22 datasets found
  1. d

    US Social Vulnerability by Census Block Groups

    • dataone.org
    Updated Nov 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bryan, Michael (2023). US Social Vulnerability by Census Block Groups [Dataset]. http://doi.org/10.7910/DVN/ARBHPK
    Explore at:
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Bryan, Michael
    Area covered
    United States
    Description

    blockgroupvulnerability OPPORTUNITY The US Centers for Disease Control (CDC) publishes a set of percentiles that compare US geographies by vulnerability across household, socioeconomic, racial/ethnic and housing themes. These Social Vulnerability Indexes (SVI) were originally intended to to help public health officials and emergency response planners identify communities that will need support around an event. They are generally valuable for any public interest that wants to relate themselves to needy communities by geography. The SVI publication and its basis variables are provided at the Census tract level of geographic detail. The Census' American Community Survey is available down the to the block group level, however. Recasting the SVI methods at this lower level of geography allows it to be tied to thousands of other demographic variables available. Because the SVI relies on ACS variables only available at the tract level, a projection model needs to applied to approximate its results using blockgroup level ACS variables. The blockgroupvulnerability dataset casts a prediction for the CDCs logic for a new contribution to the Open Environments blockgroup series available on Harvard's dataverse platform. DATA The CDC's annual SVI publication starts with 23 simple derivations using 50 ACS Census variables. Next the SVI process ranks census geographies to calculate a rank for each, where Percentile Rank = (Rank-1) / (N-1). The SVI themes are then calculated at the tract level as a percentile rank of a sum of the percentile ranks of the first level ACS derived variables. Finally, the overall ranking is taken as the sum of the theme percentile rankings. The SVI data publication is keyed by geography (7 cols) where ultimately the Census Tract FIPS code is 2 State + 3 County + 4 Tract + 2 Tract Decimals eg, 56043000301 is 56 Wyoming, 043 Washakie County, Tract 3.01 republishes Census demographics called 'adjunct variables' including area, population, households and housing units from the ACS daytime population taken from LandScan 2020 estimates derives 23 SVI variables from 50 ACS 5 Year variables with each having an estimate (E_), estimate precentage (EP_), margin of error (M_), margin percentage (MP_) and flag variable (F_) for those greater than 90% or less than 10% provides the final 4 themes and a composite SVI percentile annually vars = ['ST', 'STATE', 'ST_ABBR', 'STCNTY', 'COUNTY', 'FIPS', 'LOCATION'] +\ ['SNGPNT','LIMENG','DISABL','AGE65','AGE17','NOVEH','MUNIT','MOBILE','GROUPQ','CROWD','UNINSUR','UNEMP','POV150','NOHSDP','HBURD','TWOMORE','OTHERRACE','NHPI','MINRTY','HISP','ASIAN','AIAN','AFAM','NOINT'] +\ ['TOTAL','THEME1','THEME2','THEME3','THEME4'] + \ ['AREA_SQMI', 'TOTPOP', 'DAYPOP', 'HU', 'HH'] knowns = vars + \ # Estimates, the result of calc against ACS vars [('E_'+v) for v in vars] + \ # Flag 0,1 whether this geog is in 90 percentile rank (its vulnerable) [('F_'+v) for v in vars] +\ # Margine of error for ACS calcs [('M_'+v) for v in vars] + \ # Margine of error for ACS calcs, as percentage [('MP_'+v) for v in vars] +\ # Estimates of ACS calcs, as percentage [('EP_'+v) for v in vars] + \ # Estimated percentile ranks [('EPL_'+v) for v in vars] + \ # Sum across var percentile ranks [('SPL_'+v) for v in vars]+ \ # Percentile rank of the sum of percentile ranks [('RPL_'+v) for v in vars] [c for c in svitract.columns if c not in knowns] The SVI themes range over [0,1] but the CDC uses -999 as an NA value; this is set for ~800 or 1% of tracts which have no total poulation. The themes are numbered: Socioeconomic Status – RPL_THEME1 Household Characteristics – RPL_THEME2 Racial & Ethnic Minority Status – RPL_THEME3 Housing Type & Transportation – RPL_THEME4 The themes with their variables and ACS sources are as follows: Unlike Census data, the CDC ranks Puerto Rico and Tribal tracts separately from the US otherwise. Theme SVI Variable ACS Table ACS Variables Socioeconomic E_UNINSUR S2701 S2701_C04_001E Socioeconomic E_UNEMP DP03 DP03_0005E Socioeconomic E_POV150 S1701 S1701_C01_040E Socioeconomic E_NOHSDP B06009 B06009_002E Socioeconomic E_HBURD S2503 S2503_C01_028E + S2503_C01_032E + S2503_C01_036E + S2503_C01_040E Household E_SNGPNT B11012 B11012_010E + B11012_015E Household E_LIMENG B16005 B16005_007E + B16005_008E + B16005_012E + B16005_013E + B16005_017E + B16005_018E + B16005_022E + B16005_023E + B16005_029E + B16005_030E + B16005_034E + B16005_035E + B16005_039E + B16005_040E + B16005_044E + B16005_045E Household E_DISABL DP02 DP02_0072E Household E_AGE65 S0101 S0101_C01_030E Household E_AGE17 B09001 B09001_001E Racial & Ethnic E_TWOMORE DP05 DP05_0083E Racial & Ethnic E_OTHERRACE DP05 DP05_0082E Racial & Ethnic E_NHPI DP05 DP05_0081E Racial & Ethnic E_MINRTY DP05 DP05_0071E + DP05_0078E + DP05_0079E + DP05_0080E + DP05_0081E + DP05_0082E + ... Visit https://dataone.org/datasets/sha256%3A3edd5defce2f25c7501953ca3e77c4f15a8c71251352373a328794f961755c1c for complete metadata about this dataset.

