PPD 631 Geographic Information Systems for Public …. Jing_Project... · PPD 631 Geographic Information Systems for ... (CoC) Program . 2014 Spring ... website of the Undergraduate

PPD 631 Geographic Information Systems for Public Policy, Planning & Development

Spring 2014

Homelessness in California:

Geographic Distribution, Housing Services, & Indicators of the Issue

Xiangyi Jing Student ID#: 2598791595

USC Sol Price School of Public Policy

2014 Spring Project Paper

1

PROBLEM INTRODUCTION

Homelessness Issue

Homelessness is one of the biggest problems facing the U.S., especially in metropolitan areas.

Conducted in March 2014, a Gallup’s survey on “American’s level of worry about national

problems” indicated that 43% Americans worried about “hunger and homelessness” a “great

deal”, and 33% worried about the issue a “fair amount” (Riffkin, 2014). Such perceptions and

worries are fair, given the extent of the issue. The 2013 Annual Homeless Assessment Report

(AHAR) to Congress released by the Department of Housing and Urban Development (HUD)

showed that the Point-in-Time (PIT) estimate of homeless population on a single night in

January 2013 was 610,042 across the U.S., with about 65% living in emergency shelters or

transitional housing programs and the rest living in unsheltered locations; 45% of all the

homeless were in major city areas (The U.S. Department of Housing and Urban Development

Office of Community Planning and Development (HUD OneCPD), 2013a). Homelessness is

prevalent in large metropolitan areas, and the rate of homelessness in the most populated

metro areas tends to be higher than the national level (National Alliance to End

Homelessness (NAEH) & Homelessness Research Institute (HRI), 2012).

Homelessness in California is particularly serious. According to the 2013 AHAR, more than

22% of the total homeless population in the U.S. is located in California, and California is

one of the six states in which the majority of the homeless are unsheltered (HUD OneCPD,

2013a). Although there was a 25.8% decrease in homeless population in California between

2007 and 2013, the population increased 4.5% between 2012 and 2013 (HUD OneCPD,

2013a), indicating that homelessness is still a prominent challenge in the State.

Causes of Homelessness

Prior studies have identified various determinants and root causes of homelessness. A report

pointed out that people become homeless mainly because they do not have sufficient

“financial resources to obtain or maintain housing”; people in poverty are at high risk of

homelessness due to the high “level of housing cost burden”; unemployment is another main

factor that can lead to homelessness (NAEH & HRI, 2012). The report also mentioned that

some particular demographic groups are more likely to become homeless, such as people

who live with friends or family, people discharged from prisons or jails, youth aged out of


2

foster care, and uninsured people (NAEH & HRI, 2012).

One of the most comprehensive studies was conducted by Lee, Price-Spratlen, and Kanan

(2003). They stated that the roots of homelessness are embedded in the “social, economic,

and institutional fabric” of local communities; by using a homeless count data for 335

metropolitan areas, they proposed six structural determinants of homelessness, with 2 to 4

variables under each determinant: local housing market (rent level, home ownership, and

vacancy rate), economic conditions (e.g., unemployment rate), demographic composition

(e.g., 1-person households), the safety net (e.g., per capita mental health expenditures for

state), climate (e.g., temperature range), and community transience (mobility rate and

transport access). They found that median rent level and percentage of single-person

households were the main factors and were positively associated with homelessness rates in

metro areas (Lee, Price-Spratlen, & Kanan, 2003). Based on this study conducted by Lee et al.

(2003), some other researchers used HUD PIT data and analyzed community-level

determinants of homelessness; they confirmed most of the findings uncovered by Lee and

colleagues, with some contradictory findings as well, and additionally found a negative

correlation between state mental health expenditures and homelessness rates (Byrne, Munley,

Fargo, Montgomery, & Culhane, 2012). However, another study claimed that serious mental

illness is not a cause of homelessness; instead, there is a link between poverty and serious

mental illness, and people with mental illness are more likely to be socioeconomic deprived

and consequently face risk of homelessness (Montgomery, Metraux, & Culhane, 2013).

