5 datasets found
  1. o

    National Getis-Ord Gi* statistics for select populations; 1990-2019

    • openicpsr.org
    delimited
    Updated May 16, 2022
    + more versions
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    Cyanna McGowan; Antonio Nanni; Kelsey Rydland; Ember McCoy; Haley Mullen; Kiarri Kershaw (2022). National Getis-Ord Gi* statistics for select populations; 1990-2019 [Dataset]. http://doi.org/10.3886/E170541V2
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    May 16, 2022
    Dataset provided by
    Northwestern University
    University of Michigan
    Northwestern University. Feinberg School of Medicine
    Authors
    Cyanna McGowan; Antonio Nanni; Kelsey Rydland; Ember McCoy; Haley Mullen; Kiarri Kershaw
    License

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

    Area covered
    Census tract
    Description

    Local Getis-Ord Gi* statistics were calculated as a measure of residential racial segregation. Measures were calculated at the census tract level on the proportion of non-Hispanic White, non-Hispanic Black, non-Hispanic Asian [and Pacific Islander for 1990 and 2000 census], and Hispanic persons per census tract. Gi* statistics are Z-scores that compare the proportion of the population in the focal tract and its neighboring tracts, to the average proportion of a larger geographic unit. For the majority of tracts, the larger geographic unit was the Core-Based Statistical Area (CBSA) these tracts belonged to, and for the minority of tracts that fell outside the boundaries of a CBSA, the County was used as the larger unit.Data for the measures were obtained from the IPUMS National Historical Geographic Information System (NHGIS) data finder. Data were downloaded for the 1990 and 2000 census, and the 2006-2009, 2010-2014, and 2015-2019 5-year American Community Survey (ACS) estimates. Geographically standardized time series tables were used for 1990 and 2000 census data. All other ACS data were standardized to 2010 census tract boundaries.G*statistics were calculated using both Rook and Queen conceptualization of spatial relationships. With Rook contiguity, neighbors are determined by those that share a common edge only, while Queen contiguity neighbors are those that share both an edge or a "corner" (common vertex). See detailed documentation for further details.

  2. f

    The unequal vulnerability of communities of color to wildfire

    • plos.figshare.com
    pdf
    Updated Jun 1, 2023
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    Ian P. Davies; Ryan D. Haugo; James C. Robertson; Phillip S. Levin (2023). The unequal vulnerability of communities of color to wildfire [Dataset]. http://doi.org/10.1371/journal.pone.0205825
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ian P. Davies; Ryan D. Haugo; James C. Robertson; Phillip S. Levin
    License

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

    Description

    Globally, environmental disasters impact billions of people and cost trillions of dollars in damage, and their impacts are often felt most acutely by minority and poor communities. Wildfires in the U.S. have similarly outsized impacts on vulnerable communities, though the ethnic and geographic distribution of those communities may be different than for other hazards. Here, we develop a social-ecological approach for characterizing fire vulnerability and apply it to >70,000 census tracts across the United States. Our approach incorporates both the wildfire potential of a landscape and socioeconomic attributes of overlying communities. We find that over 29 million Americans live with significant potential for extreme wildfires, a majority of whom are white and socioeconomically secure. Within this segment, however, are 12 million socially vulnerable Americans for whom a wildfire event could be devastating. Additionally, wildfire vulnerability is spread unequally across race and ethnicity, with census tracts that were majority Black, Hispanic or Native American experiencing ca. 50% greater vulnerability to wildfire compared to other census tracts. Embracing a social-ecological perspective of fire-prone landscapes allows for the identification of areas that are poorly equipped to respond to wildfires.

  3. a

    Composite Population Vulnerability

    • data-lahub.opendata.arcgis.com
    • equity-lacounty.hub.arcgis.com
    • +1more
    Updated Dec 22, 2022
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    County of Los Angeles (2022). Composite Population Vulnerability [Dataset]. https://data-lahub.opendata.arcgis.com/datasets/lacounty::composite-population-vulnerability
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    Dataset updated
    Dec 22, 2022
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Attribute names and descriptions are as follows:

    • STATE - Census State Number

    • COUNTY - Census County Number

    • TRACT - Census Tract Number

    • plltn_p - Clean Environment domain score (average of Z-scores of Diesel PM, Ozone, PM 2.5, Safe Drinking Water), statewide percentile ranking

    • atmbl_p - Percentage of households with access to an automobile, statewide percentile ranking

