A Thesis

entitled

An Analysis of the Relationship Between Vegetation and Crime in Toledo, Ohio

by

Timothy J. Kosmyna

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Arts Degree in

Geography and Planning

___________________________________________

Dr. Yanqing Xu, Committee Chair

___________________________________________

Dr. Beth Schlemper, Committee Member

___________________________________________

Dr. Bhuiyan Alam, Committee Member

___________________________________________

Dr. Amanda C. Bryant-Friedrich, Dean

College of Graduate Studies

The University of Toledo

May 2020

© 2020 Timothy J. Kosmyna

This document is copyrighted material. Under copyright law, no parts of this document

may be reproduced without the expressed permission of the author.

iii

An Abstract of

An Analysis of the Relationship between Vegetation and Crime in Toledo, Ohio

by

Timothy J. Kosmyna

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Arts Degree in

Geography and Planning

The University of Toledo

May 2020

For several decades, urban crime has been a major problem for cities around the

world. There is much debate about whether the amount of vegetation in an urban area

leads to more or less crime. When studying this matter, geographic information systems

(GIS) and remote sensing play an important role in the analysis of this relationship. This

research draws from analytical methods found in contemporary literature as a means to

discover a relationship between crime and vegetation in Toledo, Ohio. Socioeconomic

factors are another aspect in this analysis, because vegetation is not the sole predictor of

crime. The research furthers the understanding of the need for community initiatives to

lower the impact of urban crime by highlighting a local neighborhood-based

organization, the Cherry Street Legacy Project, which incorporates Crime Prevention

Through Environmental Design (CPTED) principles into its work.

iv

Acknowledgements

I would like to thank my thesis committee for their guidance and assistance in the

completion of this project. Their input greatly benefits this work. Particular support goes

to committee chair, Dr. Yanqing Xu, for advice especially in the area of GIS analysis and

article recommendations. Acknowledgement and recognition is also attributed to Dr.

Beth Schlemper and Dr. Bhuiyan Alam for their support over the years.

v

Table of Contents

Abstract .............................................................................................................................. iii

Acknowledgements ............................................................................................................ iv

Table of Contents .................................................................................................................v

1 Introduction..............................................................................................................1

1.1 Overview............................................................................................................ 1

1.2 Study Area .........................................................................................................3

1.3 Problem Statement .............................................................................................4

1.4 Objectives .........................................................................................................5

2 Literature Review.....................................................................................................6

2.1 Positive Relationship .........................................................................................6

2.2 Psychological Benefits and Surveillance....................... ....................................7

2.3 Contemporary Studies ........................................................................................9

2.4 Crime Prevention Through Environmental Design .........................................14

3 Data and Methodology ...........................................................................................16

3.1 Data....................... ...........................................................................................16

3.2 Statistical Analysis ...........................................................................................24

3.3 Case Studies... ..................................................................................................28

3.4 Cherry Street Legacy Project... ........................................................................29

4 Results and Discussion ..........................................................................................33

vi

4.1 Results.............................................................................................................. 33

4.2 Discussion....................... .................................................................................37

5 Limitations and Conclusion ...................................................................................44

5.1 Limitations .......................................................................................................44

5.2 Conclusion .......................................................................................................47

References ..........................................................................................................................50

1

Chapter 1

Introduction

1.1 Overview

Several scholars have suggested that there is a correlation between crime and the

amount of vegetation in urban areas throughout the United States (U.S.); however, this

relationship has not been explored in Toledo, Ohio. Crime is a major problem for cities

in the U.S. and throughout the world. Crime Prevention Through Environmental Design

(CPTED) is a way that crime can be managed by proper design and use of the built

environment. A large amount of focus is placed on promoting changes in landscaping

and vegetation, such as modifying the amount, design, and type of vegetation in an area.

Implementing CPTED features can eventually lessen fear and incidence of crime in the

future (Jeffrey, 1971).

One example in Toledo, Ohio of a CPTED-based program in action is the Cherry

Street Legacy Project. This organization uses a data-driven approach to combat crime

and promote safety through environmental design projects in neighborhoods, such as

demolishing abandoned homes to create usable green space. These efforts are not

possible without partnerships between local agencies, such as the City of Toledo and

Lucas County Land Bank, which promotes a sense of community.

2

Some researchers have mixed views on the crime and vegetation relationship

based on certain aspects of vegetation. Dense vegetation in urban university campuses,

parking lots, and residential sites, have proven to change people’s perceptions of safety

and induce fear of crime by providing hiding places, or concealment, for potential

offenders and block barriers for victims to escape (Shaffer & Anderson, 1985; Nasar &

Fisher, 1993). On the contrary, high-nature, or vegetated common areas in the inner city

are said to improve psychological health and lower stress (Kaplan, 1995). These natural

environments also have the benefit of attracting large crowds of people, which increases

surveillance and thus deters people from committing criminal activities in areas with

more watchful “eyes on the street” (Coley, Kuo & Sullivan, 1997; Kuo & Sullivan,

2001a; Kuo & Sullivan, 2001b).

Geographic information systems (GIS) and remote sensing technologies have

provided more in-depth ways to explore the relationship between crime and vegetation.

This is conducted mostly through a multitude of regression analyses. A major theme of

this research is that socioeconomic and structural factors, such as income, race,

educational attainment, occupancy, population density, poverty, property hardening, and

security mechanisms, could be correlated with vegetation (Donovon & Prestemon, 2010;

Gilstad-Hayden, et al., 2015; Pearsall & Chistman, 2012; Snelgrove et al., 2004; Troy,

Grove & O’Neil-Dunne, 2012; Wolfe & Mennis, 2012). These variables are tested

through statistical modelling which can explain some of the observed effects that

vegetation, crime, and different types of crime may have on one another.

This study focuses on the city of Toledo, Ohio and utilizes the ideas presented in

contemporary studies from other cities as a basis to analyze whether a link exists between

3

crime and vegetation, along with comparing crime to sociodemographic variables by

utilizing statistical analysis methods. The paper furthers the understanding of a more

comprehensive approach to solving the urban crime dilemma through CPTED strategies

enacted by the Cherry Street Legacy Project, which is used as a case study.

