Community resilience is the capacity of individuals and households within a community to absorb the external stresses of a disaster. To measure this, the Census Bureau produced the 2019 Community Resilience Estimates (CRE). To provide context to the estimates and add to the discussion of equity, the CRE program has created the Community Resilience Estimates Equity Supplement or CRE for Equity. The CRE for Equity dataset provides information about the nation, states, counties, and census tracts from three different data sources. These sources include the Community Resilience Estimates, the American Community Survey, and the Census Bureau’s Planning Database. Providing all this information in one dataset allows users quick access to the data on a variety of topics concerning social vulnerability and equity.

The Community Resilience Estimates track how socially vulnerable every neighborhood in the United States is to the impacts of a disaster. The Community Resilience Estimates use American Community Survey microdata and Population Estimates Program data to measure the ability of individuals and households to absorb the external stresses of the impacts of a disaster.

The table State shape file is part of the dataset Census Bureau Community Resilience Estimates (CRE) datasets, available at https://redivis.com/datasets/45vc-a00v3cwcb. It contains 51 rows across 1 variables.

The Community Resilience Estimates (CRE) program provides an easily understood metric for how socially vulnerable every neighborhood in the United States is to the impacts of disasters.This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census, CRE, and ACS when using this data.Overview:Community resilience is the capacity of individuals and households within a community to prepare, absorb, respond, and recover from a disaster. Local planners, policy makers, public health officials, emergency managers, and community stakeholders need a variety of estimates to help assess the potential resiliency and vulnerabilities of communities and their constituent populations to help prepare and plan mitigation, recovery, and response strategies. Community Resilience Estimates (CRE) focuses on developing a tool to identify socio-economic vulnerabilities within populations. The 2022 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2022 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). The CRE uses small area modeling techniques that can be used for a broad range of disaster related events (hurricanes, tornadoes, floods, economic shocks, etc.) to identify population concentrations likely to be relatively more impacted by and have greater difficulties overcoming disasters. The end result is a data product which measures vulnerability more accurately and timely.Data:The ACS is a nationally representative survey with data on the characteristics of the U.S. population. The sample is selected from all counties and county-equivalents and has a sample size of about 3.5 million housing units each year. It is the premier source for timely and detailed population and housing information about our nation and its communities. We also use auxiliary data from the PEP, the Census Bureau’s program that produces and publishes estimates of the population living at a given time within a geographic entity in the U.S. and Puerto Rico. We use population data from the PEP by age group, race and ethnicity, and sex. Since the PEP does not go down to the census tract level, the CRE uses the Public Law 94-171 summary files (PL94) and Demographic Housing Characteristics File (DHC) tables from the 2020 Decennial Census to help produce the population base estimates. Once the weighted estimates are tabulated, small area modeling techniques are used to create the estimates for the CRE. Components of Social Vulnerability (SV): Resilience to a disaster is partly determined by the components of social vulnerability exhibited within a community’s population. To measure these components and construct the community resilience estimates, we designed population estimates based on individual- and household-level components of social vulnerability. These components are binary indicators or variables that add up to a maximum of 10 possible components using data from the ACS. The specific ACS-defined measures we use are as follows: Components of Social Vulnerability (SV) for Households (HH) and Individuals (I):SV 1: Income-to-Poverty Ratio (IPR) < 130 percent (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH). SV 3: Unit-level crowding with >= 0.75 persons per room (HH). SV 4: Communication barrier defined as either: Limited English-speaking households1 (HH) orNo one in the household over the age of 16 with a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH). SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: No vehicle access (HH). SV 10: Households without broadband internet access (HH). Each individual is assigned a 0 or 1 for each of the components based upon their individual or household attributes listed above. It is important to note that SV 4 is not double flagged. An individual will be assigned a 1, if either of the characteristics is true for their household. For example, if a household is linguistically isolated and no one over the age of 16 has attained a high school diploma or more education, the household members are only flagged once. The result is an index that produces aggregate-level (tract, county, and state) small area estimates: the CRE. The CRE provide an estimate for the number of people with a specific number of social vulnerabilities. In its current data file layout form, the estimates are categorized into three groups: zero , one-two, or three plus social vulnerability components. Differences with CRE 2021:The number of census tracts have increased from 84,414 in CRE 2021 to 84,415 in CRE 2022. This is due to the boundary changes in Connecticut implemented in 2022 census data products. To accommodate the boundary change, Connecticut also now has nine planning regions instead of eight counties in CRE 2022.To avoid confusion, the modeled rates are now set to equal zero in CRE 2022 for geographic areas with zero population in universe. To improve the population base estimates, CRE 2022 uses more detailed decennial estimates from the 2020 DHC in addition to PL94, whereas CRE 2021 just used PL94 due to availability at the time. See “2022 Community Resilience Estimates: Detailed Technical Documentation” for more information. Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). This dataset does not contain values for Puerto Rico or Island Areas at any level of geography.Further Information:Community Resilience Estimates Program Website https://www.census.gov/programs-surveys/community-resilience-estimates.htmlCommunity Resilience Estimates Technical Documentation https://census.gov/programs-surveys/community-resilience-estimates/technical-documentation.htmlFor Data Questionssehsd.cre@census.gov

