20240611 15431100 1.0 FALSE RSAP_USDOT_Underseved_Communities_Proj2 002 Projected GCS_North_American_1983_HARN Linear Unit: Foot (0.304800) NAD_1983_HARN_StatePlane_Arizona_Central_FIPS_0202_Feet_Intl <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><WKT>PROJCS[&quot;NAD_1983_HARN_StatePlane_Arizona_Central_FIPS_0202_Feet_Intl&quot;,GEOGCS[&quot;GCS_North_American_1983_HARN&quot;,DATUM[&quot;D_North_American_1983_HARN&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,700000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-111.9166666666667],PARAMETER[&quot;Scale_Factor&quot;,0.9999],PARAMETER[&quot;Latitude_Of_Origin&quot;,31.0],UNIT[&quot;Foot&quot;,0.3048],AUTHORITY[&quot;EPSG&quot;,2868]]</WKT><XOrigin>-17746700</XOrigin><YOrigin>-44067300</YOrigin><XYScale>3048</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808398950131233</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2868</WKID><LatestWKID>2868</LatestWKID></ProjectedCoordinateSystem> 150000000 5000 ItemDescription 20240611 15534000 20240611 15534000 This layer assesses and identifies communities that are disadvantaged according to updated Justice40 Initiative criteria in the U.S. and its territories. Census tracts that meet the Version 1.0 criteria are shaded in semi-transparent blue colors to work with a variety of basemaps. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This layer assesses and identifies communities that are disadvantaged according to updated </SPAN><A href="https://screeningtool.geoplatform.gov/en/methodology" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">Justice40 Initiative criteria</SPAN></A><SPAN>. Census tracts in the U.S. and its territories that meet the Version 1.0 criteria are shaded in semi-transparent blue colors to work with a variety of basemaps. </SPAN><A href="https://esri.maps.arcgis.com/home/item.html?id=e9fe6175efb74613bfd5d4b33b3fa476" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">See this web map</SPAN></A><SPAN> for use in your dashboards, story maps, and apps.</SPAN></P><P><SPAN>Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the </SPAN><A href="https://screeningtool.geoplatform.gov/en/methodology" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">source data</SPAN></A><SPAN> downloaded November 22, 2022.</SPAN></P><P><SPAN>If you have been using a previous version of the Justice40 data, please know that this Version 1.0 differs in many ways. See the updated </SPAN><A href="https://screeningtool.geoplatform.gov/en/methodology" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">Justice40 Initiative criteria</SPAN></A><SPAN> for current specifics. </SPAN></P><P><SPAN>Use this layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this</SPAN><A href="https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/local-government/justice40/" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt"> blog post </SPAN></A><SPAN>for more information.</SPAN></P><P><SPAN STYLE="font-weight:bold;">From the source</SPAN><SPAN>:</SPAN></P><P><SPAN STYLE="font-style:italic;">This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:</SPAN></P><UL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN STYLE="font-style:italic;">If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, or</SPAN></P></LI><LI><P><SPAN STYLE="font-style:italic;">If they are on land within the boundaries of Federally Recognized Tribes</SPAN></P></LI></UL><P><SPAN STYLE="font-style:italic;font-weight:bold;">Categories of Burdens</SPAN></P><P><SPAN STYLE="font-style:italic;">The tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.</SPAN></P><P><SPAN STYLE="font-style:italic;">In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.</SPAN></P><P><SPAN STYLE="font-style:italic;">Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."</SPAN></P><P><SPAN>Source: https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/usa_november_2022/FeatureServer</SPAN></P><P><SPAN STYLE="font-weight:bold;">Browse the Data</SPAN></P><P><SPAN>View the Data tab in the top right of this page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.