

NOTE: Dataset-specific FGDC-CSDGM metadata are provided with each TIFF file. Additional details regarding the process steps can be found in \Supplements\WRC_V2_Methods_PopulatedAreas.pdf.
Process_Date: 2023
Process_Step:
Process_Description:
1. Produce annual Burn Probability (BP; wildfire likelihood), Flame Length Probabilities (FLPs; intensity), Conditional Risk to Potential Structures (cRPS), Risk to Potential Structures (RPS), and Exposure Type rasters at 30-m resolution for all lands in the U.S. Methods for these datasets are described in Scott et al. (2024).
Source_Used_Citation_Abbreviation:
WRC 2.0 Landscape-wide hazard and risk rasters (Scott et al. 2024)
Process_Date: 2023
Process_Step:
Process_Description:
2. Prepare building footprint data by combining footprint datasets from 3DBuildings and USA Structures to create an integrated building footprints (IBF) dataset. Eliminate buildings with a footprint area smaller than 40 square meters (m²) (430 square feet), or with a centroid falling on a pixel of uninhabitable land cover (i.e., open water and permanent snow/ice as mapped in the LANDFIRE FBFM40 raster) to create the qualifying building footprints (QBF) dataset used as an input for the rasters described below.
Source_Used_Citation_Abbreviation:
ONEGEO Building Footprints, USA Structures, LANDFIRE FBFM40
Process_Date: 2023
Process_Step:
Process_Description:
3. Develop a protected areas mask to remove building footprints that are “false positives” (mapped footprints that do not represent true buildings) by filtering out buildings mapped in Wilderness or Roadless areas (as defined by the following datasets: Department of Interior wilderness areas, USDA Forest Service Wilderness Area boundaries, Other National Designated-Area boundaries and Roadless area boundaries).
Source_Used_Citation_Abbreviation:
USDA Forest Service Designated Wilderness boundaries, USDA Forest Service Other National Designated Area boundaries, USDA Forest Service Roadless Areas, BLM NLCS Wilderness Area boundaries, DOI Wilderness Area boundaries
Process_Date: 2023
Process_Step:
Process_Description:
4. Produce the Building Count raster to represent the count of qualifying building centroids in the QBF dataset located within each 30-m pixel. The Building Count dataset is the raster equivalent of a building-point feature class, with integer pixel values that represent the number of qualifying building centroids within a 30-m pixel.
Process_Date: 2023
Process_Step:
Process_Description:
5. Produce the Building Density raster to represent the density of qualifying building centroids in the QBF dataset (buildings/km²). We generated a smoothed Building Density raster using the Building Count raster and LANDFIRE FBFM40 data in a four-step process: 1) Calculate a 200-m radius moving-window sum of the 30-m Building Count raster, 2) Calculate a 200-m radius moving-window sum of habitable land cover (in km²), 3) Divide the sum-of-Building Count raster produced in step 1 by the sum-of-habitable-land-cover raster produced in step 2 to generate building density in buildings/km², 4) Convert the raster values to integers. Set values less than 0.25 buildings/km² to zero. Set values between 0.25 and 1 to a value of 1 building/km². Round all other values to the nearest integer.
Source_Used_Citation_Abbreviation:
LANDFIRE FBFM40
Process_Date: 2023
Process_Step:
Process_Description:
6. Produce the Building Coverage dataset to depict the percentage of habitable land area covered by qualified building footprints (QBF) with the following steps: 1) Define a small building as any building footprint in the QBF covering an area less than or equal to 900 m² (the size of one 30-m pixel) and map its building footprint area (m²) to the 30-m pixel its centroid falls within. Then sum building footprint area for all remaining small building centroids within a pixel to obtain the footprint contribution from small buildings. 2) Define a large building as a building footprint covering more than 900 m² (i.e., more than one pixel). Allocate building footprint area appropriately for any given building shape over 900 m² by rasterizing building polygons to 30-m resolution, snapping them to the LANDFIRE FBFM40 raster, and removing any building points located on water or permanent snow/ice as determined by the LANDFIRE FBFM40 dataset. 3) Calculate the sum of the small and large building footprint contributions for a given 30-m pixel and then generate the smoothed Building Coverage raster in a three-step process: a) calculate a 75-m radius moving-window sum of the small-and-large-combined 30-m building footprint area raster on habitable land cover (m²); b) calculate a 75-m radius moving-window sum of habitable land area (m²); c) divide the smoothed building-area raster produced in step a by the smoothed habitable land cover raster produced in step b to generate proportion of building coverage. The values in the raster from step c represent the proportion of habitable land cover in the approximately 5-acre area surrounding each pixel covered by a building footprint. 4) Create the final Building Coverage raster by multiplying the building cover proportions (0 to 1) by 100 and rounding to the nearest integer. This converts proportions to percentages with whole number values. Set values less than or equal to 1 percent to zero.
Source_Used_Citation_Abbreviation:
LANDFIRE FBFM40
Process_Date: 2023
Process_Step:
Process_Description:
