1. GIS Final 2012

2. What areas in LA County should United Way target for engagement and resource development and why? Steps to address this: What factors lead to homelessness and/or have high estimates of homeless populations? Create an index to identify areas with high risk Identify current resources in the area Identify radius to surrounding resources to determine need POLICY QUESTION

3. EXTENT OF STUDY

4. Variables used to create an index: Homelessness Estimates from Census data Predictors of homelessness: Percent of population in poverty Percent of population unemployed Percent of population that is rent burdened CREATION OF AN INDEX

6. EXTENT OF STUDY: LOS ANGELES COUNTY

7. AREAS SCORING HIGH ON INDEX

8. Basic Data: Index: Lancaster and Palmdale both have areas that score high on the Index AREA #1: PALMDALE/LANCASTER

9. LANCASTER/PALMDALE MISMATCH

10. AREA #2: IRWINDALE

11. IRWINDALE MISMATCH Irwindale didn’t have any PSH locations but they had an estimated 50 homeless individuals in the Census Tract

12. AREA #3: POMONA

13. MISMATCH IN POMONA

14. AREA #4: COMPTON

15. MISMATCH IN COMPTON

16. 1.Modeling: I used modeling to create rasters and then reclassify the rasters to create an index 2.Measurement/Analysis: I created buffers around the PSH locations to include an 3 mile radius from the point then used this distance to pro-rate the mismatch of units to estimated homeless counts 3.Original Data: I received an excel spreadsheet containing information for the PSH Locations including addresses and total unit numbers REQUIRED SKILLS USED

17. 1.Spatial Statistics: I created a statistic for the mismatch of PSH units to homeless populations in the 3 mile buffer surrounding the PSH locations 2.Inset Maps: I created inset maps for most of my maps to give audience an idea of placement within the county 3.Point/Graduated Symbol: I created a graduated symbol for the PSH locations to show the difference in total number of units within each location 4.Aggregating Attribute Fields: I aggregated the attribute fields in the Rent Burden data from the census to get total number of people with rent burden of ≥ 30% income (aggregated from all income brackets) 5.Creating Indices: I created an index from my raster data sets to show areas of greatest need/highest risk in LA County variables included homeless population estimates, percent of people in a census tract living below the poverty line, percent of people in census tract with ≥ 30% rent burden, and unemployment rate in census tract 6.Geocoding: I geocoded the addresses from the original Excel data on PSH locations from United Way 7.Attribute Sub-selection: In order to show the cities/communities of focus I selected by city name to make the map readable and show clearly the area of focus ADDITIONAL SKILLS

18. United Way: PSH data American Community Survey: Employment: Table DP03, Poverty: B17001, Rent Burden: B25106, Homeless Population Estimates: PCT20 variable “other non-institutional facilities” coded as soup kitchen lines or emergency shelters like motel room vouchers GIS: Basemaps, Address locators, etc. SOURCES

19. MODELS