Kate Vickery CRP 388K | Fall 2012

SHRINKING A CITY: VACANT LAND RE-USE IN CLEVELAND, OHIO

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Executive summary

Cleveland, Ohio – along with many other post-industrial rust-belt cities – has a unique problem: how to cope with a shrinking population. The city has experienced a 56% reduction in overall population since 1950, leaving the city with nearly 20,000 neglected vacant lots and a declining need for municipal services. For this study, I look at one of Cleveland’s most unique coping strategies: municipal landbanking. One of the city’s recent strategic planning initiatives, Re-imagining a More Sustainable Cleveland, is focused on engaging communities and private residents to “re-imagine” creative and productive uses for vacant lots.

City staff evaluate citizen applications for re-use of land held in its Land Bank based on a variety of physical and environmental determinations. Projects include small-scale economic development, residential construction, community gardens, public park, and environmental remediation. The impact of these projects could be significant in terms of improving neighborhood quality of life for residents. However, questions remain about in what ways this resource-intensive strategy of neighborhood revitalization is reaching Cleveland’s most vulnerable neighborhoods.

Using the Kirwan Institute for Race and Ethnicity’s opportunity index, I investigated the degree to which different types of re-use projects are well-matched to the underlying social-demographic needs of individual communities. Social “opportunity” indicators considered include education and child welfare; economic opportunity and mobility; housing and neighborhood development; public health; and public safety and criminal justice. Through this analysis, I determined that Cleveland’s methods of dispersing vacant land is better at meeting housing and community development needs than any other social factor, and that approximately 41% of the re-use projects fall within areas that have the lowest social opportunity indicators overall.

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Introduction: Cleveland’s Vacant Land Problem

In her 2011 inaugural address, Baltimore’s new mayor, Stephanie Rawlings-Blake, told supporters, “A shrinking city is a place unable to meet even the most basic needs of its people — basic rights that everyone should expect. A shrinking city simply cannot stand” (Scola, 2012). Rawlings-Blake’s thesis – that a shrinking city is broken and must be repaired through growth – has been the dominant way of understanding the challenge of shrinking cities. I believe that the more pressing issue for shrinking cities should be ensuring that the needs of the existing community are met. In addition, “With an abundance of vacant properties, these shrinking cities provide fertile ground for neighborhood-scale and citywide greening strategies that can revitalize urban environments, empower community residents, and stabilize dysfunctional markets” (Schilling and Logan, 2008, p. 451).

While the City of Cleveland was once an industrial powerhouse – the 10thlargest city in the United State at the turn of the century – the decline of the manufacturing industries upon which its economy was built hit the city hard. By the mid-1970’s, Cleveland was already experiencing double-digit population decline and land vacancy rates (Krumholz, 1990). Today, Cleveland is home to just under 400,000 people, less than half of its peak population in the 1950s (see Figure 1). Like many large urban areas, Cleveland’s population loss has corresponded with the population gain of its suburban areas, as seen in Map 1.

Population decline, environmental disasters, and social unrest has, in many ways, defined the city for the last 60 years. When we think of Cleveland, we might imagine iconic images of the burning Cuyahoga River, race riots in the 1960s, and what many are now calling the “ruin porn” images of the burned-out buildings and vacant land of a prototypical shrinking city (Piiparinen and Trubek, 2012). Most planning literature, popular perception, and even the Mayor of Baltimore consider cities with declining population to be “failures,” even though these cities can be found all over the world and the very preconditions of a global, capitalist marketplace makes this trend inevitable (Schatz, 2010).

Cleveland City Populaon 1820 - 2010 Source: U.S. Census

1,000,000 800,000 600,000 400,000 200,000 0

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Scholars today are just beginning to theorize about the “best” strategies for planning a shrinking city (Schatz, 2010; Schilling 2008, Center for Community Progress). I believe there are several key factors that cities like Cleveland should be taking into consideration. First, leaders of shrinking cities need to accept that their cities are in fact shrinking and adopt policies that benefit the existing population first and foremost. Second, policy makers and planners must rearticulate the problems of being a shrinking city – such as vacant land – as assets. Third, planners must come to terms with the inherent tension between aspiration – the goals for a city – and accountability – meeting the needs of people in the here and now.

The City’s Proposed Solution: Re-imagining a More Sustainable Cleveland

Cleveland began a new initiative in 2008 – the Re-imagining a More Sustainable Cleveland Plan – that is realistic about future growth patterns and focuses in a meaningful way on improving quality of life in the context of a smaller population. The plan focuses on the creative re-use of its abundant stock of vacant land – nearly 3,300 acres in 20,000 lots, 7,500 of which are under City control in Cleveland’s Land Bank. The causes of these vacancy rates are complex and interrelated, including job loss, population loss, housing stock deterioration, tax delinquency, subprime and predatory lending, and mortgage foreclosure (Community Research Partners, 2008, p. iv).

There are two key points to make about Cleveland’s population loss and its current stock of vacant land. Neither population loss nor vacancy rates should be seen as monolithic. Each factor is spatially distributed in meaningful ways around the city, reflecting some important trends in Cleveland’s social-demographic environment. While the City of Cleveland as a whole lost 22% of its population from 1990 to 2010 (Map 1), when we look more deeply at these trends, as in Map 2, we see that the greatest population loss rates are found in the eastern portion of the city, as are the highest concentrations of vacancy rates, as seen in Map 3. The neightborhoods of St. Claire Superior, Hough, Fairfax, Kinsman, Broadway Slavic Village, Buckeye Woodhall, Glenville, and Union-Miles stand out in particular as places with significant challenges in terms of population loss and land vacancy.

Vacancy is a complex problem, of course, because it includes for-sale and for-rent housing units as well as “other vacancies,” which are those most likely to be abandoned completely. Scholars like Schilling and Logan are particularly concerned with areas that have a high “other vacancy” rates as these will require the most concerted efforts by the city to remedy and often contribute to crime and a loss in vitality of residential and commercial areas (Schilling and Logan, 2008, p. 452). Vacancies of this nature also pose a fiscal challenge due to the loss in tax revenue, which can make it difficult to provide city services to these areas (ibid.). A 2008 study found that just three of Cleveland’s “most vacant” neighborhoods cost the city over $35 million in annual demolition and boarding costs; grass and trash services; and tax revenue losses (Community Research Partners, 2008, p. v).

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The Re-imagining Plan is predicated on the fact that population loss is not a trend likely to change in the near future and that the existing vacant land and neighborhoods in which it is located are assets. This in itself is revolutionary, as a key feature of most of Cleveland’s past planning efforts either gloss over population loss completely, are strategizing ways to attract new residents, or are using the language of “blight and slums” as rational to do wholesale clearing of neighborhoods. The tone of the Re- imagining Plan is different than many of these earlier initiatives. “This scenario creates a unique opportunity for Cleveland to re-imagine itself; to build a vibrant, more healthful and more prosperous community that provides a better quality of life for its residents and encourages new residents to call Cleveland home” (“Eight Ideas,” n.d., p. 1).

The Plan is basically this: community groups and individuals can apply for funding or put up their own funding to take ownership of vacant properties which are currently held in the city’s Land Bank, established in 2008 as a part of this initiative. The City of Cleveland Planning Commission staff evaluates the property and the intended use – citizens are encouraged to use the “Ideas to Action Resource Book” for inspiration – and approve or deny the project. The Resource Book offers 10 strategies for residents to consider: vineyards, orchards, market gardens, community gardens, sideyard expansions, street edge improvement, neighborhood pathways, pocket parks, native plantings, and rain gardens (Resource Book, 2011).

