The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

Indicator

Fields

Source

Health Professional Shortage Areas (HPSA) for Primary Care

hpsa_primary_pct hpsa_primary_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

Health Professional Shortage Areas (HPSA) for Mental Health

hpsa_mental_pct hpsa_mental_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

Concentrated Disadvantage

cd_pct cd_black_pct

LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

Firearm Dealers

firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

LA County DPH Office of Violence Prevention (OVP)

High and Very High Park Need Areas

parks_need_pct parks_need_black_pct

LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

High Quality Transit Areas

hqta_pct hqta_black_pct

SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

High Walkability Areas

walk_total_pct walk_black_pct

EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

High Poverty and High Segregation Areas

highpovseg_total_pct highpovseg_black_pct

CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

LA County Arts Investments

arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

Strong Start (areas with at least 9 Strong Start indicators)

strongstart_total_pct strongstart_black_pct

CA Strong Start Index https://strongstartindex.org/map

For more information about the purpose of this data, please contact CEO-ARDI.

For more information about the configuration of this data, please contact ISD-Enterprise GIS.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS.

Using data from the 2009-13 ACS 5 Year Estimates at the Census Designated Place level (CDP), calculate total percentage of minority population and total percentage of households in poverty for each CDPAlso using census tract data from the 2009-13 ACS 5 Year Estimates, tabulate percentage of minority population and total percentage of households in poverty for each City of Los Angeles Community Planning Area (CPAs)Intersect CPAs with Census Tracts and tabulate new totals for partial CPA/Census Tracts based on spatial interpolationSum total Poverty, households, minority, and population values for each CPAMerge CDPs and City of Los Angeles Community Planning Areas to create a single “Place” file for the entire SCAG region. Remove the City of Los Angeles CDP from layer. Tabulate % of households in poverty and % of minority population for each “Place”Using ranked sorting, select the places that are in the upper third in the SCAG region for both % of households in poverty (x > 0.169156) and% minority (x > 0.768549)Identify those places and export to new shapefile – “Communities_of_Concern”Union “Communities_of_Concern” shapefile with Tier2 TAZ file and tabulate % of each tract that falls in “Communities_of_Concern”Calculate total square meters in Tier2 TAZ shapefileUnion shapefile with “Communities_of_Concern”Tabulate new square meters in Tier 2 TAZ shapefileExport attribute table to DBFLoad DBF in excel and use pivot tables to tabulate total acreage by TAZ only for tracts that intersect with “Communities_of_Concern”. Create new DBF with results and load into ArcMapJoin new DBF with Tier2 TAZ shapefile and calculate % of TAZ that falls in “Communities_of_Concern” only for the records that join. All other TAZs remain 0%, if they do not intersect.

Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.

Demographic and health data for all counties in the USA.

For all community profiles: https://experience.arcgis.com/experience/f6e41347adf541f8a77cc6f3ae979df9

The map illustrates the 2019 emissions for stationary sources in the AB 617 South Los Angeles (LA) community. Stationary draft emissions are composed of reported 2018 stationary point sources and 2019 aggregated stationary sources. The 2018 stationary point emission data is based on the California Emission Inventory Development and Reporting System (CEIDARS) where Air Districts report annual emissions for facilities. Draft 2019 stationary aggregate emissions in tons per year are based on the latest CARB State Implementation Plan emission inventory with a base year of 2017 (CEPAM 2019SIP v1.01), and are projected to 2019 using the most up-to-date growth and control factors at the regional scale. Stationary aggregate source emissions are distributed to more specific locations using the latest spatial surrogates resulting in high-resolution 1x1km emission grids for the community. Examples of spatial surrogates include population, housing, employment, land cover type, etc.