1.2 Study Area

The geographic area of focus for this research is the city of Toledo, which is the

fourth most populous city in Ohio. Located in Northwest Ohio on the banks of the

Maumee River, it has a land area of 80.69 square miles and a city population of an

estimated 274,975 (U.S. Census Bureau, 2018). Boundaries were defined by 2016

Census block groups (N = 300). Although land use in Toledo is predominantly urban and

residential, the city is home to several natural areas, including 4 large metropolitan parks

and 136 city parks. Overall, Toledo's total crime rate is 52.13 crimes per 1,000 people, or

1.46 times the state rate and 1.84 times the national rate (U.S. Department of Justice,

2016). Violent crime occurs at a higher rate of 2.91 times the state rate and 3.09 times the

national rate, followed by property crime at 1.27 times the state rate and 1.64 times the

national rate. This makes Toledo rank as having the third highest crime rate in the state

behind Cleveland and Cincinnati.

4

Figure 1.1 Map of Toledo, OH with block group boundaries.

Source: Cartography by author using data from U.S. Census Bureau, Geography Division

1.3 Problem Statement

This thesis seeks to answer the primary question: Does a relationship exist

between vegetation and crime in Toledo, OH? Secondarily, are there significant

correlations between crime and other potentially confounding demographic and

socioeconomic factors? Understanding these relationships has implications for how

Toledo can work to prevent future crimes by focusing on environmental planning and

greening efforts.

5

1.4 Objectives

There are several objectives related to these research questions. The problems are

assessed by the following parameters:

1. Assess the current crime and socio demographic status in Toledo, OH.

2. Use analyses through remote sensing and GIS to determine whether a statistical

relationship exists between crime rate and the amount of vegetation in Toledo,

while also comparing correlations between crime and sociodemographic factors.

3. Determine how a local community planning initiative, the Cherry Street Legacy

Project, is working towards alleviating the impact of crime in Toledo through

Crime Prevention Through Environmental Design (CPTED).

4. Discuss the methodology used, its accuracy in the assessment of the crime and

vegetation, and whether changes could be made to improve future research.

6

Chapter 2

Literature Review

2.1 Positive Relationship

There is an immense amount of literature addressing the relationship between

vegetation and crime in urban areas throughout U.S. cities. Contemporary research

suggests that there is both a positive and negative relationship between vegetation and

crime. In addition, studies often include other socioeconomic and demographic variables

when comparing this correlation, because vegetation is not always the sole predictor of

crime in most cases. Geographic information systems (GIS) and remote sensing

technologies have significantly enhanced this type of research and have led to renowned

discoveries.

Although a majority of analyses reveals an inverse relationship, some recognize a

positive relationship and specifically identify the presence of vegetation relating to a

heightened fear of crime. A study in the year 1985 asked college students to rate the

level of security and attractiveness of 188 scenes of commercial and residential parking

lots throughout the cities of Athens and Atlanta, Georgia. The researchers concluded that

the presence of more natural, less maintained vegetation decreased the amount of security

felt by the students, thus increasing fear of crime (Shaffer & Anderson, 1985). Similar

7

findings were discovered from analyzing responses to site plans, on-site surveys, and

observations of pedestrian behavior at specific locations surrounding the Wexner Center,

an academic building at the Ohio State University in Columbus, Ohio (Nasar & Fisher,

1993). Results revealed that areas of high refuge, or concealment for a potential

offender, and limited prospect, or visibility for the victim, led to the most fear of crime.

Fear of crime was also heightened by lack of possibilities for escape, which was

characterized by heavy vegetation including tall, dense shrubs that inhibited line of sight.

These so-called crime “hot spots” also caused more difficulty for police to survey and

keep these locations secure and safe (Nasar & Fisher, 1993).

Not all vegetation reduces visibility or is view obstructing, however. A study

based on interviews with residents, housing authority administrators, and police at the

Robert Taylor Homes public housing development in Chicago, Illinois found that mature

trees generated less fear than bushes and shrubs (Kuo, Bacaicoa, & Sullivan, 1998).

Although these studies mentioned above focus on small-scale sites and do not address

actual crime data, there is a realization that urban vegetative landscape features can affect

the fear of crime felt by citizens.

2.2 Psychological Benefits and Surveillance

The other side of the discussion claims that vegetation helps to deter crime. This

idea is supported by both theoretical and empirical evidence. In fact, the power of the

natural environment, such as wooded and grassy areas, to influence mental health, human

behavior, and surveillance is well documented. The activities of people living in two

urban public housing complexes in Chicago, for example, were observed to determine

8

whether natural elements played a role in the use of outdoor spaces. After over a month

of observing residents’ behavior at these facilities and computing the data, results showed

that the presence of trees in outdoor areas attracted nearly three times more people than

those areas that were treeless (Coley, Kuo, & Sullivan, 1997). One way in which this

particular preference can be explained is by the work of Stephen Kaplan who suggested

that exposure to nature, which is trees in this case, has the potential to reduce mental

fatigue, or directed attention fatigue, and provide other restorative benefits (Kaplan,

1995). This is because nature and natural landscapes, such as vegetated areas in a busy,

fast-paced urban setting, can effortlessly engage one’s attention and eliminate the need to

think so much about problems at work or home, traffic, and complex decision-making,

which consumes most of our lives on a daily basis, especially for inner-city inhabitants.

Mental fatigue is related to the symptoms of irritability, inattentiveness, and decreased

control over impulses, which are linked to violence (Kaplan, 1987). Vegetation is not

only effective in reducing mental fatigue; it also mitigates aggression and inhibits the

potential for criminal behavior, thus lowering levels of crime.

Besides lowering aggressive behavior, vegetation also increases both informal and

implied surveillance (Kuo & Sullivan, 2001a). Informal or actual surveillance is related

to the use of vegetated outdoor spaces rather than open, barren spaces. With more people

present in natural settings, vegetation draws more eyes on the street and thus deters

criminals away from well-used spaces. Implied surveillance relates to the maintenance

and care shown to landscaping and vegetation in neighborhoods, where attractive

landscaping is known as a “cue to care” and a territorial marker (Kuo & Sullivan, 2001b).

For example, if the vegetative features of a home are well maintained, this implies that

9

the resident pays attention to their property and acts as a physical cue to a potential

intruder that they would be noticed and caught.

This idea of maintenance and care is highlighted by the broken windows theory in

that criminals are more attracted to areas that seem more poorly maintained. The theory

was formed out of a mid-1970s policing effort that assigned officers to walk in

neighborhoods and maintain order instead of patrolling from their patrol cars. The basic

premise of the broken windows theory is that if a window in a building is broken and is

left unrepaired, the rest of the windows will soon be broken (Wilson & Kelling, 1982).

