GIS work sample

Public Housing Location Suitability Analysis of Chicago Yuyan Huang Introduction Public housing was established to provide decent and safe rental housing for low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. Currently in the US, there are approximately 1.2 million households living in public housing units, managed by 3,300 local housing agencies.1 In the past decades, some public housing projects failed and were turned into new slums in cities, like the Pruitt-Igoe urban housing project in St. Louis, Missouri. For most public housing projects, land cost is a primary concern when choosing locations. However, thoughtful siting of public housing units should take socio-economic factors into consideration, such as the racial composition, poverty rate and median household income of a neighborhood as well as the proximity to hospitals, schools, bus stops and train stations.

Considering these factors could help prevent potential social segregation caused by public housing projects and could help provide the poor with more access to education, healthcare and public transportation. More access to opportunities could save low-income people from poverty trap.

This project is going to focus on suitable locations for public housing development in Chicago City. Two spatial analysis techniques in GIS - Measuring Network Distance and Multi-Criteria Evaluation are going to be used for this research. The whole evaluation process is based on a Public Housing Location Suitability Model, which is illustrated by Figure 1 in Technical Section. Results of the research could help the government or the Housing Council of Chicago City to decide suitable locations for new public housing projects or to preserve exiting ones.

Technical Section

1

U.S Department of Housing and Urban Development, “HUD’s Public Housing Program”. Accessed

November 28th, 2015, http://portal.hud.gov/hudportal/HUD?src=/topics/rental_assistance/phprog

1)

Data and Processing Methods As it could be seen from Figure 1, the Public Housing Location Suitability Model is

composed of three sets of parameters - neighborhood demographics (poverty rate, educational attainment, and racial composition), neighborhood economics (median household income and unemployment rate) and location accessibility (proximity to schools, hospitals, bus stops and train stations). In this report, the spatial scale of a neighborhood is equal to a census tract.

Neighborhood demographics and economics data are collected from the U.S. Census Bureau American Factfinder website.2 As for location accessibility, proximity to schools or hospitals is measured by the shortest driving time from a location to nearby schools or hospitals. Proximity to bus stops or rail stations is measured by the shortest walking time to surrounding CTA (Chicago Transit Authority) rail stations or bus stops. Location accessibility data, including: street network, distribution of schools, hospitals, CTA bus stops and rail stations in Chicago, are collected from City of Chicago Data Portal under the topic GIS.3 In general, locations that lie in more stable and prosperous neighborhoods (high household income, high educational attainment, low poverty rate, and low unemployment rate) and that are closer to schools, hospitals, bus stops and rail stations are more suitable for developing new public housing projects or for preserving existing ones.

Public Housing Suitability Neighborhood Demographics Poverty Rate Educational Attainment Racial Composition

Neighborhood Economics Median Household Income Unemployment Rate

Location Accessbility Schools Hospitals Public Transportation

Figure 1 Public Housing Location Suitability Model

2

http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

3

https://data.cityofchicago.org/

a) Neighborhood Accessibility When processing the collected data, the first step is to create a network database based on “StreetCenterlines” shapefile downloaded from City of Chicago Data Portal. A new field “Minutes” that represents the walking minutes to rail stations or bus stops is added as the cost of the network. And a new network dataset “BusWalkingTime” is created with the participation of “StreetCenterlines” layer. In the attribute table of “StreetCenterlines”, a field named “Length” represents the length of Street in meters. Since walking speed is set as 3 mph (mile per hour) and 1 mile is approximately equal to 1609 meters, the field “Minutes” is populated with value equal to “Length”/1609/3*60.

Then the Network Analyst Toolbar is used to analyze the proximity to bus stops based on “Minutes” field calculated above. New Service Area function in the toolbar is used to create a new layer in “BusWalkingTime” network dataset. Add Location tool is used to add the “CTA_BusStops” layer, which represents the spatial distribution of bus stops in Chicago City, as the “Facilities” sublayer in the “Service Area” layer. In the Analysis Setting tab within the Properties dialog of “Service Area” layer, impedance is set to “Minutes” and Default breaks are set as 2.5, 5, 7.5, 10, 12.5, 15 mins. After clicking Solve button, polygons individually representing areas within 2.5, 5, 7.5, 10, 12.5, 15 walking minutes from bus stops are showed on the map.