  2. a

    2018 ACS Demographic & Socio-Economic Data Of USA At Census Tract Level

    • one-health-data-hub-osu-geog.hub.arcgis.com
    Updated May 22, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    snakka_OSU_GEOG (2024). 2018 ACS Demographic & Socio-Economic Data Of USA At Census Tract Level [Dataset]. https://one-health-data-hub-osu-geog.hub.arcgis.com/items/5b67f243e6584ef1986f815932020034
    Explore at:
    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    snakka_OSU_GEOG
    Area covered
    Description

    Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at the census tract level, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsLocalized Interventions: Facilitates the development of localized interventions to address the needs of vulnerable populations within specific census tracts.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the census tract level.Research: Provides a detailed dataset for academic and applied research in socio-economic and demographic studies at a granular census tract level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific census tract areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

  3. a

    2019 ACS Demographic & Socio-Economic Data Of Oklahoma At Census Tract Level...

    • one-health-data-hub-osu-geog.hub.arcgis.com
    Updated Apr 7, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    snakka_OSU_GEOG (2024). 2019 ACS Demographic & Socio-Economic Data Of Oklahoma At Census Tract Level [Dataset]. https://one-health-data-hub-osu-geog.hub.arcgis.com/items/ba7206954ea5441b9539590303e50f8d
    Explore at:
    Dataset updated
    Apr 7, 2024
    Dataset authored and provided by
    snakka_OSU_GEOG
    Area covered
    Description

    we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI). American Community Survey (ACS):

    Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States. The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions. It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households. The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.

    CDC/ATSDR Social Vulnerability Index (SVI):

    Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events. SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability. SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.

    In our utilization of these sources, we likely integrated data from both the ACS and the SVI to analyze and understand various socio-economic and demographic indicators at the state, county, and possibly tract levels. This integrated data would have been valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States

    Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variablesCSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

  4. a

    2020 ACS Demographic & Socio-Economic Data Of Oklahoma At Census Tract Level...

    • one-health-data-hub-osu-geog.hub.arcgis.com
    Updated May 22, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    snakka_OSU_GEOG (2024). 2020 ACS Demographic & Socio-Economic Data Of Oklahoma At Census Tract Level [Dataset]. https://one-health-data-hub-osu-geog.hub.arcgis.com/items/cf38f8a63cc649779740f403a6552081
    Explore at:
    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    snakka_OSU_GEOG
    Area covered
    Description

    we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI).American Community Survey (ACS):Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themesEach tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively. In our utilization of these sources, we likely integrated data from both the ACS and the SVI to analyze and understand various socio-economic and demographic indicators at the state, county, and possibly tract levels. This integrated data would have been valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United StatesNote: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2015-2019 ACSEP_PCIEP_PCIPer capita income estimate, 2015-2019 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2015-2019 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2015-2019 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2015-2019 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2015-2019 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

  5. e

    CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI, census tract...