Broken windows points to lack of a care and social order in a community that could

influence criminals to commit more crimes there and not care about any repercussions.

Similarly, areas where vegetation is left overgrown or unmaintained can suggest to

criminals that it is okay to commit a crime there.

2.3 Contemporary Studies

Given the many psychological benefits and vigilance due to the presence of

vegetation, the link between vegetation and a reduction in actual crimes is also

sufficiently supported by many more recent research studies. GIS and remote sensing

serve as important tools to analyze the negative, or inverse, relationship between

vegetation and crime through a number of techniques. An important study conducted by

(Kuo & Sullivan, 2001b) was one of the first to observe the relationship of vegetation and

police crime reports. Aerial photographs taken by helicopter and ground-level views for

each of the 98 apartment buildings at the Ida B. Wells public housing development in

Chicago were rated based on the level of tree canopy cover and grass surrounding them

10

(Kuo & Sullivan, 2001b). Other potential predictor variables assessed were occupancy,

vacancy rates, building height, income levels and educational attainment of residents. By

running the variables through a multivariate ordinary least squares (OLS) regression,

results revealed that areas with high vegetation attributed to half as many crimes

compared to low vegetation areas. The confounding variables, however, did not hold

strong predictive power as vegetation proved to be the strongest predictor of crime. This

finding made a unique contribution by being the first study to use actual crime reports to

indicate a negative association between vegetation and crime in an inner-city setting.

Building on the work of Kuo and Sullivan (2001b), Donovan and Prestemon

(2012) focused on a residential scope, specifically the characteristics of trees, along with

property and neighborhood characteristics in relation to crime data. Donovan and

Prestemon (2012) analyzed a neighborhood in Portland, Oregon with a high proportion of

single-family homes by taking site visits to each home. The tree variables were related to

the size of trees on each respective lot, street, and block by measuring the crown of the

trees from aerial photographs. Other variables considered were housing attributes, level

of barriers to the property, alarm systems, street lighting, presence of porches, and

neighborhood watches (Donovan & Prestemon, 2012). A Poisson regression count

model was used to generate results, which proved that trees located along residential

streets, and those with larger crowns on private lots were associated with less crime. This

is because larger trees have higher crowns and are therefore less view obstructing for

residents, compared to smaller trees or shrubs on a private lot, which provide more cover

for criminals and therefore more crime occurrences. Although surveillance mechanisms

and structural characteristics may influence the motivation of a potential perpetrator, this

11

study found that large trees and trees along public right-of-ways provided the strongest

negative relationship between tree cover and crime.

Tree canopy cover was used in other studies with larger scopes. For example,

Gilstad-Hayden et al. (2015) used tree canopy cover as a measure of vegetation in the

mid-sized city of New Haven, Connecticut. A 30-meter land cover data was calibrated

from the National Land Cover Database (NLCD) with 1-meter aerial imagery, which

allowed the tree crowns to be isolated and geo-located and converted into percentage per

block group (Gilstad-Hayden, et al., 2015). Crime rates for violent crime, property

crime, total crime, and socio-demographic control variables were run through a OLS

regression and spatial lag model. The lag model was chosen because of the presence of

spatial autocorrelation and it adjusts for that. Autocorrelation occurs when a variable in

one area is affected by characteristics within that area and another area, which can create

spillover and biased statistical errors, which violate OLS assumptions of the independent

variables. Results affirmed a negative correlation with every 10 percent increase in tree

canopy cover associated with a 15 percent decrease in violent crime rates, and a 14

percent decrease in both property and total crime rates (Gilstad-Hayden, et al., 2015).

There were also compelling results associated with the control variables as renter

occupied housing and lower population density related to higher crime rates, while

African Americans and Hispanics were associated with increased violent crime, but not

property or total crime.

Unfortunately, the methods used by Gilstad-Hayden et al. (2015) only measured

tree crowns and left other types of smaller vegetation unrepresented. This may have

affected the study’s effectiveness. Troy, Grove, and O’Neil-Dunne (2012) attempted to

12

solve this issue by combining the use of color infrared imagery and surface models

generated from light detection and ranging (LiDAR) data in Baltimore, Maryland. The

use of LiDAR proved to be valuable because it allowed for more accurate detection of

trees obscured by shadows and the differentiation between canopy trees versus low

woody vegetation known as shrubbery (Troy, Grove, & O'Neil-Dunne, 2012). The

variable created was the percentage of tree canopy cover by block group in public versus

private land. A crime index including robbery, theft, and shooting was set as the

dependent variable, while tree canopy cover was set as the independent variable, along

with a number of socioeconomic conditions. Based on results from a geographically

weighted regression (GWR) model, which is commonly used to explain local spatial

relationships better than an OLS regression and spatial lag model, the study found that

there was a 20 percent decrease in crime for every 10 percent increase in tree canopy

cover (Troy, Grove, & O'Neil-Dunne, 2012). This means that tree canopy cover is

significantly associated with a reduction in crime.

Besides tree canopy cover, normalized difference vegetation index (NDVI) is a

widely used and effective method to measure vegetation (Pearsall & Christman, 2012;

Snelgrove, Michael, Waliczek, & Zajicek, 2004; Wolfe & Mennis, 2012). The

calculation of NDVI involves a ratio to reveal an inverse relationship between the near

infrared (NIR) band region and visible (VIS) region, or red color band of the

electromagnetic spectrum of a satellite image. The formula is explained as:

𝑁𝐷𝑉𝐼 =𝑁𝐼𝑅 − 𝑉𝐼𝑆

𝑁𝐼𝑅 + 𝑉𝐼𝑆

The level of greenness or concentration of healthy, green vegetation per pixel can range

from -1.0 to 1.0. A value of -1.0 indicates little to no vegetation, while an integer of 1.0

13

contains 100 percent vegetation (Snelgrove, Michael, Waliczek, & Zajicek, 2004).

Snelgrove, Michael, Waliczek, and Zajicek (2004) incorporated mean NDVI values per

census tract into a GIS interface for Austin, Texas. Crime reports, indexed by severity

level, and income levels were included along with NDVI. The data was analyzed through

Pearson’s correlation coefficient to reveal its statistical significance, which revealed that

nearly 83 percent of all crimes occurred in areas lower than the mean greenness value for

Austin (34 percent) and a strong negative correlation between the number of crimes and

greenness was discovered (Snelgrove, Michael, Waliczek, & Zajicek, 2004). It was also

proven that crime severity, based on the type, was not affected by income level or

vegetation.