Many of the decision factors the city staff is considering include physical and environmental benefits such as proximity to existing greenspaces, stormwater management benefits, renewable energy production, land-assembly, and contamination remediation (“Eight Ideas,” n.d.). Unsurprisingly, the project ideas that seem to get the most media attention are those that have public benefit, like community gardens and parks (Re-imagining Cleveland website).

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Problem Statement

The city has evaluated approximately 1,100 individual applications since 2008 from individuals and businesses wanting to re-use a vacant lot, most of which are currently held in the Cleveland City Land Bank. Eight-hundred and fifty-seven of those applications have been approved or were under review as of October 25, 2012. The most common types of projects approved by City staff can be seen in Figure 2, below.

Cleveland Vacant Land Re-use Projects Approved or under review 2008 - 2012

7% 16% 8% Commercial Improvements & New Construcon

Residenal Improvements & New Construcon 10% Yard Expansion

Urban Gardens

Playgrounds/Open Space 59%

What stands out immediately is how few of the approved projects fall under the “public use” category. Even within the urban garden category, only a few of those are community gardens; many are on private land. Particularly notable is the extraordinary number of yard expansion projects, which are intended for “stabilization” purposes in areas that have development potential in the future. Existing landowners take title to adjacent lots and the property owner or a community partner installs and maintains low-maintenance landscaping techniques in order to help stabilize property values in the neighborhood. The fact that more of this land seems to be being re-purposed for private use led me to wonder to what extent vacant land re-use projects were making a difference in the lives of the people who lived near them.

Cleveland hopes to gain benefits like “cleaner air and water, greater access to parks and recreation, improved local food security, and neighborhood-based economic development”1 from the Re-imagining plan. This brief study will add an additional layer of analysis through looking at social demographic needs of neighborhoods. I believe that the most vulnerable neighborhoods should be receiving the vast majority of this type of redevelopment effort.

1 Ibid, 3 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 7

The questions framing this study are threefold:

• What are the types and spatial distribution of vacant land redevelopment projects in Cleveland, Ohio? • In what ways are these projects related to indicators of neighborhood opportunity/vulnerability? • Can we begin to draw conclusions about the patterns of redevelopment through landbanking throughout the city?

My overall hypothesis is that the re-use projects being approved by the City are not treating the greatest social needs. While I realize that a small vacant land re-use project is not a cure-all for Cleveland’s complex social inequalities, these types of projects do have the potential to create meaningful changes.

Methodology

Data Acquisition To get at these questions, I needed to examine social demographic trends in specific neighborhoods and determined whether the types of re-use projects were a good “treatment” for the needs of the given community. I needed two primary types of data: the actual disposition data from the city’s land-banking project and a measure of social need or vulnerability in specific geographic areas. The City of Cleveland generously supplied the data from its land-banking initiative, which includes information about the applicant, the review process, and the eventual recommendation from city staff.

While I considered creating my own social vulnerability index for this project, I was apprehensive about being able to create a comprehensive enough model over the course of a semester. I approached the Kirwan Institute for Race and Ethnicity at The Ohio State University as their methods have been used in a number of excellent studies, and they have recently completed a state-wide opportunity mapping project on Ohio (Kirwan Institute, 2010). The Institute generously shared their opportunity index with me, which I will discuss in more detail shortly.

I also needed information about the city’s current land use structure in order to isolate the total stock of vacant land as well as population change data in order to set the stage about the spatial distribution of both of these trends. I obtained land use data from the City of Cleveland’s planning department, and supplemental demographic information, including population data from 1990 and 2010 from Social Explorer, a great source for U.S. decennial census data, as well as the annual American Community Survey.

Matching Needs to Treatments The Kirwan Index is made up of five indicators: education and child welfare; economic opportunity and mobility; housing and neighborhood development; public health; public safety and criminal justice. The aspects underlying each of those indicators can be seen in Table 1 below; generally, a higher number on each scale indicates higher shrinking a city | kate vickery | CRP 388K | Fall 2012 | 8

“opportunity” in that area. Please see the Appendix for more details about the Index and its underlying data.

The Kirwan institute uses the composite score of these five indicators to determine an area’s opportunity rating, on a scale of very low to very high. I chose to “unpack” the Kirwan Institute’s Opportunity Index into its sub-parts (needs) and then match the re- use projects (treatments) to these indicators, using the City’s disposition data. My method for doing this matching was based mostly on common sense and my own understandings of the benefits of certain times of land-uses. Certain re-use projects fall into multiple categories, particularly parks/open space and community gardens, which I believe have the widest array of community benefits because they have a direct impact on the sense of ownership people have over the places they live. My matching strategy can also be seen in Table 1, below.

Table 1: Kirwan Institute Opportunity Index and Matched Land Use Treatments

Indicator Aspects of Indicator Well Matched Land Use Treatments Comprehensive Score education and child welfare; economic all disposition projects opportunity and mobility; housing and neighborhood development; public health; public safety and criminal justice. Education & Child educational attainment; student Playground/Open Space Welfare poverty rate, student performance; disciplinary actions per 100 students; teacher qualifications; education quality; expenditure per pupil; dropout rate; student/teach ratio; proximity to libraries; graduation rate Economic unemployment rate; populuation on Comml./Indust. Expansions; Comml./Indust. Opportunity & publi assistance; proximity to New Construction; Comml./Indust. Parking Mobility employment (5 miles); economic Lots; Institutional Expansion (non-parking); climate; commute time Institutional Parking (non-church); Market garden; Church parking; New Institutional Housing & home values; housing vacancy rates; Yard expansions; New Other Residential Neighborhood/Comm home ownership rates; neighborhood Construction; New Single-Family Const. (1-lot); unity Development poverty New Single Family Const. (multi-lot); Parking for Residential (MF); Playground/Open Space; Community garden Public Health number of health clinics in a Community Garden; Garden (not adj. property neighborhood; access to health clinics; owner - stand alone); market garden; seasonal number of grocery stores; access to garden; playground/open space grocery stores Public Safety & adult-based incarceration rate; crime Playground/Open Space; community garden Criminal Justice index from Ohio Dept. of Public Safety

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One important caveat here is that despite my earlier assertion that “yard expansions” do not have an explicit public purpose, the purpose of these projects is to stabilize land values, which is directly related to the home values of Clevelanders. I put this category of re-use project into the Housing & Neighborhood/Community Development for this purpose. It is worth noting here, however, that this type of project is disproportionately represented within the sample of re-use projects. While beyond the scope of this study, it would be interesting to even further isolate classes of re-use projects given their cost/benefit, long-term value, public participation, number of people impacted, and other factors.

Throughout my study, there are two primary units of analysis: census tracts and statistical planning areas (SPA). There are 36 statistical planning areas in the city, which are what we think of as being “neighborhoods.” I use SPAs throughout my analysis as I am interested in trends in neighborhoods. For census tract data, I symbolize data without distinct boundaries so it is easier to see trends within SPAs without focusing on the specific census tract boundaries.

Data Processing Setting the stage First, I needed to set the stage by showing the spatial distribution of population decline and vacancy rates in the city, as these are the defining characteristics of Cleveland for the purposes of this study.

• Map 1: Municipal population change in Cuyahoga County from 1990 – 2010 o Used decennial census data per municipality in Cuygahoga county o Calculated population change over the 20 year period for each municipality

• Map 2: Population change from 1990 and 2010 within the City of Cleveland o Used decennial census data per census tracts in Cuyahoga County o Intersected 1990 census and 2010 census shapefiles o Calculated the approximate number of people in each intersected parcel in 1990 and 2010 o Dissolved the intersection back into 2010 census tracts and calculated population change within the 2010 tracts census tract

• Map 3: Vacancy rates o Used ACS 2005-2010 data to symbolize a map of vacant units/total housing units Honing in on the Problem Next, I needed to determine where there were relationship between vacant land and re- use projects generally.

