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178 Results
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• Requested and received estimates of population by SPA from LA County Public Health for the purposes of normalizing counts of ED visits
• Requested and received counts of heat stress ED visits by Special Planning Area from LACDPH
• Normalized raw counts by population.
• Generated line graphs of county total heat stress ED visits per 100,000 residents.
• Generated line graphs of heat stress ED visits by SPA.
• Omitted SPA 5 and 6 for all years except 2008 because the counts were too small.
• Requested and received counts of heat stress ED visits by Special Planning Area from LACDPH
• Normalized raw counts by population.
• Generated line graphs of county total heat stress ED visits per 100,000 residents.
• Generated line graphs of heat stress ED visits by SPA.
• Omitted SPA 5 and 6 for all years except 2008 because the counts were too small.
Tags
No tags assigned
Created
August 30 2019
Views
99
• A gap analysis to determine the shortage or surplus of affordable housing by income group was done by CHPC for renter households for years 2014-2016.
• Income groups are defined as follows:
o Deeply Low Income (DLI) is 0-15% of Area Median Income (AMI)
o Extremely Low Income (ELI) is 16-30% of AMI
o Very Low Income (VLI) is 31-50% of AMI
o Low Income is 50-80% of AMI
o Moderate Income is 80-100% of AMI
• The CHPC data shows the gap between the need for affordable housing for renter households and the supply at different income levels. We used this data to create charts to present the data over time.
• The first chart shows the cumulative deficit of affordable housing for each income group for years 2014-2016. Note, there is a larger deficit of housing for low income households compared to very low income households because there are a greater number of low income households. For example, housing that is affordable for deeply low income households is also counted as affordable to other income groups.
• The second chart shows the percentage of low income households (excludes moderate income group) for which there is no available affordable housing (housing shortage). In this data presentation, a lower percentage is better.
• Income groups are defined as follows:
o Deeply Low Income (DLI) is 0-15% of Area Median Income (AMI)
o Extremely Low Income (ELI) is 16-30% of AMI
o Very Low Income (VLI) is 31-50% of AMI
o Low Income is 50-80% of AMI
o Moderate Income is 80-100% of AMI
• The CHPC data shows the gap between the need for affordable housing for renter households and the supply at different income levels. We used this data to create charts to present the data over time.
• The first chart shows the cumulative deficit of affordable housing for each income group for years 2014-2016. Note, there is a larger deficit of housing for low income households compared to very low income households because there are a greater number of low income households. For example, housing that is affordable for deeply low income households is also counted as affordable to other income groups.
• The second chart shows the percentage of low income households (excludes moderate income group) for which there is no available affordable housing (housing shortage). In this data presentation, a lower percentage is better.
Tags
No tags assigned
Created
August 30 2019
Views
243
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.
• 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.
Tags
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Created
August 29 2019
Views
34
Downloaded annual reports of HAP metal emissions from TRI Explorer for LA County.
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Tags
No tags assigned
Created
August 28 2019
Views
46
Downloaded annual reports of HAP metal emissions from TRI Explorer for LA County.
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Tags
No tags assigned
Created
August 28 2019
Views
24
Downloaded annual reports of HAP metal emissions from TRI Explorer for LA County.
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Cleaned data and totaled masses of point source and fugitive air emissions for each of the pollutants for each year.
Generated line graphs showing the emitted masses per year for the HAP metals included in the TRI report list.
Three separate plots were required to show the changes in emitted masses over time since they range over four orders of magnitude.
Created heat maps of median household income and created proportional marker symbols for each emitter address location (2017).
Tags
No tags assigned
Created
August 28 2019
Views
21
Point data from the Transportation Injury Mapping System (TIMS) for all collisions in L.A. County from 2006-2015
• Geocoded point data from the Transportation Injury Mapping System (TIMS) for all collisions in LA County from 2006-2015 (the latest year available) was downloaded. The data set was narrowed to collisions in which bicyclists or pedestrians were the victims. The data set was further narrowed to fatalities and severe injuries for two reasons. First, this best addresses the goals of the County’s Vision Zero Plan. Vision Zero Plans, in the County and elsewhere, aim to prevent fatalities by reducing vehicle speed to minimize the risk of severe injury from collisions. Second, TIMS data is less reliable for minor injuries and does not represent frequently unreported collisions.
• A pivot table was used to calculate the number of bicyclist and pedestrian fatalities and severe injuries from 2006-2015 and presented this data graphically.
• The TIMS data was loaded into GIS and overlaid it on a map of LA County. Transparent points were used to convey what areas have had the highest number of collisions between 2006-2015.
• Geocoded point data from the Transportation Injury Mapping System (TIMS) for all collisions in LA County from 2006-2015 (the latest year available) was downloaded. The data set was narrowed to collisions in which bicyclists or pedestrians were the victims. The data set was further narrowed to fatalities and severe injuries for two reasons. First, this best addresses the goals of the County’s Vision Zero Plan. Vision Zero Plans, in the County and elsewhere, aim to prevent fatalities by reducing vehicle speed to minimize the risk of severe injury from collisions. Second, TIMS data is less reliable for minor injuries and does not represent frequently unreported collisions.
• A pivot table was used to calculate the number of bicyclist and pedestrian fatalities and severe injuries from 2006-2015 and presented this data graphically.
• The TIMS data was loaded into GIS and overlaid it on a map of LA County. Transparent points were used to convey what areas have had the highest number of collisions between 2006-2015.
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Created
August 28 2019
Views
68
Point data from the Transportation Injury Mapping System (TIMS) for all collisions in L.A. County from 2006-2015.
Tags
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Created
August 27 2019
Views
37
Created
August 27 2019
Views
44
• Downloaded annual (2005-2017) reports of HAP metal emissions from TRI Explorer for LA County.
• Cleaned data and totaled masses of off-site disposal for each of the pollutants for each year.
• Generated table and line graph showing the disposed masses per year for the HAP metals included in the TRI report list.
• Cleaned data and totaled masses of off-site disposal for each of the pollutants for each year.
• Generated table and line graph showing the disposed masses per year for the HAP metals included in the TRI report list.
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Created
August 27 2019
Views
31
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