NDVI was utilized again by Wolfe and Mennis (2012) in their analysis that

compared the crime rates of assaults, robberies, burglaries, and thefts in relation to mean

NDVI values per census tract in Philadelphia, Pennsylvania. Additional explanatory

variables included poverty rate, educational attainment, and population density. Results

from an OLS and spatial lag model indicated that all of the crimes, besides theft, were

significantly associated with higher vegetation indices. Poverty was positively associated

with each crime type, which is common, as more crime tends to occur in impoverished

areas. Significant negative correlations, however, were found between population

density and aggravated assaults and burglaries meaning these crimes are tied to sparser

populations. Educational attainment was not significantly associated with crime.

Although OLS and GWR regression analyses may be at the forefront of a number

of the studies mentioned, it is important to note that those regressions did not account for

spatial autocorrelation, which was a common issue when dealing with crime and multiple

14

control variables. Spatial autocorrelation relates to the violation of the regression

assumptions of the independent error terms (Troy, Grove, & O'Neil-Dunne, 2012). For

example, crime in one geographic area could be affected by characteristics in that area

and in other areas, leading to spillover effects, biased assumptions and less accurate

results. Overall, this led researchers to test their data with several methods and spatially

adjusted methods to account for potential errors.

2.4 Crime Prevention Through Environmental Design

Given the aforementioned studies, there is a mostly negative relationship between

vegetation and crime in contemporary literature, which has spawned a number of

community planning initiatives that work to reduce crime and improve community health

which focus on vegetation and environmental design. CPTED is a concept that has

become a popular crime prevention strategy throughout Europe, North America,

Australia, and New Zealand, as well as in Asia and South Africa (Love, 2015). It looks

at how opportunities for crime can be reduced through the built urban form first coined

by criminologist Ray Jeffrey in 1971. The term was modernized more by Oscar

Newmann’s 1972 work, Defensible Space, which highlighted the need to make spaces in

urban communities and neighborhoods safer by providing practical design elements

based on architecture and the physical environment. Newmann’s four main design

elements included territoriality, surveillance, image and milieu, and environment. He

calls for communities to be controlled not by police, but by a community of people

sharing a common terrain (Newman, 1972). This can be done by improving visibility and

15

sight lines between buildings and homes, using appropriate lighting, and promoting

proper maintenance measures for vegetation.

Since the 1970s, CPTED’s ideas have been refined by researchers, policymakers,

and practitioners to focus more on social factors to create cohesion by supporting and

celebrating diversity. Community connectivity is another factor that makes it imperative

to develop partnerships with governmental and nongovernmental groups to coordinate

activities and programs that support CPTED initiatives. The residents and other

community members should also have the opportunity to participate and let their voices

be heard throughout the decision-making process. When the physical characteristics of a

crime-ridden neighborhood are altered through planning and community initiatives based

on CPTED principles, criminal activity can be reduced at these geographic hot spots and

establish a resurgence of a stronger sense of community over time.

Several substantial conclusions can be made regarding literature on the topic of

vegetation and crime. A majority of publications support the idea that vegetation and

crime are inversely related and that vegetation significantly reduces crime in urban

environments. Methods through remote sensing and GIS hold the keys to analyzing these

relationships by creating different ways to calculate vegetation and compare

sociodemographic data. Although it was found that few sociodemographic variables are

effectively correlated to crime, each study is unique and their predictive ability can

change based on the study area, geographic units and factors under investigation.

Advancements in the understanding and analysis of vegetation and crime can assist

communities to better plan for the future and figure out ways to reduce crime through

urban planning policies and environmental design.

16

Chapter 3

Data and Methodology

3.1 Data

Toledo’s crime, vegetation, and socioeconomic data were collected from various

sources for this study. Crime data was provided by the Toledo Police Department (TPD)

from the year 2016. The crime data is a list of incidents reported to TPD that included

the date, time, address, and type of offense. The only crime reports used for this study

were those that provided a valid address within the city of Toledo’s boundaries and

completely contained within a Census block group. In all there are 302 block groups in

Toledo; however, two block groups were removed which contained a shopping center,

Franklin Park Mall, and college campus, the University of Toledo (UT), reducing the

number of block groups to 300. The block group containing the mall had the highest

property crime count in the city due to it being a heavily trafficked commercial area,

which skewed the data. A majority of UT’s crime is reported to the campus police

department and not TPD, which means the crime counts there were inaccurate and

unusable. In total, there were 11,290 crime occurrences matched to addresses through the

geocoding process in ArcMap 10.6.1. Crimes were then separated into two categories as

17

a means to identify if certain crime types had varying degrees of association with

vegetation and sociodemographic factors. The crimes were categorized based on the

2016 FBI’s Uniform Crime Report, which defines which types of crimes are violent or

property-related. Violent crime accounts for murder/non-negligent manslaughter, rape,

robbery, and aggravated assault. Property crime includes burglary, larceny-theft, motor

vehicle theft, and arson. Total crime includes both violent and property crime combined.

The average total crime rate in Toledo was calculated at 47.97 crimes per 1,000 people.

There were 1,932 violent crimes and 9,358 property crimes, with crime rates of 8.71

violent crimes and 39.26 property crimes per 1,000 people. These three crime types

(violent, property, and total) were aggregated at the block group level (N = 300) as a

means to quantify the point data and keep homogeneity amongst the other data. The data

was then converted into crime rates per 1,000 people. Table 3.1 below gives a statistical

summary of Toledo’s crime rates, followed by Figure 3.1.2 presenting choropleth maps

of crime rates by block groups.

Table 3.1 Descriptive statistics of violent, property, and total crime in 2016

Variable Mean Std. Dev. Minimum Maximum

Violent Crime (per 1,000 people) 8.71 9.91 0.00 59.78

Property Crime (per 1,000 people) 39.26 29.28 0.00 205.13

Total Crime (per 1,000 people) 47.97 36.56 0.00 217.95

Source: Toledo Police Department Statistics, 2016

18

Figure 3.1.2 Darker shading indicates more crime. The maps suggest that violent crime

is more concentrated in the center of the city, while property and total

crime are dispersed throughout with less crime in the north and south.