• Map 4: Conducted a hotspot analysis within statistical planning areas of vacant land parcels and location of re-use projects and overlapped these analyses in shrinking a city | kate vickery | CRP 388K | Fall 2012 | 10

order to see which neighborhoods are seeing the greatest overlap of need (vacancy as defined by the city’s land use data) and treatment (re-use projects). Glenville, St. Claire Superior, Broadway Slavic Village, and Kinsman stood out as having significant overlap.

• Map 5: In order to get a sense of what the scale of vacant land looks like spatially, I created a map of all vacant parcels (from the city’s land-use data) and the available land-bank land.

• Map 6: It is difficult to understand the scale of the vacancy at the city scale, so I chose the Glenville neighborhood to do a zoom-in on the concentration of vacant lots and the re-use projects (all types) in the neighborhood. I chose this neighborhood because it has greatest number of re-use projects (104) and the second highest number of vacant parcels (1497). Matching Needs to Treatments Using the matching strategy I developed, as seen in Table 1, I created a series of maps showing the spatial relationship between each social opportunity indicator and its matched re-use treatments using a simple Geographic Weighted Regression model.

• Map 7: To start, I wanted to get a sense of the comprehensive opportunity index and its relationship to the total number of re-use projects, regardless of their type. o Spatially joined total re-use project shapefile to the opportunity index layer, creating a total count for the number of re-use projects in each census tract (the unit of analysis for the Kirwan Index) o Did a Geographically Weighted Regression using the count of re-use projects as the dependent variable, and the opportunity index score as the independent variable. o Obtained an overall R2 value to determine how well the independent variable explained the variance in the dependent variable. o Highlighted the tracts that were the ‘best fit’ in terms of the model by isolating those whose regression residuals fell beyond two standard deviations from the mean. o Calculated the number of these matched re-use projects that fell within the ‘lowest’ class of the opportunity index indicator to use a reference in my analysis.

• Maps 8-12: Selected the re-use point data for which the proposed land-use (the use that the city considered in its review) matched the underlying social opportunity need, as seen in Table 1. For each social opportunity index, I conducted the same type of analysis as above, using the opportunity index score for a given variable (independent variable) and the count of matched re-use projects (dependent variable). I obtained an R2 and the percentage of projects that fall within the ‘lowest’ class of comprehensive opportunity.

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• Map 13: Finally, I zoomed back into the Glenville neighborhood, which stood out for its number of vacant lots and re-use projects, and also had two significant areas of high ‘match’ on the housing & neighborhood/community development scale. o Clipped all of the classes of dispositions to the Glenville statistical Planning Area and added the layers from the housing regression analysis and housing opportunity index.

Findings

In this section, readers will find 13 maps, organized as follows.

Setting the Stage

• Map 1: Municipal Population Change | Cuyahoga County | 1990 - 2010 • Map 2: Cleveland Population Change 1990 – 2010 (census tracts) • Map 3: Vacancy Rages | ACS 5-year Estimates (2005-2010)

Honing in on the Problem

• Map 4: Spatial Patters in Location of Vacant Land and Re-use Projects • Map 5: 2012 Stock of Vacant Land + Available Land Bank Land • Map 6: Glenville Neighborhood | Concentration of Vacancy and Re-use

Matching Needs to Treatments

• Map 7: Matching needs to treatments: Comprehensive Opportunity + All re-use projects • Map 8: Matching needs to treatments: Education & Child Welfare • Map 9: Matching needs to treatments: Economic Opportunity & Mobility • Map 10: Matching needs to treatments: Housing & Neighborhood Development • Map 11: Matching needs to treatments: Public Health • Map 12: Matching needs to treatments: Public Safety & Criminal Justice • Map 13: Glenville Neighborhood | Needs and Treatments | Housing

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VACANT LAND RE‐USE IN CLEVELAND, OHIO MUNICIPAL POPULATION CHANGE | CUYAHOGA COUNTY | 1990 ‐ 2010

CITY OF CLEVELAND (‐22%)

municipal population change ‐46% ‐ ‐20% ‐19% ‐ ‐5% ‐4% ‐ 5% 6% ‐ 25% 26% ‐ 103%

Lake Erie

OHIO

NORTH SHORE VACANT LAND RE‐USE IN CLEVELAND, OHIO COLLINWOOD POPULATION CHANGE 1990 ‐ 2010

COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

GLENVILLE

ST.CLAIR SUPERIOR

GOODRICH KIRTLAND PARK HOUGH UNIVERSITY DOWNTOWN

FAIRFAX CENTRAL

EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT SLAVIC Population Change STOCKYARDS VILLAGE 1990‐2010 Census Tracts JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD ‐100% to ‐40% KAMM'S ‐39% to ‐20%

‐19% to 0% LEE‐SEVILLE OLD BROOKLYN 1% to 170% BELLAIRE‐PURITAS

HOPKINS Lake Erie

Cleveland

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO VACANCY RATES | ACS 5‐YEAR ESTIMATES (2005‐2010)

COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

GLENVILLE

ST.CLAIR SUPERIOR

GOODRICH KIRTLAND PARK HOUGH UNIVERSITY DOWNTOWN

FAIRFAX CENTRAL

EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT STOCKYARDS SLAVIC vacancy rate VILLAGE vacant units/total units JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD 33% ‐ 52% KAMM'S 23% ‐ 32%

13% ‐ 22% LEE‐SEVILLE OLD BROOKLYN 0% ‐ 12% BELLAIRE‐PURITAS no data

Lake Erie HOPKINS Cleveland

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO SPACIAL PATTERNS IN LOCATION OF VACANT LAND AND RE‐USE PROJECTS COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

GLENVILLE

ST.CLAIR SUPERIOR

GOODRICH KIRTLAND PARK HOUGH UNIVERSITY DOWNTOWN

FAIRFAX CENTRAL re‐use project hotspot z‐score ‐1.67 ‐ 1.65 EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE 1.66 ‐ 2.32 SHOREWAY WOODHILL SHAKER KINSMAN SQUARE vacant land hotspot z‐score CUDELL TREMONT < ‐2.58 Std. Dev. CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT ‐2.58 ‐ ‐1.96 Std. Dev. SLAVIC STOCKYARDS VILLAGE ‐1.96 ‐ ‐1.65 Std. Dev. JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD ‐1.65 ‐ 1.65 Std. Dev. KAMM'S 1.65 ‐ 1.96 Std. Dev.

