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