Source: Cartography by author using Toledo Police Department Statistics, 2016

Vegetation data was gathered from an aerial satellite image covering Toledo, OH.

The image was obtained by the Sentinel-2 satellite, operated by the European Space

Agency, via the EO Browser website database. The 10-meter (m) resolution remotely

sensed infrared image was taken during the summer on June 29, 2016 to ensure that

vegetation was at peak greenness levels. The ArcGIS Image Analysis tool was utilized to

calculate the NDVI value for each pixel of the image.

19

The formula for NDVI is stated as follows:

𝑁𝐷𝑉𝐼 =𝑁𝐼𝑅 − 𝑉𝐼𝑆

𝑁𝐼𝑅 + 𝑉𝐼𝑆

where the near-infrared band (NIR) and visible light band (VIS), or red color band,

creates a ratio of the amount of vegetation per pixel. The resulting interval spans from -

1.0 to 1.0, in which a value greater than 0.5 indicates dense, healthy vegetation, while a

value near zero or below pertains to unhealthy vegetation, barren soil, rock or inanimate

objects such as buildings and roads (Wolfe & Mennis, 2012). The average NDVI per

block group was then computed through ArcMap’s Zonal Statistics tool, which calculated

the mean value of all the NDVI image pixels that fell within each block group. The mean

NDVI was then converted into a percentage to measure the mean greenness level, where

a value -1.0 received a 0% and a 1.0 received a 100% on the greenness scale. Overall,

Toledo’s land area consists of 37.29% average healthy vegetation. The vegetation data is

explained below in Table 3.1.3 and Figure 3.1.4.

Table 3.1.3 NDVI versus mean greenness values by block group. Overall, Toledo’s land

area consists of 37.28% healthy vegetation.

Mean Minimum Maximum

NDVI Values (-1 to +1) 0.37 -0.69 0.70

Mean Greenness (%) 37.29 12.69 55.59

20

Figure 3.1.4 The map on the left shows the raw NDVI values and the map on the right

shows mean greenness levels by block group. Green represents healthy

vegetation compared to orange and red, which are unhealthy vegetation.

There are higher concentrations of vegetation in the west and

northwest part of the city compared to the central, east and northeast

regions.

Source: Cartography by author using satellite imagery from EO Browser

Control, or explanatory variables, in the form of sociodemographic conditions

were another important aspect of this study. These factors were used to test the

association independently of vegetation over crime. The block group-level population

data was obtained through the U.S. Census Bureau’s American Factfinder website,

https://factfinder.census.gov/, from the 2012 to 2016 American Community Survey

(ACS). Four sociodemographic variables were chosen for this study based on previous

studies (Gilstad-Hayden, et al., 2015; Kuo & Sullivan, 2001b; Troy, Grove, & O’Neil-

Dunne, 2012; Wolfe & Mennis, 2012). They included vacancy rate (percent of vacant

housing units), race/ethnicity (percent of black population or nonwhite population), and

educational attainment (percent of population 25 years and older with less than a high

school degree), per capita income in the past 12 months which is the average income per

person, and population density (per square mile). Toledo’s block groups had an average

21

vacancy rate of 14.82%. Its black population was 28.66% and it had a high educational

attainment with only 3% of the population having less than a high school degree. Median

per capita income was measured at an average of $20,220, which is 1.61 times lower than

the national average of $32,620 (U.S. Census Bureau, 2018). Population density in

Toledo had a range between 353.88 and 18,725.96 people per square mile with a mean of

4,910.07. Table 3.1.5 and Figure 3.1.6 below explain this data in more detail.

Table 3.1.5 Descriptive statistics of socioeconomic variables in 2016

Variable Mean Std. Dev. Minimum Maximum

14.82 12.98 0.00 56.85

28.66 28.75 0.00 100.00

3.00 3.71 0.00 22.19

20.22 9.30 3.84 66.53

Vacancy Rate (%)

% Black Population

% With Less Than a High School Degree Per

Capita Income (in $1000's) Population Density

(per sq. mi.) 4910.07 2953.96 353.88 18725.96

Source: U.S. Census Bureau American Factfinder, 2012-2016 American Community

Survey (https://factfinder.census.gov/)

22

Figure 3.1.6 Choropleth maps of the socioeconomic variable by block group. Darker

shading indicates block groups with higher values of the variable.

Source: Cartography by author using data U.S. Census Bureau American Factfinder,

2012-2016 American Community Survey (https://factfinder.census.gov/)

Based on the maps in Figure 4.1.6, vacancy and the percent Black population are

similarly concentrated in the west-central part of the city, while lower educational

23

attainment is generally more dispersed throughout. Higher population densities are

clustered in established neighborhoods. In the north there is a clustering of high

population density in the Five Points and Deveaux neighborhoods, the central Toledo

cluster consists of Lagrange and the Old West End neighborhoods, while the southeastern

cluster is in the East Side neighborhood. Block groups with higher per capita income are

more prevalent in the north, west and southwest parts of the city.

These variables are known to be significant predictors of crime in previous

studies. Several of these variables point to factors that concern disadvantaged

neighborhoods, where crime tends to be higher (Gilstad-Hayden, et al., 2015). They also

stem from the broken windows theory, which claims that if a window in a building is

broken and is left unrepaired, all the rest of the windows will soon be broken (Wilson &

Kelling, 1982). A broader look at this analogy of “broken windows” signals to a lack of

care and social order in a community that could influence criminals to commit more

crimes there and give less thought to the repercussions. Vacancy is a signal for lack of

care to a property and can act as a venue for crime given that there are less “eyes on the

street” or surveillance.

Population density has two roles in causing crime. Firstly, less people living in a

specified land area means that more crime can be committed there without being caught.

Secondly, places that have activity that is more commercial tend to be more populated

during the daytime offering a greater opportunity for property crime, especially theft,

with the presence of more potential victims (Kelly, 2000). Lower per capita income can

also be applied to people of color (percent black population) which is why both of these

variables were included. “Race is a predictor of crime either through the low economic

24

success of black males or feelings of hopelessness in black communities” (Kelly, 2000).

These feelings of hopelessness, isolation, and despair can trigger minorities to partake in

criminal activities, such as stealing and violent offenses. It is likely that adults with less

than a high school education have less economic opportunity than those with a college

degree, which exacerbates this disadvantage and potential for crime.