1.96 ‐ 2.58 Std. Dev. LEE‐SEVILLE OLD BROOKLYN > 2.58 Std. Dev. BELLAIRE‐PURITAS

Lake Erie HOPKINS

Cleveland

NORTH SHORE VACANT LAND RE‐USE IN CLEVELAND, OHIO COLLINWOOD 2012 STOCK OF VACANT + AVAILABLE LAND BANK LAND

COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

GLENVILLE

ST.CLAIR SUPERIOR

GOODRICH KIRTLAND PARK HOUGH UNIVERSITY DOWNTOWN

FAIRFAX CENTRAL

EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT STOCKYARDS SLAVIC parcels available for re‐use VILLAGE JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD KAMM'S

LEE‐SEVILLE OLD BROOKLYN BELLAIRE‐PURITAS

Lake Erie HOPKINS Cleveland

Kate Vickery | November 12, 2012 CUYAHOGA Sources: US Census, Cuyahoga County, City of Cleveland [ 012340.5 Miles Projection: NAD 1983 State Plane Ohio North FIPS 3401 map 6 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 17 VACANT LAND RE‐USE IN CLEVELAND, OHIO GLENVILLE NEIGHBORHOOD | CONCENTRATION OF VACANCY AND RE‐USE

ue en Av ire Cla St. 0t Street 105th

L a k e v ie w R o a d

Avenue Superior

Cleveland vacant land re‐use projects Kate Vickery | December 12, 2012 Sources: US Census, Cuyahoga County, City of Cleveland vacant and Cleveland land bank parcels Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 00.125 0.25 0.5 Miles map 7 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 18

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING SOCIAL OPPORTUNITY AND VACANT LAND RE‐USE COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

GLENVILLE

ST.CLAIR A simple Geographically Weighted Regression (GWR) model SUPERIOR was used to determine the "fitness" of the relationship between GOODRICH comprehensive opportunity index (independent variable) KIRTLAND PARK HOUGH and the number of re‐use projects (dependent variable). UNIVERSITY Areas outlined in purple have the strongest relationship DOWNTOWN between these two factors. FAIRFAX The GWR model has an overall R‐squared value of .65. CENTRAL Approx. 41% of the re‐use projects fall within the lowest EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE economic and mobility opportunity areas. SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA FULTON VALLEY vacant land re‐use project WEST BOULEVARD BROADWAY MOUNT PLEASANT STOCKYARDS SLAVIC Geographically Weighted Regression Residuals VILLAGE JEFFERSON BROOKLYN CENTRE best match (<2.0) UNION‐MILES LEE‐HARVARD KAMM'S social opportunity index rating lowest LEE‐SEVILLE low OLD BROOKLYN BELLAIRE‐PURITAS moderate highest

HOPKINS Lake Erie

Cleveland Kate Vickery | December 10, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwin Institute [ CUYAHOGA 012340.5 Miles Projection: NAD 1983 State Plane Ohio North FIPS 3401 map 8 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 19

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING NEEDS TO TREATMENTS ‐ EDUCATION & CHILD WELFARE COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

A simple Geographically Weighted Regression (GWR) model GLENVILLE

was used to determine the "fitness" of the relationship between ST.CLAIR education & child welfare opportunity (independent variable) SUPERIOR

and the number of re‐use projects (dependent variable) that GOODRICH KIRTLAND are well‐suited to having an impact on this social indicator. PARK HOUGH Areas outlined in purple have the strongest relationship UNIVERSITY DOWNTOWN between these two factors. FAIRFAX The GWR model has an overall R‐squared value of .06. CENTRAL

Approx. 41% of the re‐use projects fall within the lowest EDGEWATER DETROIT OHIO CITY BUCKEYE BUCKEYE housing & community development opportunity areas. SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT re‐use project with child welfare focus SLAVIC STOCKYARDS VILLAGE Geographically Weighted Regression Residuals JEFFERSON BROOKLYN CENTRE best match (>2.0) UNION‐MILES LEE‐HARVARD KAMM'S Education & Child Welfare Opportunity lowest LEE‐SEVILLE OLD BROOKLYN low BELLAIRE‐PURITAS medium highest

HOPKINS Lake Erie

Cleveland Kate Vickery | December 10, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwin Institute CUYAHOGA Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 012340.5 Miles map 9 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 20

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING NEEDS TO TREATMENTS ‐ ECONOMIC OPPORTUNITY & MOBILITY

COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

A simple Geographically Weighted Regression (GWR) model GLENVILLE was used to determine the "fitness" of the relationship between ST.CLAIR economic & mobility opportunity (independent variable) SUPERIOR and the number of re‐use projects (dependent variable) that GOODRICH are well‐suited to having an impact on this social indicator. KIRTLAND PARK HOUGH Areas outlined in purple have the strongest relationship UNIVERSITY between these two factors. DOWNTOWN

FAIRFAX The GWR model has an overall R‐squared value of .24. CENTRAL Approx. 24% of the re‐use projects fall within the lowest EDGEWATER economic and mobility opportunity areas. DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT Re‐use project w/ economic purpose CLARK CUYAHOGA FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT Geographically Weighted Regression Residuals SLAVIC STOCKYARDS VILLAGE best fit (>2.0) JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD Economic & Mobility Opportunity KAMM'S lowest

low LEE‐SEVILLE OLD BROOKLYN medium BELLAIRE‐PURITAS highest

Lake Erie HOPKINS Cleveland Kate Vickery | December 10, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwin Institute CUYAHOGA Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 012340.5 Miles map 10 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 21

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING NEEDS TO TREATMENTS ‐ HOUSING & NEIGHBORHOOD DEVELOPMENT COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

A simple Geographically Weighted Regression (GWR) model GLENVILLE was used to determine the "fitness" of the relationship between ST.CLAIR SUPERIOR housing & neighborhood opportunity (independent variable) GOODRICH and the number of re‐use projects (dependent variable) that KIRTLAND are well‐suited to having an impact on this social indicator. PARK HOUGH UNIVERSITY Areas outlined in purple have the strongest relationship DOWNTOWN between these two factors. FAIRFAX CENTRAL The GWR model has an overall R‐squared value of .46. EDGEWATER Approx. 37% of the re‐use projects fall within the lowest DETROIT OHIO CITY BUCKEYE BUCKEYE housing & community development opportunity areas. SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA re‐use project w/ housing purpose FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT SLAVIC Geographically Weighted Regression Residuals STOCKYARDS VILLAGE

best match (>2.0) JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD Housing & Neigborhood Opportunity KAMM'S lowest

low LEE‐SEVILLE OLD BROOKLYN medium BELLAIRE‐PURITAS highest

HOPKINS Lake Erie

Cleveland

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING NEEDS TO TREATMENTS ‐ PUBLIC HEALTH COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

A simple Geographically Weighted Regression (GWR) model GLENVILLE was used to determine the "fitness" of the relationship between ST.CLAIR SUPERIOR public health opportunity (independent variable) and the number GOODRICH of re‐use projects (dependent variable) that are well‐suited to KIRTLAND having an impact on this social indicator. Areas outlined in purple PARK HOUGH UNIVERSITY have the strongest relationship between these two factors. DOWNTOWN

The GWR model has an overall R‐squared value of .15. FAIRFAX CENTRAL Approx. 10% of the re‐use projects fall within the lowest EDGEWATER housing & community development opportunity areas. DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA re‐use project w/ health purpose FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT SLAVIC Geographically Weighted Regression Residuals STOCKYARDS VILLAGE

best fit (>2.0) JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD Public Health Opportunity KAMM'S lowest

low LEE‐SEVILLE OLD BROOKLYN medium BELLAIRE‐PURITAS highest

HOPKINS Lake Erie

Cleveland Kate Vickery | December 2, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwin Institute CUYAHOGA Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 012340.5 Miles map 12 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 23

NORTH SHORE COLLINWOOD VACANT LAND RE‐USE IN CLEVELAND, OHIO MATCHING NEEDS TO TREATMENTS ‐ PUBLIC SAFETY & CRIMINAL JUSTICE COLLINWOOD NOTTINGHAM

EUCLID‐GREEN

A simple Geographically Weighted Regression (GWR) model GLENVILLE was used to determine the "fitness" of the relationship between ST.CLAIR public safety opportunity (independent variable) SUPERIOR

and the number of re‐use projects (dependent variable) that GOODRICH are well‐suited to having an impact on this social indicator. KIRTLAND PARK HOUGH Areas outlined in purple have the strongest relationship UNIVERSITY between these two factors. DOWNTOWN