    • cmmt_pc - Percentage of workers, 16 years and older, who commute to work by transit, walking, or cycling, statewide percentile ranking

    • emplyd_ - Percentage of population aged 20-64 who are employed, statewide percentile ranking

    • abvpvr_ - Percent of the population with an income exceeding 200% of federal poverty level, statewide percentile ranking

    • prkccs_ - Percentage of the population living within a half-mile of a park, beach, or open space greater than 1 acre, statewide percentile ranking

    • trcnpy_ - Population-weighted percentage of the census tract area with tree canopy, statewide percentile ranking

    • twprnt_ - Percentage of family households with children under 18 with two parents, statewide percentile ranking

    • ozn_pct - Mean of summer months of the daily maximum 8-hour ozone concentration (ppm) averaged over three years (2012 to 2014), statewide percentile ranking

    • pm25_pc - Annual mean concentration of PM2.5 (average of quarterly means, μg/m3), over three years (2012 to 2014), statewide percentile ranking

    • dslpm_p - Spatial distribution of gridded diesel PM emissions from on-road and non-road sources for a 2012 summer day in July, statewide percentile ranking

    • h20cnt_ - Cal EnviroScreen 3.0 drinking water contaminant index for selected contaminants, statewide percentile ranking

    • wht_pct - Percent of Whites in the total population (not a percentile)

    • heatdays - Projected annual number of extreme heat days at 2070, (not a percentile)

    • impervsu_5 - Percent impervious surface cover, statewide percentile ranking

    • transita_5 - Percent of population residing within ½ mile of a major transit stop, statewide percentile ranking

    • uhii_pctil - Urban heat island index: sum of 182 day temp. differences (degree-hr) between urban and rural reference, statewide percentile ranking

    • traffic_1 - Sum of traffic volumes adjusted by road segment length divided by total road length within 150 meters of the census tract boundary, statewide percentile ranking

    • children_1 - Percent of population under 5 years of age, statewide percentile ranking

    • elders_p_1 - Percent of population 65 years of age and older, statewide percentile ranking

    • englishs_5 - Percentage of households where at least one person 14 years and older speaks English very well, statewide percentile ranking

    • pedshurt_1 - 5-year (2006-2010) annual average rate of severe and fatal pedestrian injuries per 100,000 population, statewide percentile ranking

    • leb_pctile - Life expectancy at birth in 2010, statewide percentile ranking

    • abvpvty_s - Poverty, lowest 25th percentile statewide

    • employ_s - Unemployed, lowest 25th percentile statewide

    • twoprnt_s - Two Parent Households, lowest 25th percentile statewide

    • chldrn_s - Young Children, lowest 25th percentile statewide

    • elderly_s - Elderly, lowest 25th percentile statewide

    • englishs_s - Non-English Speaking, lowest 25th percentile statewide

    • majorwht_s - Majority Minority Population, over 50 percent of population non-white

    • D1_Social - Social barriers to accessing outdoor opportunities, combined indicators score

    • actvcom_s - Limited Active Commuting, lowest 25th percentile statewide

    • autoacc_s - Limited Automobile Access, lowest 25th percentile statewide

    • transita_s - Limited Public Transit Access, lowest 25th percentile statewide

    • trafficd_s - Traffic Density, lowest 25th percentile statewide

    • pedinjry_s - Pedestrian Injuries, lowest 25th percentile statewide

    • D2_Transp - Transportation barriers to accessing outdoor opportunities, combined indicators score

    • expbirth_s - Life Expectancy at Birth, lowest 25th percentile statewide

    • clneviro_s - Pollution, lowest 25th percentile statewide

    • D3_Health - Health Vulnerability, combined indicators score

    • parkacc_s - Limited Park Access, lowest 25th percentile statewide

    • treecan_s - Limited Tree Canopy, lowest 25th percentile statewide

    • impsurf_s - Impervious Surface, lowest 25th percentile statewide

    • exheat_s - Excessive Heat Days, highest of four quantiles

    • hisland_s - Urban Heat Island Index, lowest 25th percentile statewide

    • D4_Environ Environmental Vulnerability, combined indicators score

    • D1_Multi Multiple indicators (2 or more) with social barriers to accessessing outdoor opportunities

    • D2_Multi Multiple indicators (2 or more) with transportation barriers to accessessing outdoor opportunities