3.2 Statistical Analysis

The quantitative portion of this research involved utilizing multiple regression

models to analyze the data. Qualitative methods were also employed by using a case

study to examine the phenomenon further. A multivariate ordinary least squares (OLS)

regression was employed in ArcMap 10.6.1 to determine the strength of explanatory

power that vegetation has on the three crime outcomes, while controlling for the four

sociodemographic variables. The OLS regression is expressed as:

yi = β0 + β1MEANi + β2VACi + β3RACEi + β4EDUi + β5INCi + β5POPDENi + εi

where yi is the crime rate for block group i, β0 is a constant, MEAN is mean greenness

percentage, followed by the four beta coefficients representing the socioeconomic factors

of vacancy rate (VAC), percent black population (RACE), educational attainment (EDU),

per capita income (INC), population density per square mile (POPDEN), and εi is the

error term. The p-value (p), or probability, determines the significance of the relationship

between crime rate and each predictor variable. A p-value that has a low value less than

0.01 (p < 0.01) at 99 percent confidence is meaningful to the model and indicates that you

can reject the null hypothesis, because essentially the null hypothesis is that the

coefficient is equal to zero. The strength and type of relationship is expressed in each

control variable’s coefficient (β). For example, if the coefficient for a variable is

25

negative, the relationship is negative, and if the p-value is also less than 0.01, the

independent variable has a statistically significant association with the dependent variable

by rejecting the null hypothesis. This means that the variable is a valuable predictor and

can add value to the model’s ability to explain the phenomenon.

Spatial autocorrelation is a common problem associated with regression analyses.

It accounts for the risk that the OLS assumptions of uncorrelated error terms and

independent observations were not met, since crime rates tend to be more similar and

biased among neighboring block groups than to distant block groups due to spillover

effects (Troy, Grove, & O'Neil-Dunne, 2012; Wolfe & Mennis, 2012). A Global

Moran’s I test was conducted in ArcMap to assess if the residuals of the dependent

variables had spatial autocorrelation. The tool measures spatial autocorrelation based on

both feature locations and their values simultaneously through several statistical

computations and generates the Moran’s Index, also known as the Observed Index (ESRI,

n.d.). This statistic ranges from -1.0 to 1.0, in which values below -1.0 represent negative

autocorrelation, or dispersion, while a positive number greater than -1.0 indicates positive

autocorrelation, or clustering. The test also generates z-scores, which are standard

deviations, and p-values, or probabilities, of which to evaluate the significance of the

Observed Index. Below are the result summaries and the standard normal distribution

curves associated with each crime type’s z-scores.

26

Violent Crime Rate Property Crime Rate

Total Crime Rate

Figure 3.1.7 The standard distributions tell us that the three crime rates have positive

Moran’s Index values, high z-scores, and a statistically significant p-

values, which indicates positive clustered spatial autocorrelation.

Violent crime rate had a Moran’s Index of 0.502, followed by property crime at 0.324,

and total crime with a value of 0.419. The crime data was experiencing positive spatial

autocorrelation with clustering of high-high and low-low crime rates, based on their

statistically significant p-values (p < 0.01), positive Moran’s Indexes, and high z-scores.

Because of this autocorrelation, two spatially adjusted regression models were

implemented in the GeoDa spatial modeling program – the spatial error and spatial lag

27

models. The spatial error model assumes that the error term is subject to spatial

autocorrelation and captures the influence of the unmeasured independent variables

(Matthews, 2006). It is expressed as:

yi = β0 + β1MEANi + β2VACi + β3RACEi + β4EDUi + β5INCi + β5POPDENi + λWiεi

where λ is the spatial error coefficient and the original error term εi is now weighted by

the weights matrix Wi.

In a spatial lag model, the dependencies are assumed to exist mostly among the

dependent variable, while still incorporating the unmeasured independent variables. The

lag term is included in the regression as follows:

yi = β0 + β1MEANi + β2VACi + β3RACEi + β4EDUi + β5INCi + β5POPDENi + pWiyi + εi

where p is the coefficient to be estimated and Wiyi is the spatial lag, which is the

weighted average of the dependent variable yi for units neighboring block group i and εi

is the error term (Wolfe & Mennis, 2012).

After running both the spatial lag and spatial error models and evaluating their

statistics, results led to the choosing of the spatial lag model. The Akaike Information

Criterion (AIC) and R-squared statistic (R2) were effective in comparing the models’

goodness of fit and performance. R-squared, also called the coefficient of determination,

is “the proportion of variance ‘explained’ by the regression model”, in which its value

describes how well the dependent variable is predicted by the independent variables

(Nagelkerke, 1991). It is measured from zero to 1, or 0% to 100%, with a higher value

indicating better variance. The AIC is one of the oldest and most restrictive criteria in

regression analyses providing a measurement of the out-of-sample deviance as an

approximation for predictive accuracy (McElreath, 2016). The AIC value explains the

28

relative amount of information that was lost by the model, so a lower number means the

model lost less information and therefore represents better quality for the dataset. The

spatial lag model was chosen because it produced a higher R-squared and a lower AIC

across all three crime outcomes when compared to the spatial error model. The spatial

weights matrix was calculated using the queen’s contiguity matrix.

3.3 Case Studies

The previous analysis methods aid in addressing the quantitative aspects by

objectively comparing crime to vegetation and sociodemographic variables; however,

another way to analyze the problem is by incorporating a qualitative method in the form

of a case study. A case study involves the study of a single instance or a small number of

people, such as an event, a process, institution, or a particular place, such as a crime-

ridden neighborhood, as a way to broaden the depth of understanding about a larger

phenomenon or a concrete problem (Baxter, 2016). It differs from a quantitative method

since it is not bounded by the study design or statistical parameters, and gives way to

more substantial findings in a real-life phenomenon. Case studies play a role in testing a

theory, by considering a case that supports or falsifies the concept under consideration, or

expanding a theory in that grounded theory and other forms of qualitative research, such

as interview and observation data, can be incorporated within a case study design. Cases

can be studied at one moment in time or across multiple instances leading to the case

being revisited in order to determine if the original explanations have endured over time.