    • coronavirus-resources.esri.com
    • hub.arcgis.com
    Updated Mar 28, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2018). CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI, census tract level [Dataset]. https://coronavirus-resources.esri.com/datasets/cdcarcgis::cdcs-social-vulnerability-index-svi-2016-overall-svi-census-tract-level/about
    Explore at:
    Dataset updated
    Mar 28, 2018
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2016 overall SVI for U.S. census tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the 2016 SVI Full Documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2016 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  6. Historic US census - 1930

    • redivis.com
    application/jsonl +7
    Updated Jan 10, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stanford Center for Population Health Sciences (2020). Historic US census - 1930 [Dataset]. http://doi.org/10.57761/6e5q-rh85
    Explore at:
    application/jsonl, parquet, spss, csv, arrow, stata, avro, sasAvailable download formats
    Dataset updated
    Jan 10, 2020
    Dataset provided by
    Redivis Inc.
    Authors
    Stanford Center for Population Health Sciences
    Time period covered
    Jan 1, 1930 - Dec 31, 1930
    Area covered
    United States
    Description

    Abstract

    The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

    Before Manuscript Submission

    All manuscripts (and other items you'd like to publish) must be submitted to

    phsdatacore@stanford.edu for approval prior to journal submission.

    We will check your cell sizes and citations.

    For more information about how to cite PHS and PHS datasets, please visit:

    https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

    Documentation

    This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

    IPUMS 1930 households: This dataset includes all households from the 1930 US census.

    IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

    IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

    Section 2

    Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

    In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

    The historic US 1930 census data was collected in April 1930. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

    Notes

    • We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

    • Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

    • Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

    • Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

    • Most inconsistent information was not edite

  7. b

    Housing/Transportation Theme - Tracts: United States

    • geo.btaa.org
    Updated Mar 16, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2020). Housing/Transportation Theme - Tracts: United States [Dataset]. https://geo.btaa.org/catalog/cbd68d9887574a10bc89ea4efe2b8087_14
    Explore at:
    Dataset updated
    Mar 16, 2020
    Authors
    Centers for Disease Control and Prevention
    Time period covered
    2020
    Area covered
    United States
    Description

    This feature layer visualizes the 2018 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract15 social factors grouped into four major themesIndex value calculated for each county for the 15 social factors, four major themes, and the overall rankWhat is CDC Social Vulnerability Index?ATSDR's Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and TransportationVariablesFor a detailed description of variable uses, please refer to thefull SVI 2018 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position.Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overallpercentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individualvariable rankings.The overall tract summary ranking variable is RPL_THEMES.Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising eachtheme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.The four summary theme ranking variables are:Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCounties in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Countiesbelow the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overallflag value for each county as the total number of all variable flags.SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2018 Full DocumentationSVI Home PageContact the SVI Coordinator

  8. w

    CDC’s Social Vulnerability Index (SVI) – 2014 overall SVI, census tract...

    • data.wake.gov
    • data-ral.opendata.arcgis.com
    • +2more
    Updated Nov 10, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Raleigh (2017). CDC’s Social Vulnerability Index (SVI) – 2014 overall SVI, census tract level - Wake County [Dataset]. https://data.wake.gov/maps/ral::cdcs-social-vulnerability-index-svi-2014-overall-svi-census-tract-level-wake-county
    Explore at:
    Dataset updated
    Nov 10, 2017
    Dataset authored and provided by
    City of Raleigh
    Area covered
    Description

    This feature layer visualizes the 2014 overall SVI for U.S. census tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full 2014 SVI documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2014 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  9. d

    CDC s Social Vulnerability Index (SVI) 2014 overall SVI, census tract level...