Homelessness Policy

Homelessness has become a national issue in the U.S. for decades, and policies have been

improved in order to deal with the problem. In the 1980s and 1990s, the federal government

considered homelessness as a “short-term economic emergency” instead of a long-term

socioeconomic problem, and therefore adopted an “emergency-response approach” that could

hardly solve the issue (Kondratas, 1991). In recent years, the federal government has made

substantial changes to previous policies and initiated several programs. In May 2009, the

Homeless Emergency Assistance and Rapid Transition to Housing (HEARTH) Act of 2009

was signed by President Obama; the HEARTH Act consolidated HUD’s grant programs,

increased prevention resources, and made some other improvements (HUD OneCPD, 2013b).

As part of the HEARTH homelessness programs, the Continuum of Care (CoC) Program


3

engages counties in ending homelessness; a CoC is also the local planning unit “responsible

for coordinating the full range of homelessness services in a geographic area”; and the scale

of a CoC can be a city, a county, a metropolitan area, or even a state (HUD OneCPD, 2013a;

2013c).

STUDY DESIGN

Purpose of the Study

This study used GIS to visually show the current geographic distribution of homeless

population and homelessness housing services in California. Although causal factors and

causal relations were not specifically identified, based on literature review, this study

proposed and analyzed a few potential indicators of homelessness issue.

Research Questions

The study was intended to answer the following research questions:

1) How does homeless population distribute across CoCs/counties in California?

2) Are homeless housing services provided for areas that are most in need?

3) How do indicators of homelessness vary in each geographic area?

Data

Shapefiles:

• The CoC GIS shapefiles were downloaded from the official website of HUD’s Office of

Community Planning and Development (OneCPD Resource Exchange). According to

the Data Overview and Guidelines, the CoC dataset shows the boundaries of all CoCs in

California in 2012 (HUD OneCPD, 2012). There are 39 CoCs in the dataset.

• Note: a) not all California counties are included in these CoCs, and the boundaries

of some CoCs are not identical to county boundaries (more boundary discussion is

in the GIS Analysis section of this study).

• b) The number of CoCs are not continuous: from CoC 500 to 522, (missing

shapefiles of CoC 523 and 524), followed by CoC 525 to 526, then CoC 600 to 609,

and finally CoC 611 to 614.

• The California counties shapefile (Greenwood & Jara, 2012) was downloaded from the

website of the Undergraduate Studies in Earthquake Information Technology (USEIT)


4

that is affiliated to the Southern California Earthquake Center (SCEC) at the University

of Southern California (USC).

Map:

• One scanned map of California counties was downloaded from online search

(Unknown author, n.d.) in order to create a digitized map.

Excel Datasets:

• As part of the 2013 AHAR released by HUD, the results of 2013 Point-In-Time (PIT)

homeless count and Housing Inventory Count (HIC) conducted in January 2013 were

downloaded from the OneCPD Resource Exchange website (HUD OneCPD, 2013d).

These two datasets provide numbers of homeless population and housing services in

each CoC.

• Note: a) data of 2013 PIT homeless in CoC 605 (San Buenaventura/Ventura

County CoC) is missing, but 2013 HIC numbers of CoC 605 is provided. As a

result, analysis regarding homeless population in CoC 605 and any other analysis

that needs PIT data of CoC 605 is not included in the study.

• b) Although both PIT and HIC data of CoC 523 (Colusa, Glen, Lake, Tehama,

Trinity Counties CoC) and CoC 524 (Yuba City, Marysville/Sutter, Yuba

Counties CoC) is available, analysis of these two CoCs is excluded in the study

due to the lack of their shapefiles (as mentioned above).

• Census data, including demographics, housing and economic situation, that is used to

analyze the potential indicators of homelessness was downloaded from U.S. Census

Bureau website using the search engine of State & County QuickFacts as well as

American FactFinder. The data source is 2008-2012 American Community Survey

(ACS) 5-Year Estimates (U.S. Census Bureau, 2012).

Method

First of all, the 39 CoC shapefiles were merged into one layer, and then the merged layer was

projected using the NAD_1983_StatePlane_California_I_FIPS_0401_Feet coordinate system.

Clipping and digitizing a scanned map was used when comparing CoC boundaries with

county boundaries. Thematic mapping was the main method used in the study: different

symbologies such as graduated colors, graduated symbols, and bar charts were applied to

different maps; quantile classification and data exclusion methods were also used when

creating thematic maps.