“Statistical methods may identify deviant cases that can lead to new hypotheses,

but in isolation these methods lack any clear means of actually identifying new

29

hypotheses” (Flyvbjerg, 2011). Given that cases can be studied using different

investigative methods, this can identify left-out variables and have the potential to verify

previous discoveries and even support some statistical results. Overall, it is important to

note that the presumptions and outcomes developed through a case study are more often

due to the reality of the social phenomena studied versus the case study being used as a

research method.

3.4 Cherry Street Legacy Project

The Cherry Street Legacy Project was used as a case study for my research. It is

one of the only data-driven, neighborhood-based organizations in the nation with a

primary goal being to address crime and perception of crime by implementing CPTED-

based strategies, of which an aspect includes modifying the amount and type of

vegetation in specific areas. Although my statistical methods did not focus primarily on

neighborhood-level point-based crime data or vegetative data, Cherry Street Legacy has

identified the link between spatial and temporal crime patterns in order to assist in better

implementing CPTED principles to the built urban form. This small-scale example

supports and expands upon my research goals by providing a real-world application of

how crime is affected by the amount of vegetation in an urban setting, in which I could

potentially use to support assumptions made through my quantitative results.

Cherry Street Legacy is partnered with and headquartered at Mercy St. Vincent

Medical Center on Cherry Street in Toledo, OH. The neighborhood planning area, which

the project considers its Legacy Neighborhood and works to improve, is about a 2-mile

stretch along Cherry Street, bordered by Downtown, the Old West End, and the Old

30

North End. The area is diverse in that it offers a mix between commercial, institutional

and residential land uses. It has a high percentage of low-income housing, high vacancy

rates, and a large number of renter-occupied homes, leading to a heightened crime rate.

Institutional assets in this region include Central Catholic High School and Mercy St.

Vincent Medical Center.

Figure 3.1.8 This map shows the extent of the Cherry Street Legacy Plan Area.

Source: Cherry Street Legacy Plan, 2009

In 2009, the Cherry Street Legacy Project began working on its strategic plan to

revitalize the neighborhood by first gathering survey data from over 225 households,

which the Lucas County Land Bank extrapolated into a GIS database, to provide

31

information on whether a parcel was vacant, unoccupied, or a vacant lot. The TPD then

provided 5 years of point-based crime data to determine where crime hotspots were

located and which types of crime occurred there in order to decide on the best CPTED

features to implement and mitigate potential opportunities for crime. With the data

compiled, Cherry Street Legacy was able to devise a plan, the Cherry Street Legacy Plan,

to address blight, safety, business, and overall quality of life for the Cherry Street

corridor and surrounding neighborhoods. The development of the plan would not have

been possible without the collaboration with several community stakeholders, including

the Lucas County Land Bank, City of Toledo-Lucas County Plan Commission,

Department of Neighborhoods, Mercy Health Partners, and a number of non-profit

organizations.

In addition to using Cherry Street Legacy as a case study, I utilized other

qualitative research methods to collect data by conducting an in-person interview and

performing fieldwork. I interviewed the former coordinator of the organization, Karen

Rogalski, in August 2016, at her office in St. Vincent Mercy Medical Center. Rogalski is

also a trained CPTED professional with several years of knowledge in the field. I

observed the Legacy Neighborhood and its progress firsthand by following alongside her

as she and I walked the streets. Through the interview and walk, I collected information

on the organization and its role in making a stronger and safer community. I asked

questions pertaining to its efforts and their effectiveness in reducing crime, along with

how community engagement played a role in the longevity of the project. During our

walk, Rogalski showed me several environmental design changes and projects that took

place during the past few years. She explained Cherry Street Legacy’s efforts in detail as

32

I listened and recorded our discussion by using my cell phone and summarizing key

information by writing it down on a notepad. From there I had enough data collected to

discuss and compare these findings to my quantitative results.

33

Chapter 4

Results and Discussion

4.1 Results

There were several statistically significant results for the 300 block groups. Table

4.1 provides results from the OLS and spatial lag models predicting crime rates on the log

scale. Results were generally consistent between the OLS and spatial lag models, but the

spatial lag model had a slightly better fit for the data, given its lower AIC and higher R-

squared values for each crime outcome. For the spatial lag model, mean greenness had a

statistically significant negative relationship with violent crime (β = -0.343, p = <0.01),

property crime (β = -1.338, p = <0.01) and total crime (β = -1.664, p = <0.01), while

controlling for potential confounding variables. Based on the spatial lag model’s results

and its exponentiated coefficients, it is suggested that for every 5% increase in mean

greenness, there was an 82% decrease in violent crime rate [exp (-0.343 x 5) = 0.180].

Comparably, a 5% increase in mean greenness was associated with a 100% decrease in

property and total crime rates. These are notable results because they proved that a

greater amount of vegetation leads to much lower crime rates.

34

Significant findings were also associated with the control variables. Vacancy rate

proved to have a significant positive relationship with violent crime (β = 0.192, p =

<0.01), property crime (β = 0.599, p = <0.01) and total crime (β = 0.780, p = <0.01).

This means that higher rates of vacant housing units were associated with higher rates of

violent, property and total crime. There was also a positive correlation between percent

Black population and violent crime (β = 0.069, p = <0.01), but no association with

property or total crime rates. Educational attainment had a positive association with

property crime (β = 1.249, p = <0.01) and total crime (β = 1.302, p = <0.01), which

alluded to block groups with a less high school educated population being associated with

more crime. Lower population density was associated with higher rates of all crime

types, meaning more crime tends to occur in lesser-populated areas. The lone economic

variable per capita income did not have any significance when compared to the crime

data in either model. Overall, variance was highest in the violent crime model (R2 =

0.510), followed by total crime (R2 = 0.501), with more than half of violent and total

crime rates explained by the independent variables. Variance was lowest with property

crime (R2 = 0.429).

Table 4.1 These tables list the coefficients (β), standard errors (SE), and probabilities

(P) from OLS and spatial lag regression models predicting 2016 violent,

property and total crime rates among block groups (N = 300) in Toledo, OH.