    • datadiscoverystudio.org
    csv, geojson, kml +1
    Updated Jun 6, 2018
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2018). CDC s Social Vulnerability Index (SVI) 2014 overall SVI, census tract level - Wake County. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/0aaa311d95b14f6db5d3f7c2f9a9d1f0/html
    Explore at:
    kml, geojson, zip, csvAvailable download formats
    Dataset updated
    Jun 6, 2018
    Description

    description:

    • This feature layer visualizes the 2014 overall SVI for U.S. census tracts
    • Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract

    • 15 social factors grouped into four major themes

    • Index value calculated for each census tract for the 15 social factors, four major themes, and the overall rank
    What is CDC's Social Vulnerability Index?
    ATSDR s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.

    The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks eachcensus tracton 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:
    • Socioeconomic
    • Housing Composition and Disability
    • Minority Status and Language
    • Housing and Transportation
    Variables
    For a detailed description of variable uses, please refer to thefull 2014 SVI documentation.

    Rankings
    We ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tractrankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position.

    Overall Rankings:
    We summed the sums for each theme, ordered the census tracts, and then calculated overallpercentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individualvariable rankings.

    The overall tract summary ranking variable is RPL_THEMES.

    Theme rankings:
    For each of the four themes, we summed the percentiles for the variables comprising eachtheme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.The four summary theme ranking variables are:
    • Socioeconomic theme - RPL_THEME1
    • Housing Composition and Disability - RPL_THEME2
    • Minority Status & Language - RPL_THEME3
    • Housing & Transportation - RPL_THEME4

    Flags
    Census tractsin the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overallflag value for each census tract as the number of all variable flags.

    ; abstract:
    • This feature layer visualizes the 2014 overall SVI for U.S. census tracts
    • Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract

    • 15 social factors grouped into four major themes

    • Index value calculated for each census tract for the 15 social factors, four major themes, and the overall rank
    What is CDC's Social Vulnerability Index?
    ATSDR s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.

    The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks eachcensus tracton 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:
    • Socioeconomic
    • Housing Composition and Disability
    • Minority Status and Language
    • Housing and Transportation
    Variables
    For a detailed description of variable uses, please refer to thefull 2014 SVI documentation.

    Rankings
    We ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tractrankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position.

    Overall Rankings:
    We summed the sums for each theme, ordered the census tracts, and then calculated overallpercentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individualvariable rankings.

    The overall tract summary ranking variable is RPL_THEMES.

    Theme rankings:
    For each of the four themes, we summed the percentiles for the variables comprising eachtheme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.The four summary theme ranking variables are:
    • Socioeconomic theme - RPL_THEME1
    • Housing Composition and Disability - RPL_THEME2
    • Minority Status & Language - RPL_THEME3
    • Housing & Transportation - RPL_THEME4

    Flags
    Census tractsin the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overallflag value for each census tract as the number of all variable flags.

  10. f

    Data_Sheet_1_The neighborhood context and all-cause mortality among older...

    • figshare.com
    pdf
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Catherine García; Marc A. Garcia; Mary McEniry; Michael Crowe (2023). Data_Sheet_1_The neighborhood context and all-cause mortality among older adults in Puerto Rico.PDF [Dataset]. http://doi.org/10.3389/fpubh.2023.995529.s001
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Catherine García; Marc A. Garcia; Mary McEniry; Michael Crowe
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Puerto Rico
    Description

    BackgroundRecent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data exclude Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between the island and the mainland, it may not be appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the US to Puerto Rico. Thus, we aim to (1) examine the types of neighborhood environments older Puerto Rican adults reside in and (2) explore the association between neighborhood environments and all-cause mortality.MethodsWe linked data from the 2000 US Census to the longitudinal Puerto Rican Elderly Health Conditions Project (PREHCO) with mortality follow-up through 2021 to examine the effects of the baseline neighborhood environment on all-cause mortality among 3,469 participants. Latent profile analysis, a model-based clustering technique, classified Puerto Rican neighborhoods based on 19 census block group indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. The associations between the latent classes and all-cause mortality were assessed using multilevel mixed-effects parametric survival models with a Weibull distribution.ResultsA five-class model was fit on 2,477 census block groups in Puerto Rico with varying patterns of social (dis)advantage. Our results show that older adults residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico were at higher risk of death over the 19-year study period relative to the Urban Low Deprivation cluster, controlling for individual-level covariates.ConclusionsConsidering Puerto Rico's socio-structural reality, we recommend that policymakers, healthcare providers, and leaders across industries to (1) understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and (2) make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico.