5

GIS ANALYSIS & RESULTS

Applying GIS methods mentioned in the previous section, this study analyzed the boundary

difference between CoCs and counties, the distribution of homeless population across CoCs,

the number of year-round temporary and permanent housing beds provided to homeless

people, as well as the housing and economic demographic facts that can be potential

indicators of homelessness.

CoCs vs. Counties

As stated in the Homelessness Policy section and the shapefile descriptions, a Continuum of

Care (CoC) is the basic unit of homelessness services. It can be at different government

levels (city, county, metro area, or state), and its geographic scale is not always the same as

administrative boundaries. Both a digitalized scanned map of counties (Figure 1) and Figure

2 compare all the California CoC boundaries with county boundaries. Although the total 39

CoCs do not encompass the entire State, it is apparent that most of CoC boundaries are

consistent with county boundaries (coastal CoCs do not fully fill in the county boundaries

because of the administrative boundary extension along the coastline).


6

Comparisons between each CoC and each county boundary were conducted in GIS by

checking a particular layer while unchecking all the other CoC layers, and the results are: the

boundaries of 28 CoCs are the same as their corresponding county boundaries; 5 CoCs

contain more than one county (see Figure 3); 6 CoCs are within a county, and 2 of them (CoC

600 and 605) are slightly smaller but very similar to county boundaries: Los Angeles County

is composed of CoC 600 (Los Angeles City & County CoC), 606 (Long Beach CoC), 607

(Pasadena CoC) and 612 (Glendale CoC), and Ventura County is composed of CoC 605 (San

Buenaventura/Ventura County CoC) and 611 (Oxnard CoC) (see Figure 4).

These maps show that the scales of California CoCs vary from city to county and then to

multiple counties. Most of the analysis in this study is at the CoC level because homeless

population and housing inventory data is collected at this level, whereas analysis of the

indicators of homelessness is at the county level because it would be complicated to get

census data for CoC 600 or 605, which is larger than a city but smaller than a county.


7

Geographic Distribution of

Homeless

The Point-in-Time (PIT) estimates

provide numbers of total homeless,

total sheltered and unsheltered

homeless, total chronically homeless

and many other information regarding

homeless population on a single night

in January 2013 (HUD OneCPD,

2013d). Figure 5 displays the

distribution of total homeless by CoC

in 2013 (note: homeless data is missing

in CoC 605): CoC 600 (Los Angeles

City & County CoC) had the largest

population of homeless, followed by

CoC 500 (Anchorage CoC) and 601

(San Diego City and County CoC).

Among homeless population, chronically

homeless are people who have “either

been continuously homeless for 1 year or

more” or have “experienced at least four

episodes of homelessness in the last 3

years”, and unsheltered homeless are

“people with a primary nighttime

residence that is a public or private place

not designed for or ordinarily used as a

regular sleeping accommodation for

human beings”, according to the HUD’s

definition (HUD OneCPD, 2013a).

Measured by these standards, unsheltered

chronically homeless are most in need of

help. Figure 6 shows the percentage of


8

unsheltered chronically homeless in total homeless by CoC. San Francisco CoC (CoC 501)

has both a high level of total homeless and a high percentage of unsheltered chronically

homeless. Even though some CoCs don’t have a large number of homeless, a high percentage

of homeless in these CoCs suffer a lot. For example, homeless population in CoC 519

(Chico/Paradise/Butte County CoC) is at the lowest level, but it is one of the CoCs that have

the largest proportion of unsheltered chronically homeless. In contrast, Los Angeles City &

County CoC (CoC 600), with the largest number of total homeless, only has a medium level

of unsheltered chronically homeless, compared to other CoCs.

Housing Services for Homeless

There are five categories of housing services for homeless: Emergency Shelter (ES),

Transitional Housing (TH), Rapid Rehousing (RRH), Safe Haven (SH), and Permanent

Supportive Housing (PSH) (HUD OneCPD, 2013d). Specifically, ES is temporary, TH

provides services up to 24 months, RRH is short term, SH is long term housing for homeless

with severe mental illness, and

PSH is project- and tenant-based

housing on a long-term basis for

homeless with a disability (HUD

OneCPD, 2013a). In 2013, the

total year-round beds of housing

services except PSH by CoC are

illustrated in Figure 7, and the

number of PSH beds by CoC is

showed in Figure 8. Both the

maximum of year-round

non-PSH beds (11933) and the

maximum of PSH beds (11867)

appear in Los Angeles City &

County CoC (CoC 600).