FAIRFAX The GWR model has an overall R‐squared value of .09. CENTRAL Approx. 18% of the re‐use projects fall within the lowest EDGEWATER housing & community development opportunity areas. DETROIT OHIO CITY BUCKEYE BUCKEYE SHOREWAY WOODHILL SHAKER KINSMAN SQUARE CUDELL TREMONT CLARK CUYAHOGA re‐use project w/ safety implication FULTON VALLEY WEST BOULEVARD BROADWAY MOUNT PLEASANT SLAVIC Geographically Weighted Regression Residuals STOCKYARDS VILLAGE

best match (>2.0) JEFFERSON BROOKLYN CENTRE UNION‐MILES LEE‐HARVARD Public Safety Opportunity KAMM'S lowest

low LEE‐SEVILLE OLD BROOKLYN medium BELLAIRE‐PURITAS highest

HOPKINS Lake Erie

Cleveland Kate Vickery | December 10, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwin Institute CUYAHOGA Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 012340.5 Miles map 13 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 24

VACANT LAND RE‐USE IN CLEVELAND, OHIO GLENVILLE NEIGHBORHOOD | NEEDS AND TREATMENTS | HOUSING

e nu ve e A lair . C St 0t Street 105th

L a k e v ie w R o a d re‐use project w/ housing purpose all vacant land re‐use projects vacant and Cleveland land bank parcels Avenue Superior Geographically Weighted Regression Residuals best match (>2.0) Housing & Neighborhood Opportunity lowest

low Cleveland medium Kate Vickery | December 12, 2012 Sources: US Census, Cuyahoga County, City of Cleveland, Kirwan Institute highest Projection: NAD 1983 State Plane Ohio North FIPS 3401 [ 00.125 0.25 0.5 Miles shrinking a city | kate vickery | CRP 388K | Fall 2012 | 25

Analysis

As we can see in Cleveland’s population change from 1990 to 2010 (Map 2) the vacancy rate map (Map 3), and the vacant land stock map (Map 5) there are easily recognizable spatial trends in the challenges of vacancy and population loss. East Cleveland has the highest concentration of vacancy and population loss, as do some of the south-central neighborhoods like Tremont, Clark Fulton, and Stockyards.

While these trends may be apparent to the casual observer, a more robust analysis is needed to determine where the significant clusters of vacant land can be found. The hotspot analysis (Map 4) uses a simple z-test of the agerage distance between vacant parcels to determine which clusters were not the result of random chance. It is important to note here that the presence of neighboring vacant land has a greater effect on the creation of a hot-spot, which is why the neighborhoods in east Cleveland are particularly unlikely to be the result of random chance. The hotspot analysis on the location of re-use projects tells us that the city is doing the “best” job at using re-use projects to address vacancy issues in Glenville, St. Claire Superior, Broadway Slavic Village, and Kinsman.

Zooming in to look at Glenville, one of the neighborhood with the strongest relationship between the presence of re-use projects and the prevalence of vacant land is a stark reminder of the sheer scale of the vacancy problem for Cleveland. While over 100 re- use projects have been undertaken in Glenville, there are nearly 1,500 individual parcels either currently vacant, or available for re-use through the city’s land bank.

Absorbing all of the information from the Geographically Weighted Regression (GWR) maps is somewhat overwheleming. Table 2, below, summarizes the highlights in terms of each matching-model’s overall R2 value, an estimate of the percent of the variation in the dependent variable (number of re-use projects) can be attributed to the independent variable (the social indicator score). A higher value indicates that the model is better at explaining variation in the dependent variable.

Table 2: Results of Geographically Weighted Regression Analysis &

Number of Total % of projects projects in lowest “matched” located in lowest Indicator R2 opportunity class projects opportunity class Education & Child Welfare 0.06 24 58 41% Economic Opportunity & Mobility 0.24 34 138 25% Housing & Neighborhood Development 0.46 241 654 37% Public Health 0.15 13 126 10% Public Safety & Criminal Justice 0.09 13 69 19% Comprehensive Score 0.65 347 845 41%

Overall, the number of re-use projects the city is undertaking in each census tract is significantly related to the to the comprehensive social opportunity scores. Nearly 65% shrinking a city | kate vickery | CRP 388K | Fall 2012 | 26

of the variation in the number of re-use projects can be attributed to the opportunity ranking, and over 40% of all of the city’s re-use projects fall within those census tracts that are ranked “lowest” on the overall social opportunity index. I find this result encouraging.

When we look more deeply at the relationships between the individual indicators and their ‘matched’ re-use projects, we see some more nuanced results. First, the housing & community development indicator model is the strongest of the individual GWRs, indicating that there is a relatively strong relationship between the social need and the applied treatment. However, only 37% re-use projects matched with this indicator fall within the lowest-performing areas in terms of housing & neighborhood development need. Realistically, this GWR model is the only one for which the R2 is strong enough to be able to draw significant conclusions about the relationship.

Within each of these maps, we can see which areas (outlined in purple) have the “best fit” in terms of matching needs to treatments, but overall, looking at the percentage of projects located in the most at-risk areas is a better indicator for the education & child welfare, public health, and public safety & criminal justice indicators. Here we see that 41% of the matched education & child welfare projects are located in the lowest performing areas on this indicator. Projects in this category include public playgrounds and open space.

The result that I find most disappointing is the public health indicator. Well-matched projects to this indicator include public and private gardens, which encourage good nutrition, as well as playgrounds and openspace, which encourage exercise. While the city is touting this program as being mostly about these kinds of projects, only 126 projects, 15% of all of the projects, fall into this category, and only 10% of those are located in areas that score lowest on the public health opportunity index.

This limited study has shown that the city’s current strategy is doing a pretty good job at making an impact on housing and neighborhood opportunity indicators through the “right” types of re-use projects happening in those areas. Overall, it is locating many of its projects in this lowest performing areas. The city could be making more of a concerted effort, however, to place projects in areas that would benefit from these kinds of projects, especially in terms of public health, public safety, and education, and in certain areas of housing as well. When we look at the housing and neighborhood development indicator in Glenville alone (Map 13),for example, we can see that two of the lowest performing areas in the northwest and southeast corners of the neighborhood are not receiving very many re-use projects, while several census tracts that have a high number of projects are located in the highest performing class on this indicator scale.

There are several important caveats to this study.

• I am aware that these maps are missing some of the traditional components that would help a reader relate a simple map to a location on the grou . Features like

nd shrinking a city | kate vickery | CRP 388K | Fall 2012 | 27

parks, major highways and water bodies – the Cuyahoga Valley is almost entirely flood plain, for example, but one can’t see this in these maps – would help a read think spatially about how this data relates to actual “ground-truthed” places. Unfortunately, the City’s shapefiles for much of this data were defined and projected in such a way that I was never able to use them and my contact at the city was unable to assist. On the other hand, the purpose of this analysis is to think holistically about spatial trends of demographic data, and so the abstractness of these maps is useful for visualization purposes.

• The list of re-use projects obtained by the city is not 100% comprehensive, as there are several non-profit partners that are also administering grants for the program and I am not clear on whether every proposal gets recorded in the city’s records. I don’t believe that the number of projects completed through other entities are numerous, however.

• The Kirwan Institute’s Opportunity Index is based on data from 1999 – 2011, which means that these indicators are not as current as the land bank disposition data used in this study. In particular, the public health indicator is based on ESRI business data from 1999, and one has to imagine that there may have been changes in the number of health clinics and/or grocery stores in certain areas since then.