  11. a

    2019 ACS Demographic & Socio-Economic Data Of Oklahoma At Zip Code Level

    • one-health-data-hub-osu-geog.hub.arcgis.com
    Updated Apr 7, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    snakka_OSU_GEOG (2024). 2019 ACS Demographic & Socio-Economic Data Of Oklahoma At Zip Code Level [Dataset]. https://one-health-data-hub-osu-geog.hub.arcgis.com/items/ffcbe4548cde4d7d8f6188fad083672c
    Explore at:
    Dataset updated
    Apr 7, 2024
    Dataset authored and provided by
    snakka_OSU_GEOG
    Area covered
    Description

    we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI). American Community Survey (ACS):

    Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States. The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions. It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households. The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.

    CDC/ATSDR Social Vulnerability Index (SVI):

    Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events. SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability. SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.

    In our utilization of these sources, we likely integrated data from both the ACS and the SVI to analyze and understand various socio-economic and demographic indicators at the state, county, and possibly tract levels. This integrated data would have been valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States

    Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

  12. f

    The Portuguese version of the European Deprivation Index: Development and...

    • plos.figshare.com
    docx
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ana Isabel Ribeiro; Ludivine Launay; Elodie Guillaume; Guy Launoy; Henrique Barros (2023). The Portuguese version of the European Deprivation Index: Development and association with all-cause mortality [Dataset]. http://doi.org/10.1371/journal.pone.0208320
    Explore at:
    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ana Isabel Ribeiro; Ludivine Launay; Elodie Guillaume; Guy Launoy; Henrique Barros
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Portugal
    Description

    Socioeconomic inequalities are major health determinants. To monitor and understand them at local level, ecological indexes of socioeconomic deprivation constitute essential tools. In this study, we describe the development of the updated version of the European Deprivation Index for Portuguese small-areas (EDI-PT), describe its spatial distribution and evaluate its association with a general health indicator–all-cause mortality in the period 2009–2012. Using data from the 2011 European Union–Statistics on Income and Living Conditions Survey (EU-SILC), we obtained an indicator of individual deprivation. After identifying variables that were common to both the EU-SILC and the census, we used the indicator of individual deprivation to test if these variables were associated with individual-level deprivation, and to compute weights. Accordingly, eight variables were included. The EDI-PT was produced for the smallest area unit possible (n = 18084 census block groups, mean/area = 584 inhabitants) and resulted from the weighted sum of the eight selected variables. It was then categorized into quintiles (Q1-least deprived to Q5-most deprived). To estimate the association with mortality we fitted Bayesian spatial models. The EDI-PT was unevenly distributed across Portugal–most deprived areas concentrated in the South and in the inner North and Centre of the country, and the least deprived in the coastal North and Centre. The EDI-PT was positively and significantly associated with overall mortality, and this relation followed a rather clear dose-response relation of increasing mortality as deprivation increases (Relative Risk Q2 = 1.012, 95% Credible Interval 0.991–1.033; Q3 = 1.026, 1.004–1.048; Q4 = 1.053, 1.029–1.077; Q5 = 1.068, 1.042–1.095). Summing up, we updated the index of socioeconomic deprivation for Portuguese small-areas, and we showed that the EDI-PT constitutes a sensitive measure to capture health inequalities, since it was consistently associated with a key measure of population health/development, all-cause mortality. We strongly believe this updated version will be widely employed by social and medical researchers and regional planners.

  13. d

    Data from: Social Environment Characteristics of Bogota, Colombia, 2005 &...