Comparing Figure 6 with Figure

7 & 8, it is not hard to find that

some CoCs with high


9

percentages of chronically unsheltered homeless, such as the Chico/Paradise/Butte County

CoC (CoC 519), the Humboldt County CoC (CoC 522), and the Imperial County CoC (CoC

613), have low levels of both non-PSH and PSH services. Considering that these CoCs also

have the lowest number of total homeless, it is possible that the numbers of housing beds are

small because there are not a lot of people in need of housing services. Dividing the total of

all housing services (both non-PSH and PSH) by total homeless (note: homeless data is

missing in CoC 605), Figure 9 shows that only 7 CoCs provide each homeless person more

than 1 bed on average; although Los Angeles City & County CoC (CoC 600) has the

maximum amount of both non-PSH and PSH beds, the CoC’s year-round number of beds per

homeless is at the lowest level; housing services received by each homeless in CoC 519 and

613 are in fact at the medium level, whereas CoC 522 is still at the lowest level. These

findings indicate that some CoCs may not be providing sufficient housing services to

homeless in most need.

Potential Indicators of Homelessness

Studies have shown that homelessness is a complex issues that involves many factors and has

a series of root causes. Simplifying the factors that have been stated in literature review, this


10

study only provides basic information about veteran proportions, housing markets, and

economic situations, which may not be causal factors but can be potential indicators of the

issue. As mentioned above, analysis in this section is at the county level: Oxnard CoC (CoC

611) and San Buenaventura/Ventura County CoC (CoC 605) are incorporated in the Ventura

County; Los Angeles City & County CoC (CoC 600), Long Beach CoC (CoC 606), Pasadena

CoC (CoC 607) and Glendale CoC (CoC 612) are incorporated in the Los Angeles County.

Also, CoCs with two or more counties were broken down to separate counties and were

analyzed at the county level.

Veteran Proportion: the 2013 Point-in-Time (PIT) count shows that California has the largest

number of Veterans on a single night in January 2013, which was 15,179, and veterans

accounted for 11.1% of total homeless in California (HUD OneCPD, 2013a). Figure 10

shows the proportions of veterans

among total California population

by county. The sizes of dots reveal

that counties in Northern California

have the largest proportions of

veterans. Comparing Figure 10 with

Figure 5, it seems that counties with

a large number of homeless may

not have a high proportion of

veteran population. Additionally,

unemployment rate is considered as

a factor that may have relations

with proportions of veterans.

However, the result indicates that

counties having the same level of

unemployment rates may have

significantly different proportions

of veterans. For instance, the Butte

County (corresponding to CoC 519)


11

and the Imperial County

(corresponding to CoC 613) are at

the same level of unemployment

rate, but the former has the largest

proportion of veterans while the

latter has the lowest proportion.

Housing Market: people become

homeless due to the lack of ability

to afford housing. The assumption

is that counties with more per

capita housing units and/or higher

homeownership rates tend to have

less homeless population. Figure 11

displays the different levels of total

housing units, per capita housing

units as percent of total units, and

homeownership rates across

counties.

Comparing Figure 5 with Figure 11, it can be found that some counties with large number of

homeless, such as the Santa Clara County (corresponding to CoC 500) and the Los Angeles

County (corresponding to the sum of CoC 600, 606, 607 and 612), tend to have large number

of housing units as well. Using the bar charts with the normalized percent of total, Figure 11

reveals that some counties with less housing units can have both high levels of per capita

housing units and high levels of homeownership rates, such as the Mendocino County

(corresponding to CoC 509) and the Shasta County (corresponding to CoC 516), and these

two counties have a smaller number of total homeless.

Economic Situation: an unfavorable economic situation can potentially increase people’s risk

of lacking sufficient financial resources for housing, so economic situation is assumed as an

indicator of homelessness: counties with poor economic situations are more likely to have

more homeless population. Including three factors that can represent economic situation,


12

Figure 12 illustrates the

12-month per capita money

income of total population in

each county, and the bar

charts display the percent of

total population below

poverty level as well as the

unemployment rates. As

shown in the Figure, counties

with the highest level of per

capita income

($34,572-$55,695) are in

Northern California,

especially in the Bay area.