7. Produce the Population Count (PopCount) raster with the following general steps. 1) Estimate 2021 Census Block Group population count using both the 2021 5-year American Community Survey (ACS) and 2021 Population Estimates Program (PEP) population estimates: a) downscale ACS population counts from Block Group to Block level using Decennial Census Total Population proportions, b) leverage 2021 PEP data (by county) to increase (or decrease) the Decennial Census Block population by the fractional population change for the county containing the Block, c) assign the larger of the ACS and PEP population estimates to the final estimated population count for a given block. 2) Allocate population to building points in the following steps: a) calculate the area of each building footprint polygon (in square meters) from the QBF dataset filtered by protected areas and converted to point features (footprint centroids), b) for each Census Block nationwide divide the 2021 PEP-adjusted Census Block population count obtained in the first step by the count of remaining building points in the Block. This ratio is the effective population count per building point—the population represented by each building point. This becomes a Block level population-per-building-centroid attribute in the building points dataset. 3) Produce the PopCount raster by converting the building points to a 30-m raster where the pixel value is the sum of the population-per-building-centroid attribute of all building centroids within the raster grid cell, while accounting for missing Blocks (Census Blocks with buildings but no population). The final Population Count raster is the effective population per building point multiplied by the number of building points falling within the pixel. It is a 30-m raster with pixel values representing residential population count (persons) in each pixel.
Source_Used_Citation_Abbreviation:
2021 ACS 5-yr Estimates, 2021 PEP, 2020 Decennial Census Total Population Estimates
Process_Date: 2023
Process_Step:
Process_Description:
8. Produce the Population Density raster (PopDen) using the Population Count raster and habitable land cover (any pixel not mapped as water, or permanent snow/ice in the LANDFIRE FBFM40 raster) with the following general steps. 1) Calculate a 200-m radius moving-window sum of the 30-m PopCount raster. 2) Calculate a 200-m radius moving-window sum of habitable land cover (in km²). 3) Divide the smoothed PopCount raster produced in step 1 by the smoothed habitable land cover raster produced in step 2 to generate population density in people/km². 4) Convert the PopDen raster values to integers. 5) Account for Census Blocks with population but no buildings with the following steps: a) estimate the mean population density across the Block as the Census Block population count divided by the habitable land cover within the Block (all but water and permanent snow/ice), b) if the mean population density is greater than 1 person per 100 acres, allocate the population count uniformly across the habitable land cover in the Block before generating the final PopDen raster. If the mean population density is less than or equal to 1 person per 100 acres, reset the population count to zero. Setting this minimum mean density is necessary to avoid including large areas of very low population density in the final PopDen raster. 6) Set values less than 0.25 people/km² to zero. Set values between 0.25 and 1 to a value of 1 person/km². Round all other values to the nearest integer.
Source_Used_Citation_Abbreviation:
LANDFIRE FBFM40
Process_Date: 2023
Process_Step:
Process_Description:
9. Produce the Housing Unit Count raster (HUCount) using U.S. Census Bureau Redistricting data and Qualified Building Footprints with the following steps. 1) Allocate housing unit data to building points using the number of housing units in each census block from the Occupancy Status table of the 2020 Redistricting dataset, then divide the reported housing units in each Block by the total number of buildings in the filtered QBF dataset to calculate the housing unit count per building centroid attribute. 2) To represent populations that live in group quarters, both institutional (i.e., prisons, juvenile detention centers, and skilled nursing facilities) or noninstitutional (i.e., military housing, university housing), use the Group Quarters Population by Major Population Type table of the 2020 Redistricting dataset, and convert to housing units by dividing by 2.5 persons per housing unit (the average population per housing unit as listed in the Annual Social and Economic Supplement to the Current Population Survey). 3) Sum the total group housing units in a Block and divide by the total number of buildings in the Block. 4) Convert the building points to a 30-m raster where the raster value is the sum of the housing-unit-per-centroid attribute of all building centroids within each raster grid cell. Do this individually for both the redistricted housing unit data and the group population housing unit data, then merge both raster layers to create the final HUCount raster. HUCount pixel values represent the number of housing units in each pixel, posted as floating point decimal values (not integerized) to avoid eliminating buildings in Blocks with very low population.
Source_Used_Citation_Abbreviation:
ONEGEO Building Footprints, USA Structures, 2020 Occupancy Status, 2020 Group Quarters Population, 2022 CPS Annual Social and Economic Supplement
Process_Date: 2023
Process_Step:
Process_Description:
10. Produce the Housing Unit Density raster (HUDen) from the Housing Unit Count raster using a four-step process: 1) calculate a 200-m radius moving-window sum of the 30-m housing-unit count raster; 2) calculate a 200-m radius moving-window sum (in km²) of habitable land cover (mapped as water, or permanent snow/ice in the LANDFIRE FBFM40 raster); 3) divide the smoothed housing-unit count raster produced in step 1 by the smoothed habitable land cover raster produced in step 2 to generate housing-unit density in housing units/km²; 4) convert the HUDen raster values to integers, set values less than or equal to 0.1 HU/km² to zero, set values between 0.1 and 1 to a value of 1 HU/km², and round all other values to the nearest integer.
Source_Used_Citation_Abbreviation:
LANDFIRE FBFM40
Process_Date: 2023
Process_Step:
Process_Description:
11. Produce the Housing Unit Exposure raster (HUExposure) with the following general steps: 1) convert the HUDen raster to a 30-m raster of housing unit count by multiplying by 0.0009 (square kilometers per pixel); then 2) multiply housing unit count by the Burn Probability raster from Scott et al. (2024). Values of HUExposure are floating point decimal numbers that represent the annual average housing units exposed per pixel, over the 20,000+ annual simulation iterations (Dillon et al. 2023). They provide a relative measure of the mean annual number of homes that could be exposed to wildfire. Pixels where the Housing Unit Density raster is zero are NoData in the HUExposure raster.

Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3
Source_Used_Citation_Abbreviation:
WRC Landscape-wide hazard and risk rasters (Scott et al. 2024)
Process_Date: 2023
Process_Step:
Process_Description:
12. Produce the Housing Unit impact raster (HUImpact) with the following general steps: 1) convert the HUDen raster to a 30-m raster of housing unit count by multiplying by 0.0009; 2) then multiply housing unit count per pixel by the cRPS raster which captures the conditional probability of different fire intensities if a fire were to occur, and the relative consequences to a structure at those intensities (see Scott et al. 2024 for cRPS response functions), 3) then multiply by the Exposure Type raster produced in Scott et al. 2024 to mimic the reduction of potential losses with distance from burnable land cover. Calculated values of HUImpact can be very small decimal numbers. Because it is a unitless index, HUImpact values can be converted to integers while preserving the relative values and as much precision as possible. To produce the final integer version of the HUImpact raster, multiply the initial values by 1,000,000 and round to the nearest integer. Pixels where the HUDen raster is zero are NoData in the HUImpact raster. Consideration of local variation in susceptibility is beyond the scope of the Wildfire Risk to Communities project, and HUImpact should be considered a landscape metric rather than specific to any one home.
Source_Used_Citation_Abbreviation:
WRC Landscape-wide hazard and risk rasters (Scott et al. 2024)
Process_Date: 2023
Process_Step:
Process_Description:
13. Produce the Housing Unit risk raster (HURisk) by simply multiplying the HUImpact raster (as described in the previous step) by the burn probability raster from Scott et al. (2024). To produce the final integer version of the HURisk raster, we multiply by 1,000,000 and round to the nearest integer. Pixels where the HUDen raster is zero are NoData in the HURisk raster. As is the case for HUImpact, generalized response functions are used in the calculation of HURisk, so it should be considered a landscape metric rather than specific to any one home.
Source_Used_Citation_Abbreviation:
WRC Landscape-wide hazard and risk rasters (Scott et al. 2024)
Process_Date: 2023
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Spatial_Data_Organization_Information:
Raster_Object_Information:
Raster_Object_Type: Pixel
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below is a description of the files included in this data publication.