    • D3_Multi Multiple indicators (1 or more) with health vulnerability

    • D4_Multi Multiple indicators (2 or more) with environmental vulnerability

    • Comp_DIM - Multiple Indicators, combined dimensions score

    • D1_Major - Majority indicators (4 or more) with social barriers to accessessing outdoor opportunities

    • D2_Major - Majority indicators (3 or more) with transportation barriers to accessessing outdoor opportunities

    • D3_Major - Majority indicators (1 or more) with health vulnerability

    • D4_Major - Majority indicators (3 or more) with environmental vulnerability

    • Comp_DIM_2 - Majority Indicators, combined dimensions score


    DISCLAIMER: The data herein is for informational purposes, and may not have been prepared for or be suitable for legal, engineering, or surveying intents. The County of Los Angeles reserves the right to change, restrict, or discontinue access at any time. All users of the maps and data presented on https://lacounty.maps.arcgis.com or deriving from any LA County REST URLs agree to the "Terms of Use" outlined on the County of LA Enterprise GIS (eGIS) Hub (https://egis-lacounty.hub.arcgis.com/pages/terms-of-use).
  4. Non-White Population in the US (Current ACS)

    • gis-for-racialequity.hub.arcgis.com
    Updated Jul 2, 2021
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    Urban Observatory by Esri (2021). Non-White Population in the US (Current ACS) [Dataset]. https://gis-for-racialequity.hub.arcgis.com/maps/bd59d1d55f064d1b815997f4b6c7735f
    Explore at:
    Dataset updated
    Jul 2, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

  5. a

    dallas tract 2020

    • hub.arcgis.com
    • egisdata-dallasgis.hub.arcgis.com
    Updated Jun 30, 2022
    + more versions
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    City of Dallas GIS Services (2022). dallas tract 2020 [Dataset]. https://hub.arcgis.com/maps/DallasGIS::dallas-tract-2020
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    Dataset updated
    Jun 30, 2022
    Dataset authored and provided by
    City of Dallas GIS Services
    Area covered
    Description

    Data is released by the US Census Bureau following a Decennial count that is used in support of compliance with Public Law 94-171 and the Voting Rights Act (VRA). Public Law (P.L.) 94-171, enacted in 1975, directs the Census Bureau to make special preparations to provide the redistricting data needed by the fifty states. Within a year following Census Day, the Census Bureau must send the data agreed upon to redraw districts for the state legislature to each state's governor and majority and minority legislative leaders and those state officials legally responsible for statewide redistricting such as commission chairs. Process Doc:pl94171_data_loading_and_processing.docx (sharepoint.com)

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Cyanna McGowan; Antonio Nanni; Kelsey Rydland; Ember McCoy; Haley Mullen; Kiarri Kershaw (2022). National Getis-Ord Gi* statistics for select populations; 1990-2019 [Dataset]. http://doi.org/10.3886/E170541V2

National Getis-Ord Gi* statistics for select populations; 1990-2019

Explore at:
delimitedAvailable download formats
Dataset updated
May 16, 2022
Dataset provided by
Northwestern University
University of Michigan
Northwestern University. Feinberg School of Medicine
Authors
Cyanna McGowan; Antonio Nanni; Kelsey Rydland; Ember McCoy; Haley Mullen; Kiarri Kershaw
License

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

Area covered
Census tract
Description

Local Getis-Ord Gi* statistics were calculated as a measure of residential racial segregation. Measures were calculated at the census tract level on the proportion of non-Hispanic White, non-Hispanic Black, non-Hispanic Asian [and Pacific Islander for 1990 and 2000 census], and Hispanic persons per census tract. Gi* statistics are Z-scores that compare the proportion of the population in the focal tract and its neighboring tracts, to the average proportion of a larger geographic unit. For the majority of tracts, the larger geographic unit was the Core-Based Statistical Area (CBSA) these tracts belonged to, and for the minority of tracts that fell outside the boundaries of a CBSA, the County was used as the larger unit.Data for the measures were obtained from the IPUMS National Historical Geographic Information System (NHGIS) data finder. Data were downloaded for the 1990 and 2000 census, and the 2006-2009, 2010-2014, and 2015-2019 5-year American Community Survey (ACS) estimates. Geographically standardized time series tables were used for 1990 and 2000 census data. All other ACS data were standardized to 2010 census tract boundaries.G*statistics were calculated using both Rook and Queen conceptualization of spatial relationships. With Rook contiguity, neighbors are determined by those that share a common edge only, while Queen contiguity neighbors are those that share both an edge or a "corner" (common vertex). See detailed documentation for further details.

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