    • researchdiscovery.drexel.edu
    Updated Mar 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Alex Quistberg; Olga Lucia Sarmiento; Natalia Hoyos Botero (2025). Social Environment Characteristics of Bogota, Colombia, 2005 & 2018 [Dataset]. https://researchdiscovery.drexel.edu/esploro/outputs/dataset/Social-Environment-Characteristics-of-Bogota-Colombia/991022028136304721
    Explore at:
    Dataset updated
    Mar 9, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Alex Quistberg; Olga Lucia Sarmiento; Natalia Hoyos Botero
    Time period covered
    2025
    Area covered
    Bogotá, Colombia
    Description

    This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains sociodemographic data by city block from census data for Bogota, Colombia in 2005 and 2018 from DANE: National Administrative Department of Statistics (https://geoportal.dane.gov.co/) from the national population and demographic censuses. These data include proportion of individuals by sex, age, educational level, employment status from individual data, proportion of households in poverty or inadequate housing, and proportion of households with utility connections and dwelling quality within a city block. Data cleaning included: (1) Census data were provided at the level of persons, households, dwellings and spatial data (city blocks). To relate non-spatial and spatial data, city block codes (22 characters) were generated by concatenating the department code (2 characters), municipality (3 characters), class (1 character), rural sector (3 characters),rural section (2 characters), population center (3 characters), urban sector (4 characters), urban section (2 characters) and city block (2 characters).These codes were linked to the persons database. (2) The 2005 and 2018 census had some records with missing information on water and sewer connection which were filled with the category "Not reported". Regarding the wall material variable, the 2005 census did not report this information, so for that year this variable was filled in its entirety by the category “Not reported”. That same variable had some missing records in the 2018 census, which were managed in the same way. (3) The 2005 and 2018 census data were merged into one dataset with the following attributes: city block code, census year, water connection, sewer connection and wall durability categories. Poverty and inadequate housing datasets were merged using the city block ID, and only the attributes of interest were kept.The 2005 and 2018 educational level and employment status census data had two additional categories with no clear definition in the census documentation ("Not applicable" and "Not reported"). Those categories were merged into the "Not reported" category. The 2005 and 2018 census data were merged into one dataset with the following attributes: city block code, census year, sex, educational level, and employment status, combining the multiple categories of socioeconomic variables.

  14. f

    Summary statistics of the census variables included in the construction of...

    • plos.figshare.com
    xls
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ana Isabel Ribeiro; Ludivine Launay; Elodie Guillaume; Guy Launoy; Henrique Barros (2023). Summary statistics of the census variables included in the construction of the EDI-PT score (n = 10 562 178 residents, n = 3 997 724 households). [Dataset]. http://doi.org/10.1371/journal.pone.0208320.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ana Isabel Ribeiro; Ludivine Launay; Elodie Guillaume; Guy Launoy; Henrique Barros
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary statistics of the census variables included in the construction of the EDI-PT score (n = 10 562 178 residents, n = 3 997 724 households).

  15. a

    2020 ACS Demographic & Socio-Economic Data Of Oklahoma At Zip Code Level

    • one-health-data-hub-osu-geog.hub.arcgis.com
    Updated May 22, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    snakka_OSU_GEOG (2024). 2020 ACS Demographic & Socio-Economic Data Of Oklahoma At Zip Code Level [Dataset]. https://one-health-data-hub-osu-geog.hub.arcgis.com/items/5175de388f27415caf6087afafa1cc52
    Explore at:
    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    snakka_OSU_GEOG
    Area covered
    Description

    we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI).American Community Survey (ACS):Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.

  16. CDC’s Social Vulnerability Index (SVI) – 2014 socioeconomic SVI, census...

    • hub.arcgis.com
    Updated Mar 24, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2017). CDC’s Social Vulnerability Index (SVI) – 2014 socioeconomic SVI, census tract level [Dataset]. https://hub.arcgis.com/datasets/4759ce4869d3412895b6024c02a71d35
    Explore at:
    Dataset updated
    Mar 24, 2017
    Dataset authored and provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2014 socioeconomic SVI for U.S. census tractsSocioeconomic SVI is one of the four major themes of the overall SVISocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full 2014 SVI documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2014 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  17. a

    CDC’s Social Vulnerability Index (SVI) – 2014 housing/transportation SVI,...

    • hub.arcgis.com
    Updated Mar 24, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2017). CDC’s Social Vulnerability Index (SVI) – 2014 housing/transportation SVI, census tract level [Dataset]. https://hub.arcgis.com/datasets/cdcarcgis::cdcs-social-vulnerability-index-svi-2014-housing-transportation-svi-census-tract-level
    Explore at:
    Dataset updated
    Mar 24, 2017
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2014 housing/transportation SVI for U.S. census tractsHousing/transportation SVI is one of the four major themes of the overall SVISocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full 2014 SVI documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2014 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  18. CDC’s Social Vulnerability Index (SVI) – 2014 minority/language SVI, census...