Components of Social Vulnerability refers to the categories of social vulnerability out of a possible 10 variables (e.g., poverty status, age 65+, unemployment, etc.), where 0 components is considered "low," 1-2 components is considered "moderate," and 3 or more components is considered is "high.".POPUNI refers to the population universe, which is the total population except those in adult correctional/juvenile facilities and college dorms..For the variables ending in PF_PR and PF_US, the given rate is compared to the PR rate and U.S. rate, respectively. For example, PRED0_PF_PR denotes whether the estimated percentage of individuals with zero components of social vulnerability is significantly different than the PR rate. These variables use the following values: -999 = Not applicable; -1 = Lower; 0 = Not statistically different; and 1 = Higher.

🇺🇸 미국

"Components of Social Vulnerability" refers to the categories of social vulnerability out of a possible 10 variables (e.g., poverty status, age 65+, unemployment, etc.), where 0 components is considered "low," 1-2 components is considered "moderate," and 3 or more components is considered is "high.".POPUNI refers to the population universe, which is the total population except those in adult correctional/juvenile facilities and college dorms.

The table Tract shape file is part of the dataset Census Bureau Community Resilience Estimates (CRE) datasets, available at https://redivis.com/datasets/45vc-a00v3cwcb. It contains 72831 rows across 1 variables.

This layer shows aggregate-level (state) small area estimates of community resilience. The CRE is the number of people facing a specific number of risks. In its current form, the data categorizes estimates into three groups: zero risks, 1-2 risks, and three or more risks. The layer includes estimates for all counties and tracts in the U.S. and their corresponding margins of error.Current Vintage: 2019Data downloaded from: Census Bureau's Community Resilience EstimatesThe United States Census Bureau's Community Resilience Estimates:About this ProgramDataTechnical DocumentationThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and Community Resilience Estimates when using this data.Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).

The Community Resilience Estimates for Puerto Rico track how socially vulnerable every neighborhood in Puerto Rico is to the impacts of a disaster. The Community Resilience Estimates use Puerto Rico Community Survey microdata and Population Estimates Program data to measure the ability of individuals and households to absorb the external stresses of the impacts of a disaster.

Abstract

The 2019 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2019 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). Local planners, policy makers, public health officials, and community stakeholders can use the estimates as one tool to help assess the potential resiliency of communities and plan mitigation and recovery strategies. The CRE uses small area modeling techniques. These techniques are flexible and can easily be modified for a broad range of uses (hurricanes, tornadoes, floods, economic recovery etc.).

The table is consisted of geographic infomation including state county and tract, and variables indcating risk factors. The CRE groups the population estimates into three categories: zero risk factors, one-two risk factors, and three plus risk factors. The data file includes the population estimate, estimate margin of error, rate, and rate margin of error for each of the three categories.

