Erica Quintana
GIS Final Write Up
Introduction:
United Way is a nonprofit organization in LA County that helps to change policies and climates
surrounding urban poverty issues to create ways to end poverty in LA County. One poverty issue United
Way focuses on is ending homelessness in our county. With this project I wanted to help the Home for
Good team at United Way identify the next areas in LA County that they should target to help improve
the homeless resources so that area could work towards ending homelessness locally.
Extent of Study: Los Angeles County and the census tracts contained in Los Angeles County
Since the extent of my study was LA County I would need to develop an index to rank cities. I based my
index on the three biggest predictors of homelessness- percent of people living below the poverty line,
percent unemployment, and percent of people severely rent burdened. The other variable I used to
create the index was the census predicted unsheltered homeless count for the census tract.
I converted each variable into a raster and mapped each variable I used in my index to get an idea of
what that variable looked like throughout the county first to be able to judge whether my index was
correct.
The raster maps for the four variables are:
Erica Quintana
GIS Final Write Up
The employment variable is from table DP03 in the American Community Survey and indicates percent
unemployment in a census tract. The poverty variable indicates the percent of the population in the
census tract living below the poverty and is from table B17001. The rent burden variable indicates the
percent of people in the census tract that spends more than 30% of their income on housing costs and is
from table B25106. The unsheltered homeless population estimates is from table PCT20 and contains
information on group quarters population by group quarters type. A row in this chart counts “other noninstitutional facilities” coded as soup kitchen lines or emergency shelters like motel room vouchers.
With these four rasters I used map algebra in GIS to compute an index that would identify areas in LA
County that scored high on the index which would indicate that they were high “risk” areas or high
“need” areas.
The resulting index map:
Erica Quintana
GIS Final Write Up
On this map the orange and red areas scored higher on the index indicating that they are in most need
of attention. When choosing areas that the Home For Good team should work with, I wanted to choose
areas that were not Los Angeles and Long Beach because these areas already receive much of the
homeless resource funding and are already aware of the homeless issues and working towards ending
homelessness. Instead I wanted to identify areas that scored high on the index but are not as active in
ending homelessness in their area and may have a difficult time in securing funding to address poverty
issues because they may not know the resources available or how to obtain them.
From a visual inspection of the index calculations the four main target areas I decided to analyze four
main areas more in terms of existing resources and possible mismatch. I used permanent supportive
housing locations not only to indicate the resources available to end homelessness in the city but also as
an indication of issue awareness and willingness to end homelessness in the city. The areas identified
were Lancaster/Palmdale, Irwindale, Pomona, and Compton.
Index map indicating four areas for further analysis:
Erica Quintana
GIS Final Write Up
Erica Quintana
GIS Final Write Up
Cities/Communities for further analysis:
Lancaster/Palmdale:
These cities had some areas that scored high on the index and therefore I decided to identify this as one
of United Way’s target areas.
I then wanted to further analyze the cities based on the resources they might have to address
homelessness and then determine if there was a mismatch of housing units and unsheltered homeless
individuals. Upon inspection of the resources in these areas there seems to be 3 PSH locations in these 2
cities.
Erica Quintana
GIS Final Write Up
The three mile buffer around each of PSH locations shows that there is somewhat of a mismatch
between the PSH units and the estimated homeless populations of these two cities. The presence of
permanent supportive housing units indicates that Lancaster/Palmdale has begun to address the
homeless issue in their area but as they still score high on the need index it is recommended that UW
help this area organize and advocate for increased resources.
Irwindale
Erica Quintana
GIS Final Write Up
The entire City of Irwindale scores high on the Index this is why I decided to identify this as an area to
target and analyze more. Upon further analysis of the resources I observed that there were actually no
PSH resources in this area and the 3 mile buffer of another PSH location barely reaches the corner of the
City.
Erica Quintana
GIS Final Write Up
Further, the 3 mile radius that is barely tangent to Irwindale already seems to have a mismatch in terms
of units and homeless population. When I looked at the census estimate of homelessness it looks like
Irwindale as an estimated 50 homeless individuals within about a 4 mile radius which would be a
relatively high density. Since I couldn’t create a buffer for this city due to the lack of PSH locations I
drew a circle around it and filled it with the color that relatively corresponds to the homeless population
estimates we are using to compare other cities.
Pomona:
Pomona was another area that seemed to score high on the Index and so I decided to choose this as an
area to analyze further.
Erica Quintana
GIS Final Write Up
Upon further analysis it appears as though Pomona has two small PSH locations.
Erica Quintana
GIS Final Write Up
The 3 mile radius around these locations shows that there is a little bit of a mismatch in terms of supply
and homeless population. There may not be as large a need to focus on this area but Pomona appears to
be taking steps to address the homeless issue in their community and since they are rather isolated from
the rest of the PSH locations I recommend that UW encourage them to be a “housing model” or a
“housing hub” for the Eastern part of LA County.
Compton
I chose to identify Compton as a target area because they lie almost directly between two areas with
large numbers of homeless individuals (Los Angeles and Long Beach).
Since Compton lies in the middle of these two hubs I theorize that there are either a lot of transient
homeless individuals that pass through here or that they may often get overlooked for PSH resources (or
both)
Upon further inspection of the PSH resources it appears as though there is a noticeable amount of
mismatch between homeless population and units in this area- especially since Compton is only about 4
miles across.
Erica Quintana
GIS Final Write Up
I therefore recommend that UW target this area to help them organize and advocate for more resources
here so they are not overshadowed by LA and Long Beach and can accommodate the homeless that
lives in the city.
Conclusion:
I recommend that the Home For Good team target Lancaster and Palmdale because they scored high on
the need index and have a mismatch between resources available and demand. I recommend that Home
For Good make Irwindale a priority because this city scored high on the index and currently has no
permanent supportive housing resources to address the issue of homelessness in the area. I recommend
that Home For Good target Pomona, despite their relatively low mismatch, because this city is isolated
from most other permanent supportive housing locations and are working to end homelessness in their
city. I think that Home For Good’s support for Pomona could help make Pomona a “housing model” for
other cities in East LA County. Finally, I recommend that Home For Good make the city of Compton a
priority because Compton is right between two cities with very large numbers of homelessness- Los
Angeles and Long Beach. It is important that Home For Good help Compton draw more PSH resources to
their city to address the mismatch of housing resources and to help accommodate the transient
homeless population that likely travels through Compton often.
Erica Quintana
GIS Final Write Up
Resources:
United Way: PSH data
American Community Survey/Census: 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
ESRI: Basemaps, Address locators, etc.
Skills Used:
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
Erica Quintana
GIS Final Write Up
3. Original Data: I received an excel spreadsheet containing information for the PSH Locations
including addresses and total unit numbers Spatial Statistics: I created a statistic for the
mismatch of PSH units to homeless populations in the 3 mile buffer surrounding the PSH
locations
4. Spatial Statistics: I created a statistic for the mismatch of PSH units to homeless populations in
the 3 mile buffer surrounding the PSH location
5. Inset Maps: I created inset maps for most of my maps to give audience an idea of placement
within the county
6. Point/Graduated Symbol: I created a graduated symbol for the PSH locations to show the
difference in total number of units within each location
7. 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)
8. 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
9. Geocoding: I geocoded the addresses from the original Excel data on PSH locations from United
Way
10. 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