Counties with high per capita

money income level tend to

have lower poverty rates and

lower unemployment rates as

well.

Comparing the figures about

the homeless population distributions (Figure 5 & 6) with Figure 12, it is found that some

counties with relatively poor economic situations (e.g., the Los Angeles County) indeed have

a larger amount of homeless, but it is not the case for some other counties. For example,

although the San Francisco County (corresponding to CoC 501) and the Santa Clara County

(corresponding to CoC 500) have relatively good economic situations, they still have a large

number of homeless as well as a high percentage of unsheltered chronically homeless.

STUDY LIMITATIONS

This study only provides a basic geographic analysis of homelessness issue, and there are a

lot of limitations.

• Data Limitations: a) as mentioned in the data descriptions, there is missing shapefiles

of CoC 523 and 524 (although PIT and HIC data is available), as well as missing data of


13

2013 PIT homeless count in CoC 605; b) currently, homelessness data from HUD and

other government agencies is only number counts of homeless population and housing

inventory within a CoC; there is no specific address or location of homeless people or

housing facilities. As a result, there is no way to analyze the density of homeless or

housing services in a CoC.

• Analysis Limitations: Analysis regarding potential indicators of homelessness is

inconclusive because a) only a few census attributes were selected to measure each

indicator, and the results may not show the whole picture of counties’ facts; and b) the

study did not include statistical analysis of causal relations between the indicators and

the number of homeless.

CONCLUSION

Homelessness is a serious issue facing California. As metropolitan CoCs/counties,

homelessness problem in San Francisco and Los Angeles is particular severe. For counties

with less homeless population, they may have a high percentage of unsheltered chronically

homeless, indicating that long-term services for homeless are necessary. Although the total

housing services are largely allocated to counties with more homeless, services received by

each homeless in most of the counties are inadequate. Analysis of homelessness indicators

may not be conclusive. Some findings are consistent with the assumptions, but some results

are contradictory to the assumptions. For example, counties with a large number of total

homeless do not necessarily have a high proportion of veteran population.

One of the policy implications of the study is that there should be an increase in housing

services, especially long-term services for unsheltered chronically homeless. In addition,

since CoCs have different levels of administrative boundaries, it requires government at

different levels to coordinate and cooperate with each other efficiently to provide services for

homeless.


14

REFERENCES Byrne, T., Munley, E. A., Fargo, J. D., Montgomery, A. E., & Culhane, D. P. (2012). New

perspectives on community-level determinants of homelessness. Journal of Urban Affairs, 35(5), 607-625. DOI: 10.1111/j.1467-9906.2012.00643.x

Kondratas, A. (1991). Ending homelessness: Policy challenges. American Psychologist,

46(11), 1226-1231. DOI: http://dx.doi.org.libproxy.usc.edu/10.1037/0003-066X.46.11.1226

Lee, B. A., Price-Spratlen, T., & Kanan, J. W. (2003). Determinants of homelessness in

metropolitan areas. Journal of Urban Affairs, 25, 335–356. DOI: 10.1111/1467-9906.00168

Montgomery, A. E., Metraux, S., & Culhane, D. P. (2013). Rethinking homelessness

prevention among persons with serious mental illness. Social Issues and Policy Review, 7(1), 58-82. DOI: 10.1111/j.1751-2409.2012.01043.x

National Alliance to End Homelessness (NAEH) & Homelessness Research Institute (HRI).

(2012). The state of homelessness in America 2012. Retrieved from http://msnbcmedia.msn.com/i/MSNBC/Sections/NEWS/z-pdf-archive/homeless.pdf

Riffkin, R. (2014, March 12). Climate change not a top worry in U.S.: U.S. concerns with the

quality of the environment dropped in 2014. Retrieved from http://www.gallup.com/poll/167843/climate-change-not-top-worry.aspx