Repeat the above steps for rail stations, and polygons showing areas within 2.5, 5, 7.5, 10, 12.5 and 15 walking minutes from rail stations are created.

For hospitals and schools, all other steps are the same except for that the value of the “Minutes” field in “StreetCenterlines” layer is recalculated as driving time to hospitals or schools, which is equal to “Length”/1609/30*60 since the driving speed limit is set as 30mph in Chicago City. Besides, Default breaks are reset as 5, 10, 15, 20, 15, 30 mins in the Analysis Setting tab within the Properties dialog of “Service Area”.

After having service areas of bus stops, rail stations, schools and hospitals, Polygon to Raster tool is used to transform the Service Areas polygon layers into raster layers. The value field is all set as “ToBreak”, cell size is set as 0.0015 and “CELL_Center” is set as the cell assignment type. Then Reclassify tool is used to assign new values to each cell. In the raster layers for bus stops and rail stations, cells with value of 15 are reclassified with value 1, and

12.5 to 2, 10 to 3, 7.5 to 4, 5 to 5, and 2.5 to 6, which indicates that locations within closer walking distance to bus stops or rail stations have higher suitability score. For raster layers of hospitals and schools, cells with value 30 are reclassified with value 1, and 25 to 2, 20 to 3, 15 to 4, 10 to 5, 5 to 6, which indicates that locations within closer driving distance to hospitals or schools have higher suitability score. b) Neighborhood Demographics and Economics First of all, tables containing information for poverty rate, education attainment, racial composition, median household income and unemployment rate at census tract level are downloaded from the U.S Census Bureau American Factfinder. Then the downloaded tables are joined into the attribute table of the ‘CensusTract2010� layer, which is a shapefile downloaded from City of Chicago Data Portal. Similarly, Polygon to Raster tool is used and five raster layers of each demographic and economic parameter are created. Again Reclassify tool is used to assign new value from 1 to 6 to each cell based on its original value. c) Suitability Score Finally, Raster Calculator tool is used to combine the values of different parameters.

And this calculation process is hierarchical. Firstly, the score of each group is calculated. For example, the score of neighborhood accessibility is equal to the average of hospital, school and public transportation accessibility scores since the same weight is given to these three factors. But before that the score of public transportation accessibility is calculated as the average of CTA bus stop and CTA rail station accessibility score.

Then the total suitability score is equal to the score of neighborhood accessibility *1/2, plus the score of neighborhood demographics *1/4, and plus the score of neighborhood economics*1/4. Areas with the highest score is the most suitable for public housing projects.

2)

Results As is seen from the following maps, the darkest colors show the locations that are most

suitable for developing public housing projects. These locations lie in neighborhoods that are stable and prosperous (high household income, high education attainment, low poverty rate and low unemployment rate). In that way, new public housing projects would not lead to concentrations of poverty and new slums in Chicago. Besides, these locations are close to rail stations and bus stops to reduce the commuting cost for low-income people living in public

housing units. They also provide easier access to education opportunities (schools) and healthcare opportunities (hospitals).

Figure 2 Neighborhood Income Suitability

Figure 3 Neighborhood Poverty Suitability

Figure 4 Neighborhood Accessibility Suitability

Figure 5 Final Suitability Score

Discussion The findings from this research could tell the general public and policymakers the answer to the question – “Where is a good place to build public housing units in Chicago?”

Policymakers could use the public housing suitability map (Figure 5) to help decide the suitable location to construct a new public housing project or to protect an existing one.

Besides, researchers could use the suitability evaluation model constructed in this research (Figure 1) and similar data processing techniques in GIS to identify suitable locations for public housing in other cities.

Bibliography [1] FactFinder, the U.S. Census Bureau http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml [2] Data Portal, City of Chicago, https://data.cityofchicago.org/ [3] Liz Thompson, Affordable Housing Suitability Model, Shimberg Center at the University of Florida, http://www.shimberg.ufl.edu/fl_housingSuitableModel.html [4] Shimberg Center at the University of Florida, Central Florida Regional Planning Council, Affordable Housing Suitability Model For the Florida Heartland, Hearland 2060,http://heartland2060.org/download/affordable_housing/housing_suitability_model_ 2-7-14.pdf [5] Jeff Davidson, Steve Long, Kris Ackerson, Affordable Housing Location Suitability Model, City of Lowa City http://www.iowadot.gov/systems_planning/pdf/TAPE-MPOJC-20130820.pdf