</SPAN></P><P><SPAN>Symbology updated 2/19/2023 to show additional tracts whose overlap with tribal lands is greater than 0% but less than 1%, to be designated as "Partially Disadvantaged" alongside tracts whose overlap with tribal lands is 1% or more. </SPAN></P></DIV></DIV></DIV> Council on Environmental Quality, Esri USDOT Underserved Community Social Equity ACS American Community Survey FEMA National Risk Index NRI Energy LEAD EPA EJSCREEN Office of Air and Radiation OAR EPA National Air Toxics Assessment NATA Department of Transportation DOT Comprehensive Housing Affordability Strategy CHAS RCRA CERCLIS RMP Risk-Screening Environmental Indicators RSEI Justice 40 Justice40 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">Purpose</SPAN></P><P><SPAN>The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: </SPAN><A href="https://screeningtool.geoplatform.gov/en/" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">Climate and Economic Justice Screening tool</SPAN></A></P><P><SPAN STYLE="font-style:italic;">"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: </SPAN><A href="https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/27/executive-order-on-tackling-the-climate-crisis-at-home-and-abroad/" STYLE="text-decoration:underline;"><SPAN STYLE="font-style:italic;font-size:10pt">Executive Order on Tackling the Climate Crisis at Home and Abroad</SPAN></A></P><P><SPAN STYLE="font-weight:bold;">Use of this Data</SPAN></P><P><SPAN STYLE="font-style:italic;">"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: </SPAN><A href="https://www.whitehouse.gov/omb/briefing-room/2021/07/20/the-path-to-achieving-justice40/" STYLE="text-decoration:underline;"><SPAN STYLE="font-style:italic;font-size:10pt">The Path to Achieving Justice 40</SPAN></A></P><P><SPAN>The layer has some transparency applied to allow it to work sufficiently well on top of many basemaps. For optimum map display where streets and labels are clearly shown </SPAN><SPAN STYLE="font-style:italic;font-weight:bold;">on top</SPAN><SPAN> of this layer, try one of the </SPAN><A href="https://livingatlas.arcgis.com/en/browse/?q=human%20geography" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">Human Geography basemaps</SPAN></A><SPAN> and set transparency to 0%, as is done in </SPAN><A href="https://www.arcgis.com/home/item.html?id=e9fe6175efb74613bfd5d4b33b3fa476" STYLE="text-decoration:underline;"><SPAN STYLE="font-size:10pt">this example web map</SPAN></A><SPAN>.</SPAN></P></DIV></DIV></DIV> Esri ArcGIS 13.3.0.52636 20240611 /9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDAREA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA 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FALSE 0 TRUE FALSE RSAP_USDOT_Underseved_Communities_Proj2 Feature Class 0 OBJECTID ObjectID OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. GEOID10 2010 FIPS Code String 80 0 0 SF State/Territory String 80 0 0 CF County Name String 80 0 0 DF_PFS Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile) Double 8 38 8 AF_PFS Current asthma among adults aged greater than or equal to 18 years (percentile) Double 8 38 8 HDF_PFS Coronary heart disease among adults aged greater than or equal to 18 years (percentile) Double 8 38 8 DSF_PFS Diesel particulate matter exposure (percentile) Double 8 38 8 EBF_PFS Energy burden (percentile) Double 8 38 8 EALR_PFS Expected agricultural loss rate (Natural Hazards Risk Index) (percentile) Double 8 38 8 EBLR_PFS Expected building loss rate (Natural Hazards Risk Index) (percentile) Double 8 38 8 EPLR_PFS Expected population loss rate (Natural Hazards Risk Index) (percentile) Double 8 38 8 HBF_PFS Housing burden (percent) (percentile) Double 8 38 8 LLEF_PFS Low life expectancy (percentile) Double 8 38 8 LIF_PFS Linguistic isolation (percent) (percentile) Double 8 38 8 LMI_PFS Low median household income as a percent of area median income (percentile) Double 8 38 8 PM25F_PFS PM2.5 in the air (percentile) Double 8 38 8 HSEF Percent individuals age 25 or over with less than high school degree Double 8 38 8 P100_PFS Percent of individuals < 100% Federal Poverty