The U.S. Department of Housing and Urban Development Office of Community Planning

and Development (HUD OneCPD). (2013a). The 2013 annual homeless assessment report (AHAR) to congress part 1: Point-in-time estimates of Homelessness. Retrieved from https://www.onecpd.info/resources/documents/ahar-2013-part1.pdf


and Development (HUD OneCPD). (2013b). Continuum of Care (CoC) Program. Retrieved from https://www.onecpd.info/coc/


and Development (HUD OneCPD). (2013c). Homeless Emergency Assistance and Rapid Transition to Housing Act. Retrieved from https://www.onecpd.info/homelessness-assistance/hearth-act/

DATA SOURCES CoC Shapefiles: The U.S. Department of Housing and Urban Development Office of Community Planning

and Development (HUD OneCPD). (2012). CoC GIS Shapefiles – California CoCs. Retrieved from https://www.onecpd.info/coc/gis-tools/

Counties Shapefile: Greenwood, R., & Jara, M. (2012). California Counties Shapefiles. Retrieved from http://scec.usc.edu/internships/useit/content/california-counties-shapefiles


15

California Map: Unknown author. (n.d.). California Counties Map. Retrieved from https://www.tracy.k12.ca.us/sites/mwoodward/Documents/Old.Web.Pages/Old.Classroom.Web.Page/Core_Social_Studies/Geography/California%20Agriculture.htm 2007-2013 Point-in-Time Estimates by CoC & 2007-2013 Housing Inventory Count by CoC: The U.S. Department of Housing and Urban Development Office of Community Planning

and Development (HUD OneCPD). (2013d). 2013 AHAR: Part 1 - PIT Estimates of Homelessness. Retrieved from https://www.onecpd.info/resource/3300/2013-ahar-part-1-pit-estimates-of-homeless

ness/ Demographics, Housing & Economic Facts of Counties: U.S. Census Bureau. (2012). 2008-2012 American Community Survey 5-Year Estimates. Retrieved from

http://factfinder2.census.gov/faces/nav/jsf/pages/community_facts.xhtml & http://quickfacts.census.gov/qfd/states/06000.html


16

APPENDIX

The List of California CoCs and Corresponding Counties

CoC Number CoC Name County Name

CA-500 San Jose/Santa Clara City & County CoC Santa Clara CA-501 San Francisco CoC San Francisco CA-502 Oakland/Alameda County CoC Alameda CA-503 Sacramento City & County CoC Sacramento CA-504 Santa Rosa/Petaluma/Sonoma County CoC Sonoma CA-505 Richmond/Contra Costa County CoC Contra Costa

CA-506 Salinas/Monterey, San Benito Counties CoC Monterey San Benito

CA-507 Marin County CoC Marin CA-508 Watsonville/Santa Cruz City & County CoC Santa Cruz CA-509 Mendocino County CoC Mendocino CA-510 Turlock/Modesto/Stanislaus County CoC Stanislaus CA-511 Stockton/San Joaquin County CoC San Joaquin CA-512 Daly/San Mateo County CoC San Mateo

CA-513 Visalia, Kings, Tulare Counties CoC Kings Tulare

CA-514 Fresno/Madera County CoC Fresno Madera

CA-515 Roseville/Rocklin/Placer, Nevada Counties CoC Placer Nevada

CA-516 Redding/Shasta County CoC Shasta CA-517 Napa City & County CoC Napa CA-518 Vallejo/Solano County CoC Solano CA-519 Chico/Paradise/Butte County CoC Butte CA-520 Merced City & County CoC Merced CA-521 Davis/Woodland/Yolo County CoC Yolo CA-522 Humboldt County CoC Humboldt CA-525 El Dorado County CoC El Dorado

CA-526 Tuolumne, Calaveras, Amador Counties CoC Tuolumne Calaveras Amador

CA-600 Los Angeles City & County CoC

Los Angeles CA-606 Long Beach CoC CA-607 Pasadena CoC CA-612 Glendale CoC CA-601 San Diego San Diego CA-602 Santa Ana/Anaheim/Orange County CoC Orange CA-603 Santa Maria/Santa Barbara County CoC Santa Barbara CA-604 Bakersfield/Kern County CoC Kern CA-605 Ventura Ventura CA-611 Oxnard/San Buenaventura/Ventura County CoC CA-608 Riverside City & County CoC Riverside


17

CA-609 San Bernardino City & County CoC San Bernadino CA-613 Imperial County CoC Imperial CA-614 San Luis Obispo County CoC San Luis Obispo