DATA FILES (10 × 52)

Georeferenced TIFF files are provided for the each of the following spatial extents: continental U.S., the District of Columbia, and each U.S. state ([EXTENT] = CONUS for continental U.S., DC for District of Columbia, AB for Alabama, AK for Alaska, AZ for Arizona, AK for Arkansas, CA for California, ..., WI for Wisconsin, and WY for Wyoming). For each extent there are ten different raster datasets:

1. BuildingCount_[EXTENT].tif: Continuous integer values representing the count of buildings located within each 30-meter (m) pixel. Values for the U.S. are between 0 and 50.

2. BuildingDensity_[EXTENT].tif: Continuous integer values representing the density of buildings in within each 30-m pixel (buildings per square kilometer [km²]). Values for the U.S. are between 0 and 8,054.

3. BuildingCover_[EXTENT].tif: Continuous integer values representing the percentage of habitable land area covered by buildings in each 30-m pixel. Values for the U.S. are between 0 and 100.

4. PopCount_[EXTENT].tif: Continuous floating point values representing residential population count (persons) in each 30-m pixel. Values for the U.S. are between 0 and 7,571.

5. PopDen_[EXTENT].tif: Continuous integer values representing the residential population density (people/km²) in each 30-m pixel. Values for the U.S. are between 0 and 109,512.

6. HUCount_[EXTENT].tif: Continuous floating point values representing the number of housing units in each 30-m pixel. Values for the U.S. are between 0 and 3,030.4.

7. HUDen_[EXTENT].tif: Continuous integer values representing housing-unit density in each 30-m pixel (housing units/km²). Values for the U.S. are between 0 and 62,264.

8. HUExposure_[EXTENT].tif: Continuous floating point values representing the expected number of housing units within a 30-m pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. Values for the U.S. are between 0 and 0.13.

9. HUImpact_[EXTENT].tif: Continuous integer values representing the relative potential impact of fire to housing units at any 30-m pixel, if a fire were to occur. It is a unitless index that incorporates the general consequences of fire on a home as a function of fire intensity. Values for the U.S. are between 0 and 1,952,625,152.

10. HURisk_[EXTENT].tif: Continuous integer values of a unitless index that represents all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on 30-m pixels where housing unit density is greater than zero. Values for the U.S. are between 0 and 7,556,012.

(Associated OVR files are included, and contain pyramids that allow the raster datasets to draw more quickly in GIS software. Associated XML files contain dataset-specific FGDC-CSDGM metadata containing a description of the content, quality, and other characteristics of the data.)



SUPPLEMENTAL FILES (2)

1. \Supplements\WRC_V2_Methods_PopulatedAreas.pdf: Portable Document Format (PDF) file containing detailed descriptions of the data products included in this publication and the methods used to create them.

2. \Supplements\WRC_V2_Populated Areas_GISDataSymbology.pdf: PDF file with suggested class definitions and colors for displaying the landscape-wide risk raster datasets in GIS software.
Entity_and_Attribute_Detail_Citation:
see supplemental files
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of September 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2020-0060-2
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIFF
Format_Version_Number: 2024
Format_Information_Content:
BuildingDensity and BuildingCount: 16-bit unsigned integer; BuildingCover: 16-bit signed integer; PopDen, HUDen, HUImpact, and HURisk: 32-bit signed integer; PopCount, HUCount, and HUExposure: 32-bit floating point; LZW compression; pyramids: vary by extent, Nearest Neighbor resampling
File_Decompression_Technique: Files zipped using the zipfile module in Python 2.7.16 (.ZIP file format version 6.3.6). (We recommend putting all *.zip files into a folder named Data before unzipping.)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2020-0060-2
Digital_Form:
Digital_Transfer_Information:
Format_Name: PDF
Format_Version_Number: see Format Specification
Format_Information_Content:
Portable Document Format file
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2020-0060-2
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20240910
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Fire Modeling Institute (FMI)
Contact_Address:
Address_Type: mailing and physical
Address: Missoula Fire Sciences Laboratory
Address: 5775 US Hwy 10 W
City: Missoula
State_or_Province: MT
Postal_Code: 59808
Country: USA
Contact_Voice_Telephone: 406-329-4836
Contact_Electronic_Mail_Address: eva.c.karau@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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