• Using Geographically Weighted Regression for this analysis may not actually be the best method for this type of analysis, especially because I had not run robust OLS regressions on my data before embarking on the GWR technique, which is advised. I have obviously not captured all of the possible independent factors that determine the dependent outcome (the number of projects in each census tract). It would be interesting for a future analysis to create a robust model that includes more possible independent variables. In addition to the opportunity index, variables could include financial considerations, development potential of a particular piece of land, and some sort of “influence” factor for the person applying for the use project (e.g. does having being wealthier or having a personal relationship with a planning commissioner influence the recommendation to approve or deny?). Putting more independent variables 9into the model would have allowed me to get a sense of which factors are most significant in predicting the number of re-use projects in a given area. As it stands, my only purpose here was to test the ‘fitness’ of a single relationship, but there is much more that could be done using this technique and data.

Conclusion

I would like to conclude with one final caveat to the methods of my analysis. In general, indices like the Kirwan Institute’s should be used and interpreted with caution. The Opportunity Index’s greatest strength is also its greatest weakness – though its ability to show us trends in social opportunity and mobility, it overly aggregates complex shrinking a city | kate vickery | CRP 388K | Fall 2012 | 28

social characteristics in a way that ignores diversity. The strength of a neighborhood may be the presence of a few highly motivated charismatic community leaders or an incredible afterschool program, for example, but we can’t see these subtleties within the Opportunity Index.

This type of aggregation can be used to over-simplify a “place” and lead to the stereotypes that plague many American rust-belt cities, making it more difficult for them to be able to envision a more sustainable or creative future for themselves. Labeling a place “ghetto” is an easy way to effectively disempower an entire community with one fell swoop. A recent article in The Atlantic Cities sums this phenomenon up well.

“Ruin!” versus “Revival!” narratives are mesofacts— broad, flexible yet significant beliefs about places or peoples. Mesofacts influence perceptions, which drive behaviors, which affect how places are cared for, praised, derided, or left for dead. When we package the idea of a Rust Belt death and rebirth with superficial themes, people on the ground are left to clean up the mess (Piiparinen and Trubek, 2012).

The Re-Imagining a More Sustainable Cleveland plan has the potential to revolutionize the way that the city strategizes planning efforts in the context of being a shrinking city. By adopting policies that benefit the existing population first and foremost, rearticulating the problem of vacant land as an asset, and being intentional with the obligation to be aspirational and forward thinking and remaining accountable to the needs of current residents, planners can really make a difference in Cleveland. I would encourage the City to begin considering the kinds of social-demographic trends observed by the Kirwan Institute’s opportunity index when disposing of its vacant land in order to further its mission to create lasting, community benefits.

Additional Research

I obtained a significant amount of data that I did not end up using in this relatively simple study. As a next step, I would be interested in investigating the extent to which the disposition of the vacant parcels is in accordance with the future land use plan envisioned by the City’s 2020 Comprehensive Plan. I hypothesize that some of the mechanisms may really be used only as a temporary holding pattern until “real development” is available in certain areas. In order to do this, I would need data about the future land-use plan for the city, detailed data about the land banks current stock of land and it’s “development potential.” I have both of these items currently, as well as other information about the areas of Cleveland that the 2020 comprehensive plan has identified as “hot spots” for certain types of development. This research could become part of my professional report.

shrinking a city | kate vickery | CRP 388K | Fall 2012 | 29

References

Cleveland City Planning Commission. “Eight Ideas for Vacant Land Re-use in Cleveland.” Available: http://planning.city.cleveland.oh.us/ftp/8IdeasForVacantLandReuseCleveland.p df Center for Community Progress. (2012). Available: http://www.communityprogress.net/ City of Cleveland. (n.d.). “City of Cleveland Census Geography.” http://planning.city.cleveland.oh.us/census/geography.html

Cleveland Land Lab at the Cleveland Urban Design Collaborative (2008). “Re-Imagining a More Sustainable Cleveland: Citywide Strategies for Reuse of Vacant Land.” (Re-Imagining). Available: http://reimaginingcleveland.org/about/links-and- resources/ Kent State University’s Cleveland Urban Design Collaborative; Neighborhood Progress, Inc. (2011). “Re-imagining Cleveland: Ideas to Action Resource Book.” Available: http://reimaginingcleveland.org/files/2011/03/ideas-to-action-white-layout- for-printing.pdf Kirwan Institute for Race and Ethnicty. (2010, March 10). “The State of Black Ohio 2010,” Columbus, OH: The Ohio State University. Available: http://kirwaninstitute.osu.edu/the-state-of-black-ohio-2010/ Community Research Partners; Rebuild Ohio. (2008, February). $60 Million and Counting: The cost of vacant and abandoned properties to eight Ohio cities. Columbus, OH. Available: http://www.greaterohio.org/files/policy- research/execsummary.pdf Jacobs, J. (1961). The death and life of great American cities. New York: Vintage Books. Krumholz, N. and J. Forester (1990). Making equity planning work: Leadership in the public sector, Philadelphia: Temple University Press.

Re-imagining Cleveland. (2012). http://reimaginingcleveland.org/ Schilling, J., & Logan, J. (2008). Greening the Rust Belt: A Green Infrastructure Model for Right Sizing America’s Shrinking Cities. Journal Of The American Planning Association, 74(4), 451-466. Doi:10.1080/01944360802354956 Scola, N. (2012, November 19). “The rise of the new Baltimoreans,” Next American City. Retrieved from http://americancity.org/daily/entry/the-rise-of-the-new- baltimoreans Schatz, L.K. (2010). What helps or hinders the adoption of “good planning” principles in shrinking cities? A comparison of recent planning exercises in Sudbury, Ontario and Youngstown, Ohio (Unpublished doctoral dissertation). University of Waterloo, Ontario. shrinking a city | kate vickery | CRP 388K | Fall 2012 | 30

APPENDIX

shrinking a city | kate vickery | CRP 388K | Fall 2012 | 31

Data Acquisition & Sources

Population Data – This data was collected from a number of sources. Municipal Population for Cuyahoga Communities. Cleveland, OH: Cuyahoga County Planning Commission (2010). http://planning.co.cuyahoga.oh.us/census/population.html 1990 Decennial Census Data [computer file]. Washington, DC: Social Explorer, 2012. 2010 Decennial Census Data [computer file]. Washington, DC: Social Explorer, 2012. American Community Survey 2005-2010 [computer file]. Washington, DC: Social Explorer, 2012. Basic geospatial data – This data was collected from a number of sources. TIGER shapefile, Cuyahoga Census Tracts [computer file]. Washington, DC: U.S. Census, 2010. Great Lakes Polygon [computer file]. Austin, TX: Jonathan Ogren, 2012. TIGER shapefile, State of Ohio [computer file]. Washington, DC: U.S. Census, 2010. TIGER shapefile, Ohio Counties [computer file]. Washington, DC: U.S. Census, 2010. Cuyahoga County Boundary [computer file]. Cleveland, OH: Cuyahoga County Planning Commission, 2012. Cuyahoga County Municipal Boundaries [computer file]. Cleveland, OH: Cuyahoga County Planning Commission, 2012. Opportunity Index - GIS Specialist, Matthew Martin, provided this data and the accompanying documentation. I communicated with him via email and phone; contact information available upon request. Opportunity Index [computer file]. Columbus, OH: Kirwan Institute for Race and Ethnicity at The Ohio State University, 2010. City of Cleveland Data – GIS Specialist Kristofer Lucskay, provided this data and the accompanying documentation. I communicated with him via email and phone; contact information available upon request. Land Bank Disposition Data as of 7/1/12 [computer file]. Cleveland, OH: City of Cleveland Planning Commission, 2012. Available Land Bank Parcels as of October, 2012 [computer file]. Cleveland, OH: City of Cleveland Planning Commission, 2012. Current Land Use [computer file]. Cleveland, OH: City of Cleveland Planning Commission, 2012. Streets [computer file]. Cleveland, OH: City of Cleveland Planning Commission, 2012. shrinking a city | kate vickery | CRP 388K | Fall 2012 | 32