The Southern California Bight (SCB; Figure I-1), an open embayment in the coast between Point Conception and Cape Colnett (south of Ensenada), Baja California, is an important and unique ecological resource. The SCB is a transitional area that is influenced by currents from cold, temperate ocean waters from the north and warm, tropical waters from the south. In addition, the SCB has a complex topography, with offshore islands, submarine canyons, ridges and basins, that provides a variety of habitats. The mixing of currents and the diverse habitats in the SCB allow for the coexistence of a broad spectrum of species, including more than 500 species of fish and 1,500 species of invertebrates. The SCB is a major migration route, with marine bird and mammal populations ranking among the most diverse in north temperate waters. The coastal zone of the SCB is a substantial economic resource. Los Angeles/Long Beach Harbor is the largest commercial port in the United States, and San Diego Harbor is home to one of the largest US Naval facilities in the country. More than 100 million people visit southern California beaches and coastal areas annually, bringing an estimated $9B into the economy. Recreational activities include diving, swimming, surfing, and boating, with about 40,000 pleasure boats docked in 13 coastal marinas within the region (NRC 1990). Recreational fishing brings in more than $500M per year. The SCB is one of the most densely populated coastal regions in the country, which creates stress upon its marine environment. Nearly 20 million people inhabit coastal Southern California, a number which is expected to increase another 20% by 2010 (NRC 1990). Population growth generally results in conversion of open land into non-permeable surfaces. More than 75% of southern Californian bays and estuaries have already been dredged and filled for conversion into harbors and marinas (Horn and Allen 1985). This "hardening of the coast" increases the rate of runoff and can impact water quality through addition of sediment, toxic chemicals, pathogens and nutrients to the ocean. Besides the impacts of land conversion, the SCB is already home to fifteen municipal wastewater treatment facilities, eight power generating stations, 10 industrial treatment facilities, and 18 oil platforms that discharge to the open coast. Each year, local, state, and federal organizations spend in excess of $10M to monitor the environmental quality of natural resources in the SCB. Most of this monitoring is associated with National Pollutant Discharge Elimination System (NPDES) permits and is intended to assess compliance of waste discharge with the California Ocean Plan and the Federal Clean Water Act, which set water quality standards for effluent and receiving waters. Some of this information has played a significant role in management decisions in the SCB. While these monitoring programs have provided important information, they were designed to evaluate impacts near individual discharges. Today, resource managers are being encouraged to develop management strategies for the entire SCB. To accomplish this task, they need regionally-based information to assess cumulative impacts of contaminant inputs and to evaluate relative risk among different types of stresses. It is difficult to use existing data to evaluate regional issues because the monitoring was designed to be site-specific and is limited to specific geographic areas. The monitoring provides substantial data for some areas, but there is little or no data for the areas in between. Beyond the spatial limitations, data from these programs are not easily merged to examine relative risk. The parameters measured often differ among programs. Even when the same parameters are measured, the methodologies used to collect the data often differ and interlaboratory quality assurance (QA) exercises to assess data comparability are rare.

The map illustrates the preliminary 2019 PM25 emissions for stationary sources in the AB 617 South Los Angeles community. Draft emissions are composed of reported 2018 stationary point sources and 2019 aggregated stationary sources. The 2018 stationary point emission data is based on the California Emission Inventory Development and Reporting System (CEIDARS) where Air Districts report annual emissions for facilities. Draft 2019 stationary aggregate emissions in tons per year are based on the latest CARB State Implementation Plan emission inventory with a base year of 2017 (CEPAM 2019SIP v1.01), and are projected to 2019 using the most up-to-date growth and control factors at the regional scale. Stationary aggregate source emissions are distributed to more specific locations using the latest spatial surrogates resulting in high-resolution 1x1km emission grids for the community. Examples of spatial surrogates include population, housing, employment, land cover type, etc.

The map illustrates the preliminary 2019 ROG emissions for stationary sources in the AB 617 South Los Angeles community. Draft emissions are composed of reported 2018 stationary point sources and 2019 aggregated stationary sources. The 2018 stationary point emission data is based on the California Emission Inventory Development and Reporting System (CEIDARS) where Air Districts report annual emissions for facilities. Draft 2019 stationary aggregate emissions in tons per year are based on the latest CARB State Implementation Plan emission inventory with a base year of 2017 (CEPAM 2019SIP v1.01), and are projected to 2019 using the most up-to-date growth and control factors at the regional scale. Stationary aggregate source emissions are distributed to more specific locations using the latest spatial surrogates resulting in high-resolution 1x1km emission grids for the community. Examples of spatial surrogates include population, housing, employment, land cover type, etc.