Line (percentile) Double 8 38 8 P200_I_PFS Percent of individuals below 200% Federal Poverty Line (percentile) Double 8 38 8 AJDLI_ET Meets the less stringent low income criterion for the adjacency index? Integer 4 10 0 LPF_PFS Percent pre-1960s housing (lead paint indicator) (percentile) Double 8 38 8 KP_PFS Share of homes with no kitchen or indoor plumbing (percent) (percentile) Double 8 38 8 NPL_PFS Proximity to NPL sites (percentile) Double 8 38 8 RMP_PFS Proximity to Risk Management Plan (RMP) facilities (percentile) Double 8 38 8 TSDF_PFS Proximity to hazardous waste sites (percentile) Double 8 38 8 TPF Total population Double 8 38 8 TF_PFS Traffic proximity and volume (percentile) Double 8 38 8 UF_PFS Unemployment (percent) (percentile) Double 8 38 8 WF_PFS Wastewater discharge (percentile) Double 8 38 8 UST_PFS Leaky underground storage tanks (percentile) Double 8 38 8 N_WTR Water and Wastewater Disadvantaged Integer 4 10 0 N_WKFC Workforce Development Disadvantaged Integer 4 10 0 N_CLT Climate Change Disadvantaged Integer 4 10 0 N_ENY Energy Disadvantaged Integer 4 10 0 N_TRN Transportation Disadvantaged Integer 4 10 0 N_HSG Housing Disadvantaged Integer 4 10 0 N_PLN Legacy Pollution Disadvantaged Integer 4 10 0 N_HLTH Health Disadvantaged Integer 4 10 0 SN_C Identified as disadvantaged Integer 4 10 0 SN_T Identified as disadvantaged due to tribal overlap String 80 0 0 DLI Greater than or equal to the 90th percentile for diabetes, is low income, and has a low percent of higher ed students? Integer 4 10 0 ALI Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students? Integer 4 10 0 PLHSE Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students? Integer 4 10 0 LMILHSE Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students? Integer 4 10 0 ULHSE Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students? Integer 4 10 0 EPL_ET Greater than or equal to the 90th percentile for expected population loss Integer 4 10 0 EAL_ET Greater than or equal to the 90th percentile for expected agricultural loss Integer 4 10 0 EBL_ET Greater than or equal to the 90th percentile for expected building loss Integer 4 10 0 EB_ET Greater than or equal to the 90th percentile for energy burden Integer 4 10 0 PM25_ET Greater than or equal to the 90th percentile for PM 2.5 exposure Integer 4 10 0 DS_ET Greater than or equal to the 90th percentile for diesel particulate matter Integer 4 10 0 TP_ET Greater than or equal to the 90th percentile for traffic proximity Integer 4 10 0 LPP_ET Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile Integer 4 10 0 HRS_ET Tract-level redlining score meets or exceeds 3.25 String 80 0 0 KP_ET Greater than or equal to the 90th percentile for share of homes without indoor plumbing or a kitchen Integer 4 10 0 HB_ET Greater than or equal to the 90th percentile for housing burden Integer 4 10 0 RMP_ET Greater than or equal to the 90th percentile for Risk Management Plan (RMP) proximity Integer 4 10 0 NPL_ET Greater than or equal to the 90th percentile for NPL (superfund sites) proximity Integer 4 10 0 TSDF_ET Greater than or equal to the 90th percentile for proximity to hazardous waste sites Integer 4 10 0 WD_ET Greater than or equal to the 90th percentile for wastewater discharge Integer 4 10 0 UST_ET Greater than or equal to the 90th percentile for leaky underwater storage tanks Integer 4 10 0 DB_ET Greater than or equal to the 90th percentile for diabetes Integer 4 10 0 A_ET Greater than or equal to the 90th percentile for asthma Integer 4 10 0 HD_ET Greater than or equal to the 90th percentile for heart disease Integer 4 10 0 LLE_ET Greater than or equal to the 90th percentile for low life expectancy Integer 4 10 0 UN_ET Greater than or equal to the 90th percentile for unemployment Integer 4 10 0 LISO_ET Greater than or equal to the 90th percentile for households in linguistic isolation Integer 4 10 0 POV_ET Greater than or equal to the 90th percentile for households at or below 100% federal poverty level Integer 4 10 0 LMI_ET Greater than or equal to the 90th percentile for low median household income as a percent of