Detailed Data Processing Steps Map 1: Municipal Population Change | Cuyahoga County | 1990 - 2010 • Used municipality shapefile • Copied Town_ID field from municipality shapefile into population database to use as a join field. • Joined table of population data for each municipality in Cuyahoga county for 1990, 2000, 2010 • Added PopChange field to joined attribute table • Used field calculator to calculate population change from 1990 to 2010 (pop1990-pop2010/pop1990*100) • Classified municipality map layer by population change Map 2: Cleveland Population Change 1990 – 2010 (census tracts) • Added Cuyahoga_census.shp and 2010census streadsheet to map. • Added a field called “tractID” with a “double” format field to census shapefile and used the field calculator to duplicate the “TRACTCE10” field into “tractID” • Joined 2010cenus table to Cuyahoga_census shapefile using the “tractID” field in the shapefile and “Geo_TRACT” in the database (new shapefile = Cuyahoga_census2010) • Calculated population density by creating a new field “2010PopDen” and using field calculator to divide “2010TotPop” by “2010LandAr” • Added 1990_censustract shapefile • Created new field “1990area” in 1990censustract.shp • Calculated area (square miles) for 1990 census tract boundaries using calculate geometry within attribute table • Selected census tracts whose “CO” (county) field = 035 (Cuyahoga County code) and created new layer from selection • Created new field “1990Tract” in 1990censustract = to Tractname*100 • Joined 1990census database to 1990_censustract shapefile using “1990Tract” field in shapefile and “1990Geo_Tract” in database (new shapefile = cuyahoga1990census) • Performed an intersection for cuyahoga1990census and cuyahoga2010census (new shapefile = 1990and2010pop_intersect) • Created new field (Area) and calculated area (square miles) for 1990and2010pop_intersect using field calculator • Created new field 1990people = 1990cens_5 (1990 population density) * Area (area of intersected area) • Created new field 2010people = 2010PopDen (2010 population density) * Area (area of intersected area) shrinking a city | kate vickery | CRP 388K | Fall 2012 | 33

• Confirmed that the total population for each of these fields matched the total population from the original 1990 and 2010 joined census shapefiles using ‘statistics’. They were each within 1000 people. This error can be explained by mapping error, but was not enough to question the integrity of this method. • Created new field 90_19_pcha = ((2010people-1990people/1990people)*100) • Classified the layer using the new percent change field to confirm that the trends look correct based on what I know about population change trends in the county. • In order to determine what the average percent change in population for the current 2010 census tracts, I performed a dissolve geoproccessing function: o input feature = 1990and2010pop_intersect o dissolve field = TRACTCE10 (2010 census tract identification number) o statistic field = 2010people (2010 population) and 1990people (1990 population) o statistic type = SUM for both • New layer = 90_10_pop_change_county • Added variable pc_change as a double • Calculated the percent change ((SUM_2010pe - SUM_1990pe)/SUM_1990pe))*100 • Clipped 90_10_pop_change to clevelandmuni shapefile (municipal boundary for City of Cleveland) • Classified census tracts by population change Map 3: Vacancy Rages | ACS 5-year Estimates (2005-2010) • Clipped Cleveland county census tract layer (2010) to Cleveland municipality layer • Added field to the 90_10_pop_change shapefile and duplicated the TRACT10ID field as a ‘double’ format • Joined Cleveland_demo spreadsheet, which contains data from the 2005-2010 ACS, from Social Explorer • Classified each census tract by vacant units standardized by total housing units Map 4: Spatial Patters in Location of Vacant Land and Re-use Projects • Selected points that have been approved or are currently under review (“LUrecommendation” = blank or Approve • Created new layer from each selection • Performed a merge to combine the two layers into one (disposition_appAPDrev) • Performed a spatial join to opportunity_clev layer = opp_ALLdispositions_join shrinking a city | kate vickery | CRP 388K | Fall 2012 | 34

• Performed average nearest neighbor spatial analysis using disposition_appANDrev • Results were significant (z-score =-30.0, p<.001). • Spatial join of SPA with disposition_andANDrev, including counts in the final output =SPAs_with_dispositions • Selected all SPAs that have at least one re-use project and created new layer from selection = SPA_with_disposition_join • Hotspot Analysis (using Manhattan distance) for SPA_with_disposition_join (hottest spots = St. Clair Superior, Glenville, Broadway Slavic Village, Kinsman) • Added vacant land layer to the map and used ‘feature to point’ data management tool to convert the individual polygons into a shapefile of points called vacantland_point • Did a special join of vacantland_point with SPA shapfile • Hotspot Analysis (using Manhattan distance) for vacant land concentrations. • Symbolized vacant land hotspot using traditional z-score methods, and overlaid re-use hotspot using cross-hatch pattern for statistically significant hotspot zones with a z-score over 1.65 in order to visualize where the overlap in the two hotspots are. Map 5: 2012 Stock of Vacant Land + Available Land Bank Land • Selected vacant land from Current Land Use shapefile • Created new shapefile from this selection • Imported available land bank shapefile (availableLB) and merged it with the vacant land shapefile in order to have a single shapefile with all of the “available” land for re-use (available_vacant_land) Map 6: Glenville Neighborhood | Concentration of Vacancy and Re-use • added SPA, streets, disp_appANDrev, and available_vacant_land to a map • selected by attribute for the SPA ‘glenville’ and created a new layer from the selection • clipped the other three spapefiles to the glenville shape Map 7: Matching needs to treatments: Comprehensive Opportunity + All re-use projects • Obtained opportunity index from the Kirwin Institute of Race and Ethnicity at The Ohio State University (Matthew Martin). This data was collected for Kirwin’s “State of Black Ohio” study, released March, 2010. • Clipped opportunity_oh to Clevelandmuni shapefile • Classified census tracts by “opp_COMP” using four classifications, natural breaks • Overlayed disposition_appANDrev shrinking a city | kate vickery | CRP 388K | Fall 2012 | 35

• Created simple map of the opportunity index and disposition data points overlaid. Used this map to: • Do a Geographically Weighted Regression on opp_ALLdispositions_join using count as the dependent variable, comp (the underlying indicator for the econ opportunity score) as the independent variable. I used the adaptive kernel type and AICc bandwidth method. Overall R2=.65 • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. • Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (comp< -1.12413) and used statistics within attribute table to find the total number of projects that fall within the ‘lowest’ category = 347 and used this to calculate the percentage of the projects that fall within the lowest category. Using the comprehensive opportunity index and different categories of re-use projects, I created maps for each of the five categories of social opportunity from the Kirwan index with overlaid point data for the ‘matched’ re-use projects. See Table 1 for the details of how these were matched. The following processes allowed me to create two maps for each social opportunity index: one with geographic distribution only and the second including a geographically weighted regression model in order to determine how well the types of projects meet the needs of different areas. I classified all of the opportunity indices into four classes, using natural breaks. In this report, I have only included the maps with the regression results included, but can provide the simple maps upon request. Map 8: Matching needs to treatments: Education & Child Welfare Selected attributes from disposition_appANDrev and explored layer for all disposition projects that are a good “match” for areas with low education/child welfare = new layer “disposition_edu” • "ProposedUs" = 'Playground/Open Space’ • Created new field in disposition_appANDrev called “opp_edu” and filled the variable with a 1 if the proposed land use was selected from the query • Created a new layer from the selection called opportunity_edu • Spatial join of opportunity_edu with opportunity_clev so that each census tract has a count of the number of education-focused re-use projects (new layer = opp_edu_join) • Renamed field “count_”  edu_count • Did a Geographically Weighted Regression on opp_edu_join using edu_count as the dependent variable, edu_comp (the underlying indicator for the edu shrinking a city | kate vickery | CRP 388K | Fall 2012 | 36