Community resilience describes the capacity of individuals and households within a community to absorb a disaster’s external stressors. The standard Community Resilience Estimates (CRE) measures a community’s social vulnerability to natural disasters. However, the social vulnerabilities to extreme heat exposure differ from other natural disasters. As a result, the CRE Team created a new set of estimates called the Community Resilience Estimates for Heat (CRE for Heat).The CRE for Heat is an experimental data product from the U.S. Census Bureau. Experimental data products are innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products. The Census Bureau is seeking feedback from data users and stakeholders on the quality and usefulness of these new products.In collaboration with Arizona State University’s Knowledge Exchange for Resilience (KER), the CRE Team produced the 2022 CRE for Heat using data on individuals and households. The data sources include the 2022 American Community Survey (ACS), the Census Bureau’s Population Estimates Program (PEP), and the 2020 Census. Based on feedback from data users, the CRE for Heat contains a new component of social vulnerability, “Households that potentially lack air conditioning”. This component of social vulnerability was created using data from the 2021 American Housing Survey, machine learning techniques, and auxiliary data. More information about this is found in the CRE for Heat Quick Guide.Local planners, policymakers, public health officials, and community stakeholders can use the CRE for Heat to assess their community’s vulnerability to extreme heat and plan cooling and intervention strategies. WHAT’S NEWComponents of Social Vulnerability (SV)The CRE adjusted terminology from “risk factors” to “components of social vulnerability” after discussions with stakeholders such as emergency managers and urban planners. In these fields, “risk” refers to the likelihood a disaster or event will occur. “Vulnerabilities” refer to the conditions people experience which may compound the impact of a disaster.The CRE Program is committed to providing a data product that is understandable and meets the needs of its users. To better explain the purpose of the estimates and how they were developed, the language was adjusted.“Components” highlights the combination of factors that define social vulnerability. “Social vulnerability” refers to the characteristics that could impede a community’s ability to deal with disasters and external stressors. The results of this assessment form the basis of a community’s Community Resilience Estimate.Extreme Heat ExposureThe CRE for Heat 2022 estimates contain an additional measure of exposure to extreme heat (PRED3EXP). Not all socially vulnerable communities are equally exposed to extreme heat. Pairing the CRE for Heat estimates with heat exposure data provides a more comprehensive look at social vulnerability to heat. In the 2022 CRE for Heat dataset, an area is considered exposed to extreme heat if it meets one of two criteria. The two heat exposure criteria are:Areas where the maximum air temperature has reached or exceeded 90 degrees Fahrenheit for two or more days in a row during 2022.Areas where estimated wet bulb temperature has reached or exceeded 80 degrees at any time during 2022.On the county and tract level files, these exposure variables are available as LONG_90_DAY and MAX_WBT.On the state and national file, the exposure variable, PRED3EXP_E, measures the estimated number of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. Similarly, PREDEXP_PE, measures the rate of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. These variables, and their accompanying margins of error, are available on the national and state files.Components of Social VulnerabilityComponents of Social Vulnerability (SV) for Households (HH) and Individuals (I)SV 1: Financial hardship defined as: Income-to-Poverty Ratio (IPR) < 130 percent (HH) or50% < for housing/rental costs (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH).SV 3: Housing quality described as:Unit-level crowding with > 0.75 persons per room (HH) orLive in mobile home, boat, RV, Van, or other (HH). SV 4: Communication barrier defined as either:Limited English-speaking households (HH) or No one in the household has a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH).SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: Transportation exposure described as:No vehicle access (HH) orWork commuting methods with increased exposure to heat (e.g., public transportation, bicycle, walking) (I). SV 10: Households without broadband Internet access (HH). SV 11: Households that potentially lack air conditioning (HH).

The table Counties shape file is part of the dataset Census Bureau Community Resilience Estimates (CRE) datasets, available at https://redivis.com/datasets/45vc-a00v3cwcb. It contains 3142 rows across 1 variables.

"Components of Social Vulnerability" refers to the categories of social vulnerability out of a possible 10 variables (e.g., poverty status, age 65+, unemployment, etc.), where 0 components is considered "low," 1-2 components is considered "moderate, and 3 or more components is considered is "high.".POPUNI refers to the population universe, which includes all adults except those in correctional or juvenile facilities and college dorms..For the variables ending in PF_PR and PF_US, the given rate is compared to the PR rate and U.S. rate, respectively. For example, PRED0_PF_PR denotes whether the estimated percentage of individuals with zero components of social vulnerability is significantly different than the PR rate. These variables use the following values: -999 = Not applicable; -1 = Lower; 0 = Not statistically different; and 1 = Higher.