area median income Integer 4 10 0 IA_LMI_ET Low median household income as a percent of territory median income in 2009 exceeds 90th percentile Integer 4 10 0 IA_UN_ET Unemployment (percent) in 2009 exceeds 90th percentile Integer 4 10 0 IA_POV_ET Percentage households below 100% of federal poverty line in 2009 exceeds 90th percentile Integer 4 10 0 TC Total threshold criteria exceeded Double 8 38 8 CC Total categories exceeded Double 8 38 8 IAULHSE Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)? Integer 4 10 0 IAPLHSE Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)? Integer 4 10 0 IALMILHSE Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)? Integer 4 10 0 IALMIL_76 Low median household income as a percent of territory median income in 2009 (percentile) Double 8 38 8 IAPLHS_77 Percentage households below 100% of federal poverty line in 2009 for island areas (percentile) Double 8 38 8 IAULHS_78 Unemployment (percent) in 2009 for island areas (percentile) Double 8 38 8 LHE Low high school education and low percent of higher ed students Integer 4 10 0 IALHE Low high school education in 2009 (island areas) Integer 4 10 0 IAHSEF Percent individuals age 25 or over with less than high school degree in 2009 Double 8 38 8 N_CLT_EOMI At least one climate threshold exceeded Integer 4 10 0 N_ENY_EOMI At least one energy threshold exceeded Integer 4 10 0 N_TRN_EOMI At least one traffic threshold exceeded Integer 4 10 0 N_HSG_EOMI At least one housing threshold exceeded Integer 4 10 0 N_PLN_EOMI At least one pollution threshold exceeded Integer 4 10 0 N_WTR_EOMI At least one water threshold exceeded Integer 4 10 0 N_HLTH_88 At least one health threshold exceeded Integer 4 10 0 N_WKFC_89 At least one workforce threshold exceeded Integer 4 10 0 FPL200S Exceeds Federal Poverty Level 200 threshold Integer 4 10 0 N_WKFC_91 Both workforce socioeconomic indicators exceeded Integer 4 10 0 TD_ET Greater than or equal to the 90th percentile for DOT travel barriers Integer 4 10 0 TD_PFS DOT Travel Barriers Score (percentile) Double 8 38 8 FLD_PFS Share of properties at risk of flood in 30 years (percentile) Double 8 38 8 WFR_PFS Share of properties at risk of fire in 30 years (percentile) Double 8 38 8 FLD_ET Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years Integer 4 10 0 WFR_ET Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years Integer 4 10 0 ADJ_ET Is the tract surrounded by disadvantaged communities? Integer 4 10 0 IS_PFS Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile) Double 8 38 8 IS_ET Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income? Integer 4 10 0 AML_ET Is there at least one abandoned mine in this census tract, where missing data is treated as False? Integer 4 10 0 FUDS_RAW Is there at least one Formerly Used Defense Site (FUDS) in the tract? String 80 0 0 FUDS_ET Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False? Integer 4 10 0 IMP_FLG Income data has been estimated (imputed) based on neighbor income String 80 0 0 DM_B Percent Black or African American Double 8 38 8 DM_AI Percent American Indian / Alaska Native Double 8 38 8 DM_A Percent Asian Double 8 38 8 DM_HI Percent Native Hawaiian or Pacific Double 8 38 8 DM_T Percent two or more races Double 8 38 8 DM_W Percent White Double 8 38 8 DM_H Percent Hispanic or Latino Double 8 38 8 DM_O Percent other races Double 8 38 8 AGE_10 Percent age under 10 Double 8 38 8 AGE_MIDDLE Percent age 10 to 64 Double 8 38 8 AGE_OLD Percent age over 64 Double 8 38 8 TA_COU_116 Number of Tribal areas within Census tract for Alaska Double 8 38 8 TA_COUNT_C Number of Tribal areas within Census tract Double 8 38 8 TA_PERC Percent of the Census tract that is within Tribal areas Double 8 38 8 TA_PERC_FE Percent of the Census tract that is within Tribal areas, for display Double 8 38 8 UI_EXP Methodology type used String 80 0 0 THRHLD Total thresholds possible Double 8 38 8 GlobalID GlobalID GlobalID 38 0 0 Shape Shape Geometry 8 0 0 Feature geometry. Esri Coordinates defining the features. Shape.STArea() Shape.STArea() Double 0 0 0 Shape.STLength() Shape.STLength() Double 0 0 0