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Erica Quintana. GIS Final 2012. Policy Question. 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?

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Erica quintana

Erica Quintana

GIS Final 2012


Policy question

Policy Question

  • 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


Extent of study

Extent of Study


Creation of an index

Creation of an Index

  • 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


Extent of study los angeles county

Extent of Study: Los Angeles County


Areas scoring high on index

Areas Scoring High on Index


Area 1 palmdale lancaster

Area #1: Palmdale/Lancaster

  • Basic Data:

    • Index: Lancaster and Palmdale both have areas that score high on the Index


Lancaster palmdale mismatch

Lancaster/palmdale Mismatch


Area 2 irwindale

Area #2: Irwindale


Irwindale mismatch

Irwindale mismatch

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


Area 3 pomona

Area #3: Pomona


Mismatch in pomona

Mismatch in pomona


Area 4 compton

Area #4: Compton


Mismatch in compton

Mismatch in compton


Required skills used

Required Skills Used

  • Modeling: I used modeling to create rasters and then reclassify the rasters to create an index

  • 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

  • Original Data: I received an excel spreadsheet containing information for the PSH Locations including addresses and total unit numbers


Additional skills

Additional Skills

  • Spatial Statistics: I created a statistic for the mismatch of PSH units to homeless populations in the 3 mile buffer surrounding the PSH locations

  • Inset Maps: I created inset maps for most of my maps to give audience an idea of placement within the county

  • Point/Graduated Symbol: I created a graduated symbol for the PSH locations to show the difference in total number of units within each location

  • 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)

  • 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

  • Geocoding: I geocoded the addresses from the original Excel data on PSH locations from United Way

  • 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


Sources

Sources

  • 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.


Models

Models


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