    • hub.arcgis.com
    Updated Mar 24, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2017). CDC’s Social Vulnerability Index (SVI) – 2014 minority/language SVI, census tract level [Dataset]. https://hub.arcgis.com/datasets/cdcarcgis::cdcs-social-vulnerability-index-svi-2014-minority-language-svi-census-tract-level/about
    Explore at:
    Dataset updated
    Mar 24, 2017
    Dataset authored and provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2014 minority/language SVI for U.S. census tractsMinority/languageSVI is one of the four major themes of the overall SVISocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full 2014 SVI documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2014 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  19. a

    CDC’s Social Vulnerability Index (SVI) – 2010 overall SVI, census tract...

    • hub.arcgis.com
    Updated Mar 28, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2018). CDC’s Social Vulnerability Index (SVI) – 2010 overall SVI, census tract level [Dataset]. https://hub.arcgis.com/maps/cdcarcgis::cdcs-social-vulnerability-index-svi-2010-overall-svi-census-tract-level
    Explore at:
    Dataset updated
    Mar 28, 2018
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2010 overall SVI for U.S. census tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract14 social factors grouped into four major themesIndex value calculated for each census tract for the 14 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 14 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the 2010 SVI Full Documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is R_PL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - R_PL_THEME1Housing Composition and Disability - R_PL_THEME2Minority Status & Language - R_PL_THEME3Housing & Transportation - R_PL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2010 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

  20. a

    CDC’s Social Vulnerability Index (SVI) – 2000 overall SVI, census tract...

    • hub.arcgis.com
    Updated Mar 28, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2018). CDC’s Social Vulnerability Index (SVI) – 2000 overall SVI, census tract level [Dataset]. https://hub.arcgis.com/maps/cdcarcgis::cdcs-social-vulnerability-index-svi-2000-overall-svi-census-tract-level
    Explore at:
    Dataset updated
    Mar 28, 2018
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This feature layer visualizes the 2000 overall SVI for U.S. census tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the 2000 SVI Full Documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is USTP. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - USG1TPHousing Composition and Disability - USG2TPMinority Status & Language - USG3TPHousing & Transportation - USG4TPFlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2000 SVI Full DocumentationSVI Home PageContact the SVI Coordinator

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Bryan, Michael (2023). US Social Vulnerability by Census Block Groups [Dataset]. http://doi.org/10.7910/DVN/ARBHPK

US Social Vulnerability by Census Block Groups

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Nov 8, 2023
Dataset provided by
Harvard Dataverse
Authors
Bryan, Michael
Area covered
United States
Description

