2015 Incorporated Cities and Unincorporated Area Greenhouse Gas Inventory (GPC Compliant)

Fire perimeter data describes wildfire history from CAL FIRE (including timber fires 10 acres or greater, brush fires 30 acres and greater, and grass fires 300 acres or greater) and US Forest Service (10-acre minimum for fires since 1950). The fire perimeters dataset contains separate polygons for each burn occurrence. Fire frequency data for 2000-2016 was pulled directly from CalFire annual reports. CalFire data for 2017 and 2018 was compiled using their ongoing reporting of large fires.

Species were identified that currently occur in LA County and are listed as endangered, threatened based on the Endangered Species Act. The list for all species came from searching the United States Fish and Wildlife Service (USFWS) 5-year plans by county (Los Angeles), which directs you to a table of the currently listed species. Species counts were determined from 2018 data on iNaturalist. Current statuses were confirmed using online databases of species records, including eBird and iNaturalist for vertebrates, the Consortium of California Herbaria for plants, and UCLA experts.

Species count sourced from 2018 iNaturalist for each taxa group using the following specifications:
o Quality = Research
o Reviewed = Any
o Identifications = Most Agree
o Captive/Cultivated = No
o Place = Los Angeles County
• Species were identified that currently occur in LA County and are listed as endangered, threatened based on the Endangered Species Act. The list for all species came from searching the United States Fish and Wildlife Service (USFWS) 5-year plans by county (Los Angeles), which directs you to a table of the currently listed species.
• Current statuses were confirmed using online databases of species records, including eBird and iNaturalist for vertebrates, the Consortium of California Herbaria for plants, and UCLA experts.

Emissions from passenger vehicles and trucks are estimated based on VMT by vehicle type. VMT for each jurisdiction is estimated using trip-based travel forecasting models developed by Southern California Association of Governments (SCAG). SCAG’s regional travel demand model analyzes transportation network and socioeconomic data such as population, household, and employment, to forecast daily vehicle trips for each traffic
analysis zone (TAZ). Model outputs include:
• Vehicle trips by type: including cars, light trucks, medium duty trucks, heavy duty trucks, and transit vehicles
• Vehicle trip lengths by trip purpose
• Vehicle trip origins and destinations

Based on vehicle trips analysis, VMT calculations are performed for all cities within LA County (except for the City of Avalon), including unincorporated areas that are under direct County control. Using CARB Emissions Factors (EMFAC) model, CO2 and N2O emissions are estimated by multiplying emissions rates with vehicle activity data in all cities and unincorporated areas within the South Coast (SC) sub-area and the Mojave Desert (MD) sub-area. Sub-area emissions are then disaggregated based on speed bin by time of day and vehicle profile to estimate GHG emissions from into passenger vehicle and heavy duty trucks.

Data sourced from UCLA Energy Atlas. Includes building energy use by city and building type.

Data by Special Planning Area from LA County Public Health for the purposes of normalizing counts of ED visits. Data is normalized by population and includes counts of heat stress ED visits by Special Planning Area from LACDPH.
Omitted SPA 5 and 6 for all years except 2008 because the counts were too small.

Geospatial data of gentrified census tracts and proximty to high quality transit areas.
The proximity of gentrified census tracts to 2012 HQTAs was mapped. California Housing Partnership identified tracts that gentrified between 2000 and 2013, defining gentrification as socioeconomically disadvantaged neighborhoods that experienced faster changes in the following areas relative to County-level trends during the same period: 1) the percentage point increase in college educated population; 2) the percentage point increase non-Hispanic white population; 3) absolute value increase in median household income; and 4) the absolute value increase in gross rent. The gentrified census tract layer was added to a map of 2012 HQTAs within LA County.

Data describes housing affordability for selected geographies and determines the shortage or surplus of affordable housing by income group was done by CHPC for renter households for years 2014-2016.
Deeply Low Income (DLI) is defined as 0-15% of Area Median Income (AMI)
Extremely Low Income (ELI) is defined as 16-30% of AMI
Very Low Income (VLI) is defined as 31-50% of AMI
Low Income is defined as 50-80% of AMI
Moderate Income is defined as 80-100% of AMI

See https://data.lacounty.gov/Transportation/County-Employee-Average-Vehicle-Ridership-AVR-2012/b92g-vuub for more detail.

Average Vehicle Ridership (AVR) is the figure derived by dividing the employee population at a given worksite that reports to work weekdays between 6:00 a.m. and 10:00 a.m. by the number of vehicles driven by these employees commuting from home to the worksite during these hours. The AVR is calculated using a weekly averaging period. The applicable employee population is multiplied by the number of weekdays in the selected averaging period, then divided by the total number of vehicles driven by these employees to the worksite during the same period.

The survey is administered to each county department by location. The calculated AVR for each responding location was included in the data from the CSO (who received the data from the Department of Human Resources) for years 2012-2018, except 2017. An AVR of “1” means that every employee drove alone; therefore a greater AVR is a better outcome. The target AVR is set for each county department, and targets range mostly from 1.5-1.75.
For each year, a weighted average for the total AVR across county departments was calculated to account for the varied number of employees at each location and graphically presented the data.