Community resilience describes the capacity of individuals and households within a community to absorb a disaster’s external stressors. The standard Community Resilience Estimates (CRE) measures a community’s social vulnerability to natural disasters. However, the social vulnerabilities to extreme heat exposure differ from other natural disasters. As a result, the CRE Team created a new set of estimates called the Community Resilience Estimates for Heat (CRE for Heat).The CRE for Heat is an experimental data product from the U.S. Census Bureau. Experimental data products are innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products. The Census Bureau is seeking feedback from data users and stakeholders on the quality and usefulness of these new products.In collaboration with Arizona State University’s Knowledge Exchange for Resilience (KER), the CRE Team produced the 2022 CRE for Heat using data on individuals and households. The data sources include the 2022 American Community Survey (ACS), the Census Bureau’s Population Estimates Program (PEP), and the 2020 Census. Based on feedback from data users, the CRE for Heat contains a new component of social vulnerability, “Households that potentially lack air conditioning”. This component of social vulnerability was created using data from the 2021 American Housing Survey, machine learning techniques, and auxiliary data. More information about this is found in the CRE for Heat Quick Guide.Local planners, policymakers, public health officials, and community stakeholders can use the CRE for Heat to assess their community’s vulnerability to extreme heat and plan cooling and intervention strategies. WHAT’S NEWComponents of Social Vulnerability (SV)The CRE adjusted terminology from “risk factors” to “components of social vulnerability” after discussions with stakeholders such as emergency managers and urban planners. In these fields, “risk” refers to the likelihood a disaster or event will occur. “Vulnerabilities” refer to the conditions people experience which may compound the impact of a disaster.The CRE Program is committed to providing a data product that is understandable and meets the needs of its users. To better explain the purpose of the estimates and how they were developed, the language was adjusted.“Components” highlights the combination of factors that define social vulnerability. “Social vulnerability” refers to the characteristics that could impede a community’s ability to deal with disasters and external stressors. The results of this assessment form the basis of a community’s Community Resilience Estimate.Extreme Heat ExposureThe CRE for Heat 2022 estimates contain an additional measure of exposure to extreme heat (PRED3EXP). Not all socially vulnerable communities are equally exposed to extreme heat. Pairing the CRE for Heat estimates with heat exposure data provides a more comprehensive look at social vulnerability to heat. In the 2022 CRE for Heat dataset, an area is considered exposed to extreme heat if it meets one of two criteria. The two heat exposure criteria are:Areas where the maximum air temperature has reached or exceeded 90 degrees Fahrenheit for two or more days in a row during 2022.Areas where estimated wet bulb temperature has reached or exceeded 80 degrees at any time during 2022.On the county and tract level files, these exposure variables are available as LONG_90_DAY and MAX_WBT.On the state and national file, the exposure variable, PRED3EXP_E, measures the estimated number of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. Similarly, PREDEXP_PE, measures the rate of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. These variables, and their accompanying margins of error, are available on the national and state files.Components of Social VulnerabilityComponents of Social Vulnerability (SV) for Households (HH) and Individuals (I)SV 1: Financial hardship defined as: Income-to-Poverty Ratio (IPR) < 130 percent (HH) or50% < for housing/rental costs (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH).SV 3: Housing quality described as:Unit-level crowding with > 0.75 persons per room (HH) orLive in mobile home, boat, RV, Van, or other (HH). SV 4: Communication barrier defined as either:Limited English-speaking households (HH) or No one in the household has a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH).SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: Transportation exposure described as:No vehicle access (HH) orWork commuting methods with increased exposure to heat (e.g., public transportation, bicycle, walking) (I). SV 10: Households without broadband Internet access (HH). SV 11: Households that potentially lack air conditioning (HH).

This filtered view includes census tracts in Maricopa County only. Published by the US Census Bureau, the Community Resilience Estimates (CRE) for Heat measures social vulnerability specifically in the context of extreme heat exposure. See the "Source Link" below for more information.

In collaboration with Arizona State University’s Knowledge Exchange for Resilience, the CRE for Heat are produced using information on individuals and households from the American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP).

All credit for variables in AHRQ_included_variables.csv is attributed to

• AHRF: Area Health Resources Files (AHRF) 2009-2018. US Department of Health and
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  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
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• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
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  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
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  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
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  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
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  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
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• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
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  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
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• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
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• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
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  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
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  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
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• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
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• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
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  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
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  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
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  0248.1.xml.
  AHRQ SDOH Database (v1), from Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke;
  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
• AHRQ SDOH Database (v1), from National Center for Environmental Information (NCEI), National
  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
  Atmospheric Administration (NOAA), Storm Events Database 2009-2020 Retrieved August 2021
  from https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/.
• AHRQ SDOH Database (v1), from U.S. Census Bureau, Opportunity Insights. Opportunity Atlas [All
  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
  County Race, Gender, and Parental Income Percentile] Retrieved October 2021 from:
  https://www.c
• AHRQ SDOH Database (v1), from National Center for Education Statistics. U.S. Skills Map: State
  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
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• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Physician
  Compare. https://data.cms.gov/provider-data/topics/doctors-clinicians.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Provider of
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• AHRQ SDOH Database (v1), from Small Area Health Insurance Estimates. (n.d.). U.S. Census
  Bureau. https://www.census.gov/data/datasets/time-series/demo/sahie/estimates-acs.html
• AHRQ SDOH Database (v1), from Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D.,
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Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