blockgroupvulnerability OPPORTUNITY The US Centers for Disease Control (CDC) publishes a set of percentiles that compare US geographies by vulnerability across household, socioeconomic, racial/ethnic and housing themes. These Social Vulnerability Indexes (SVI) were originally intended to to help public health officials and emergency response planners identify communities that will need support around an event. They are generally valuable for any public interest that wants to relate themselves to needy communities by geography. The SVI publication and its basis variables are provided at the Census tract level of geographic detail. The Census' American Community Survey is available down the to the block group level, however. Recasting the SVI methods at this lower level of geography allows it to be tied to thousands of other demographic variables available. Because the SVI relies on ACS variables only available at the tract level, a projection model needs to applied to approximate its results using blockgroup level ACS variables. The blockgroupvulnerability dataset casts a prediction for the CDCs logic for a new contribution to the Open Environments blockgroup series available on Harvard's dataverse platform. DATA The CDC's annual SVI publication starts with 23 simple derivations using 50 ACS Census variables. Next the SVI process ranks census geographies to calculate a rank for each, where Percentile Rank = (Rank-1) / (N-1). The SVI themes are then calculated at the tract level as a percentile rank of a sum of the percentile ranks of the first level ACS derived variables. Finally, the overall ranking is taken as the sum of the theme percentile rankings. The SVI data publication is keyed by geography (7 cols) where ultimately the Census Tract FIPS code is 2 State + 3 County + 4 Tract + 2 Tract Decimals eg, 56043000301 is 56 Wyoming, 043 Washakie County, Tract 3.01 republishes Census demographics called 'adjunct variables' including area, population, households and housing units from the ACS daytime population taken from LandScan 2020 estimates derives 23 SVI variables from 50 ACS 5 Year variables with each having an estimate (E_), estimate precentage (EP_), margin of error (M_), margin percentage (MP_) and flag variable (F_) for those greater than 90% or less than 10% provides the final 4 themes and a composite SVI percentile annually vars = ['ST', 'STATE', 'ST_ABBR', 'STCNTY', 'COUNTY', 'FIPS', 'LOCATION'] +\ ['SNGPNT','LIMENG','DISABL','AGE65','AGE17','NOVEH','MUNIT','MOBILE','GROUPQ','CROWD','UNINSUR','UNEMP','POV150','NOHSDP','HBURD','TWOMORE','OTHERRACE','NHPI','MINRTY','HISP','ASIAN','AIAN','AFAM','NOINT'] +\ ['TOTAL','THEME1','THEME2','THEME3','THEME4'] + \ ['AREA_SQMI', 'TOTPOP', 'DAYPOP', 'HU', 'HH'] knowns = vars + \ # Estimates, the result of calc against ACS vars [('E_'+v) for v in vars] + \ # Flag 0,1 whether this geog is in 90 percentile rank (its vulnerable) [('F_'+v) for v in vars] +\ # Margine of error for ACS calcs [('M_'+v) for v in vars] + \ # Margine of error for ACS calcs, as percentage [('MP_'+v) for v in vars] +\ # Estimates of ACS calcs, as percentage [('EP_'+v) for v in vars] + \ # Estimated percentile ranks [('EPL_'+v) for v in vars] + \ # Sum across var percentile ranks [('SPL_'+v) for v in vars]+ \ # Percentile rank of the sum of percentile ranks [('RPL_'+v) for v in vars] [c for c in svitract.columns if c not in knowns] The SVI themes range over [0,1] but the CDC uses -999 as an NA value; this is set for ~800 or 1% of tracts which have no total poulation. The themes are numbered: Socioeconomic Status – RPL_THEME1 Household Characteristics – RPL_THEME2 Racial & Ethnic Minority Status – RPL_THEME3 Housing Type & Transportation – RPL_THEME4 The themes with their variables and ACS sources are as follows: Unlike Census data, the CDC ranks Puerto Rico and Tribal tracts separately from the US otherwise. Theme SVI Variable ACS Table ACS Variables Socioeconomic E_UNINSUR S2701 S2701_C04_001E Socioeconomic E_UNEMP DP03 DP03_0005E Socioeconomic E_POV150 S1701 S1701_C01_040E Socioeconomic E_NOHSDP B06009 B06009_002E Socioeconomic E_HBURD S2503 S2503_C01_028E + S2503_C01_032E + S2503_C01_036E + S2503_C01_040E Household E_SNGPNT B11012 B11012_010E + B11012_015E Household E_LIMENG B16005 B16005_007E + B16005_008E + B16005_012E + B16005_013E + B16005_017E + B16005_018E + B16005_022E + B16005_023E + B16005_029E + B16005_030E + B16005_034E + B16005_035E + B16005_039E + B16005_040E + B16005_044E + B16005_045E Household E_DISABL DP02 DP02_0072E Household E_AGE65 S0101 S0101_C01_030E Household E_AGE17 B09001 B09001_001E Racial & Ethnic E_TWOMORE DP05 DP05_0083E Racial & Ethnic E_OTHERRACE DP05 DP05_0082E Racial & Ethnic E_NHPI DP05 DP05_0081E Racial & Ethnic E_MINRTY DP05 DP05_0071E + DP05_0078E + DP05_0079E + DP05_0080E + DP05_0081E + DP05_0082E + ... Visit https://dataone.org/datasets/sha256%3A3edd5defce2f25c7501953ca3e77c4f15a8c71251352373a328794f961755c1c for complete metadata about this dataset.

Search
Clear search
Close search
Google apps
Main menu