opportunity score) as the independent variable. I used the adaptive kernel type and AICc bandwidth method. Overall R2=.06 (this indicator is very poor at predicting the number of projects of this nature). • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. • Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (ECMOB_com< -1.95737) and used statistics within attribute table to find the total number of projects that fall within the ‘lowest’ category = 24 and used this to calculate the percentage of the projects that fall within the lowest category. Map 9: Matching needs to treatments: Economic Opportunity & Mobility Selected attributes from disposition_appANDrev and exported layer for all disposition projects that are a good “match” for areas with low economic opportunity & mobility = new layer “disposition_econ” • "ProposedUs" = 'Church Parking' OR "ProposedUs" = 'Comml./Indust. Expansions' OR "ProposedUs" = 'Comml./Indust. New Construction' OR "ProposedUs" = 'Comml./Indust. Parking Lots' OR "ProposedUs" = 'Institutional Expansion (non-parking)' OR "ProposedUs" = 'Institutional Parking (non- Church)' OR "ProposedUs" = 'Market Garden' OR "ProposedUs" = 'New Institutional' • Created new field in disposition_appANDrev called “opp_econ” and filled the variable with a 1 if the proposed land use was selected from the query • Created a new layer from the selection called opportunity_econ • Spatial join of opportunity_econ with opportunity_clev so that each census tract has a count of the number of economic-focused re-use projects (new layer = opp_econ_join) • Renamed field “count_”  econ_count • Did a Geographically Weighted Regression on opp_econ_join using housing_count as the dependent variable, ECMOB_comp (the underlying indicator for the econ opportunity score) as the independent variable. I used the adaptive kernel type and AICc bandwidth method. Overall R2=.24 • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. • Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (ECMOB_com< -0.833797) and used statistics within attribute table to find the total number of projects that fall within the shrinking a city | kate vickery | CRP 388K | Fall 2012 | 37

‘lowest’ category = 34 and used this to calculate the percentage of the projects that fall within the lowest category. Map 10: Matching needs to treatments: Housing & Neighborhood Development Selected attributes from disposition_appANDrev and explored layer for all disposition projects that are a good “match” for areas with low housing and neighborhood development = new layer “disposition_housing” • "ProposedUs" = 'Community Garden' OR "ProposedUs" = 'New Other Residential Construction' OR "ProposedUs" = 'New Single-Family Const. (1 lot requested)' OR "ProposedUs" = 'New Single-Family Const. (multiple lots requested)' OR "ProposedUs" = 'Parking for Residential (multi-family)' OR "ProposedUs" = 'Yard Expansion' OR "ProposedUs" = 'Playground/Open Space' • Created new field in disposition_appANDrev called “opp_house” and filled the variable with a 1 if the proposed land use was selected from the query • Created a new layer from the selection called opportunity_housing • Spatial join of opportunity_housing with opportunity_clev so that each census tract has a count of the number of housing-focused re-use projects (new layer = opp_housing_join) • Renamed field “count_”  housing_count • Did a Geographically Weighted Regression on opp_housing_join using housing_count as the dependent variable, HN_comp (the underlying indicator for the housing opportunity score)as the independent variable . I used the adaptive kernel type and AICc bandwidth method. Overall R2=.46 • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. • Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (HN_com<-1.475972) and used statistics within attribute table to find the total number of projects that fall within the ‘lowest’ category = 241 and used this to calculate a the percentage of the projects that fall within the lowest category Map 11: Matching needs to treatments: Public Health Selected attributes from disposition_appANDrev and explored layer for all disposition projects that are a good “match” for areas with low public health = new layer “disposition_health” • "ProposedUs" = 'Community Garden' OR "ProposedUs" = 'Garden (not adj. property owner - stand alone)' OR "ProposedUs" = 'Market Garden' OR "ProposedUs" = 'Seasonal Garden' OR "ProposedUs" = 'Playground/Open Space' shrinking a city | kate vickery | CRP 388K | Fall 2012 | 38

• Created new field in disposition_appANDrev called “opp_health” and filled the variable with a 1 if the proposed land use was selected from the query • Created a new layer from the selection called opportunity_health • Spatial join of opportunity_health with opportunity_clev so that each census tract has a count of the number of health-focused re-use projects (new layer = opp_health_join) • Renamed field “count_”  health_count • Did a Geographically Weighted Regression on opp_health_join using health_count as the dependent variable, PH_comp (the underlying indicator for the econ opportunity score) as the independent variable. I used the adaptive kernel type and AICc bandwidth method. Overall R2=.15 • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. • Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (PH_com< 0.325069) and used statistics within attribute table to find the total number of projects that fall within the ‘lowest’ category = 13 and used this to calculate the percentage of the projects that fall within the lowest category. Map 12: Matching needs to treatments: Public Safety & Criminal Justice Selected attributes from disposition_appANDrev and explored layer for all disposition projects that are a good “match” for areas with low public safety = new layer “disposition_safety” • "ProposedUs" = 'Community Garden' OR "ProposedUs" = 'Playground/Open Space' • Created new field in disposition_appANDrev called “opp_safe” and filled the variable with a 1 if the proposed land use was selected from the query • Created a new layer from the selection called opportunity_safety • Spatial join of opportunity_safety with opportunity_clev so that each census tract has a count of the number of safety-focused re-use projects ((new layer = opp_safety_join) • Renamed field “count_”  safety_count • Did a Geographically Weighted Regression on opp_safety_join using safety_count as the dependent variable, PSCJ_comp (the underlying indicator for the housing opportunity score)as the independent variable . I used the adaptive kernel type and AICc bandwidth method. Overall R2=.09 shrinking a city | kate vickery | CRP 388K | Fall 2012 | 39

o Classified the standard errors for the GWR into two classes, below and above 2 standard deviations in order to capture those areas that where the “best” fit seems to be. Outlined those areas in purple. o Selected by attribute census tracts that fell within the threshold of the “lowest” category of the housing indicator (PSCJ_com< -4.020526) and used statistics within attribute table to find the total number of projects that fall within the ‘lowest’ category = 13 and used this to calculate a the percentage of the projects that fall within the lowest category Map 13: Glenville Neighborhood | Needs and Treatments | Housing • added disposition_housing and disposition_appANDrev and clipped each to the Glenville SPA shapefile • greyed out any project points that were not housing/community related and made housing points bright pink • added opportunity_clev and clipped this file to the Glenville SPA shapefile • classified into four categories (lowest to highest) using natural breaks • added opp_housing_regression and clipped to the Glenville SPA shapefile • Classified the standard errors for the GWR into two classes, below and above 2 standard deviations and outlined those areas in purple. • Labeled several key streets for reference Reference Maps • Selected all census tracts in censustracts_county.shp, and removed the tract that extends into Lake Erie (TRACTCE10=99000) from the selection. Created new shapefile from the selection, named cuyahoga_census.shp. • Dissolved census tracts into single shapefile for state of Ohio. • Clipped TIGER County Map to the shapefile created from the dissolve. • Selected Cleveland municipality from Cuyahoga county municipality layer and created new layer with only City of Cleveland (clevelandmuni.shp). • Selected Cuyahoga county from county map and created new layer from the selection named Cuyahoga.shp • Obtained shapefile of the Great Lakes to use as reference • Used a state reference map for county-level maps, a county reference map for city-wide maps, and a city reference map for neighborhood-level maps Metadata All of the geographic data in this study was defined and projected to have the following metadata: • Datum: NAD 1983 • Projection: State Plane Ohio North FIPS 3401 (feet)