The Biden-Harris administration announced the launch of a new Voluntary Community-Driven Relocation program, led by the Department of the Interior, to assist Tribal communities severely impacted by environmental threats. Through investments from President Biden’s Bipartisan Infrastructure Law and Inflation Reduction Act, the Department is committing $115 million for 11 severely impacted Tribes to advance relocation efforts and adaptation planning. Additional support for relocation will be provided by the Federal Emergency Management Administration (FEMA) and the Denali Commission. Alaska communities located along coastlines and tidally influenced rivers are vulnerable to coastal erosion. These communities face advanced planning decisions, such as implementing shore protection or moving infrastructure. This work aims to provide quantitative erosion exposure data to Alaskans that can be combined with local knowledge and evidence for developing hazard mitigation plans and strategies to address erosion. DGGS Report of Investigation 2021-3, Erosion exposure assessment of infrastructure in Alaska coastal communities, provides estimated erosion exposure for 48 communities from the Bering to the Beaufort seas. The Division of Geological & Geophysical Surveys conducted a shoreline change assessment to forecast 20-, 40-, and 60-year erosion estimates using the Digital Shoreline Analysis System (DSAS; Himmelstoss and others, 2018), and estimated the replacement cost of infrastructure in the forecast area. The geodatabase includes mean erosion forecasts and maximum uncertainties for 38 communities along with infrastructure locations and classification derived from Alaska Division of Community & Regional Affairs digital mapping products (DCRA, 2021) for 44 communities. All files are available from the DGGS website: https://doi.org/10.14509/30672. The sea level rise (SLR) coastal inundation layers were created using existing federal products: the (1) NOAA Coastal Digital Elevation Models (DEMs) and (2) 2022 Interagency Sea Level Rise Technical Report Data Files. The DEMs for the Continental United States (CONUS) are provided in North American Vertical Datum 1988 (NAVD 88) and were converted to Mean Higher High Water (MHHW) using the NOAA VDatum conversion surfaces; the elevation values are in meters (m). The NOAA Scenarios of Future Mean Sea Level are provided in centimeters (cm). The MHHW DEMs for CONUS were merged and converted to cm and Scenarios of Future Mean Sea Level were subtracted from the merged DEM. Values below 0 represent areas that are below sea level and are “remapped” to 1, all values above 0 are remapped to “No Data”, creating a map that shows only areas impacted by SLR. Areas protected by levees in Louisiana and Texas were then masked or removed from the results. This was done for each of the emissions scenarios (Lower Emissions = 2022 Intermediate SLR Scenario Higher Emissions = 2022 Intermediate High SLR Scenario) at each of the mapped time intervals (Early Century - Year 2030, Middle Century - Year 2050, and Late Century - Year 2090). The resulting maps are displayed in the CMRA Assessment Tool. County, tract, and tribal geographies summaries of percentage SLR inundation were also calculated using Zonal Statistics tools. The Sea Level Rise Scenario year 2020 is considered “baseline” and the impacts are calculated by subtracting the baseline value from each of the near-term, mid-term and long-term timeframes. Thumbnail image and following quote courtesy of The Yurok Tribe, “Klamath River estuary on the Yurok Indian Reservation, anticipated area of greatest direct impact from sea level rise.”

Description: The CRE groups the population estimates into three categories: zero components of social vulnerability, one-two components of social vulnerability, and three plus components of social vulnerability. The data file includes the population estimate, estimate margin of error, rate, rate margin of error, rate statistical flag vs. U.S., rate statistical flag vs. Puerto Rico for each of the three categories. The numeric population estimates are denoted by an E at the end of the variable name. The margin of error for these estimates have an “M”. The fields for rates and the accompanying margin of error are denoted with “PE” and “PM”. The fields for rate statistical flags are “PF_US” (statistical comparison vs. United States) and “PF_PR” (statistical comparison vs. Puerto Rico), both measured at 90% confidence level.Source: Community Resilience Estimates Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.