Variable

OLS Model

Mean Greenness (%)

Vacancy Rate (%)

% Black Population

% With Less Than a High School Degree

Per Capita Income (in $1000's)

Population Density (per mi²)

Intercept

R-squared

AIC

Violent Crime Property Crime Total Crime

β SE P β SE P β SE P

0.067 <0.01 0.210 <0.01 0.247 <0.01

0.040 <0.01 0.125 <0.01 0.148 <0.01

0.017 <0.01 0.055 0.870 0.065 0.210

-0.402

0.239

0.082

0.095 0.117 0.462

-1.415

0.655

0.011

1.285 0.364 0.096

-1.817

0.894

0.092

1.380 0.429 0.100

-0.089 0.060 0.089 -0.275 0.188 0.145 -0.364 0.221 0.102

0.000 <0.01 -0.002 0.005 <0.01 -0.003 0.001 <0.01 -0.0004

21.370 2.787 <0.01 94.613 8.685 <0.01 115.982 10.222 <0.01

0.481 0.423 0.487

2047.15 2729.05 2826.80

35

Variable

Spatial Lag Model

Mean Greenness (%)

Vacancy Rate (%)

% Black Population

% With Less Than a High School Degree

Per Capita Income (in $1000's)

Population Density (per mi²)

Intercept

Lag Coefficient (Rho)

R-squared

AIC

By examining the relationship between vegetation and crime in the high crime

mid-sized city of Toledo, OH, there is sufficient evidence proving the important role that

vegetation plays in influencing crime rates. The negative association between vegetation

and crime was the strongest relationship above all of the sociodemographic factors.

These findings supported other research results from similar studies in large metropolitan

areas, such as Chicago, Portland, Baltimore, Philadelphia, and even smaller cities, like

New Haven, CT (Donovan & Prestemon, 2012; Gilstad-Hayden, et al., 2015; Kuo &

Sullivan, 2001; Troy, Grove, & O'Neil-Dunne, 2012; Wolfe & Mennis, 2012).

The associations between crime and some of the control sociodemographic

variables were not as strong with vegetation, but they were significant nonetheless. The

most reliable variable among the sociodemographic factors was vacancy rate, in which it

proved as a strong positive predictor of all crime types. This falls in line with the idea of

less “eyes on the street” giving way to more crime to take place (Coley, Kuo & Sullivan,

1997; Kuo & Sullivan, 2001a; Kuo & Sullivan, 2001b). Another striking observation

was that unlike the original expectation that higher populated areas would bring more

crime, population density had an inverse relationship with crime across all outcomes.

Violent Crime Property Crime Total Crime

β SE P β SE P β SE P

0.066 <0.01 0.211 <0.01 0.247 <0.01

0.040 <0.01

-1.338

0.599 0.125 <0.01 0.147 <0.01

0.018 <0.01 0.055 0.963 0.065 0.381

-0.343

0.192

0.058

0.057 0.112 0.610

-0.003

1.249 0.359 <0.01

-1.664

0.780

0.057

1.302 0.420 <0.01

-0.057 0.058 0.329 -0.230 0.186 0.217 -0.276 0.218 0.205

-0.001 0.0001 <0.01 -0.002 0.0004 <0.01 -0.003 0.001 <0.01

17.862 2.841 <0.01 87.473 9.757 <0.01 103.077 11.373 <0.01

0.272 0.123 0.182

0.510 0.429 0.501

2033.48 2725.99 2820.16

36

Although population density’s coefficients were low and this relationship was generally

weak, it coincides with the results of a negative relationship founded by (Gilstad-Hayden,

et al., 2015) and (Wolfe & Mennis, 2012). One possible explanation for this is how

spread out Toledo’s population density is across its block groups because of the lack of

permanent residents living downtown and higher densities situated in neighborhoods

outside of the center city. There are still temporary influxes of higher population in the

downtown block groups due to concerts, sporting events, and work, noted by generally

higher crime rates there, but a large number of residents living in close proximity is

generally lacking.

Other important realizations were the positive associations of race and educational

attainment with crime, which tied to disadvantaged neighborhoods. Higher rates of

violent crime were associated with a higher percent of black population. This supports

the idea of nonwhite individuals being more disadvantaged and more apt to commit

crimes due to potential feelings of hopelessness (Kelly, 2000). Greater percentages of

individuals with less than a high school education were attributed to higher rates of

property crime and total crime, which ties in with the idea of disadvantaged communities

living among areas of higher crime. Per capita income, the only economic variable used,

was insignificant with all crime types. There is no reasoning behind this lack of

significance but it does follow similar findings in other articles comparing crime and

vegetation (Gilstad-Hayden, et al., 2015; Snelgrove, Michael, Waliczek, & Zajicek,

2004).

37

4.2 Discussion

Although the statistical results reveal that the presence of more vegetation is a

clear indicator of lower crime rates, some of the sociodemographic relationships hold

clues as to the type of actions taken by the Cherry Street Legacy Project to mitigate the

impact of crime. Through my interview with Rogalski, she affirmed that blight,

particularly vacant properties, were the first items to be addressed by the organization.

This follows along with my findings of high vacancy rates being associated with higher

crime rates, and the fact the Cherry Street Legacy Project was making it a priority to

lower vacancies reassured this presumption. These actions also supported the concept

behind the “broken windows theory” because if a rundown property is transformed into

attractive greenery or landscaping, it lowers the chances for more houses to become

vacant nearby and for criminal activity to occur.

An example of a successful neighborhood-led crime reduction initiative took

place in the year 2016 on Mentor Drive off Cherry Street between Central Avenue and

Manhattan Boulevard. Mentor Drive was coined the name “Murder Alley” by residents

due to its association with violent crime and shootings. Mentor Drive was a cul-de-sac

consisting of 14 parcels, 11 of which were acquired by the Land Bank following tax

foreclosure. Four of those parcels were already vacant, while 7 were torn down.

Coordination between St. Vincent’s Cherry Street Legacy, the City of Toledo, the

Toledo-Lucas County Sustainability Commission, and a local non-profit farming group,

the Glass City Goat Gals, led to a unique solution of adaptive reuse for the 14 vacant

parcels by creating an urban farm, complete with goats, a butterfly house, and a vegetable

garden. Volunteers included high school students from Toledo’s Scott High School, who

38

were members of a summer youth program called the Green Team, and helped change

the street’s name to “Glory Road”. This illustrates that Cherry Street Legacy, along with

engagement and help from the community could create something new out of the street’s

former, violent past and change one’s perception of the once feared street.

39

Figure 4.1.2 These two photos represent an environmental change from a street that was

once filled with abandoned homes in August 2011 and crime (top) to one

that now has its own urban farm, taken from May 2019 (bottom).

Source: Google Earth Street View

Another successful project took place in 2013 involving a vacant nursing home,

Arbors at Toledo located on Cherry Street, with the help of the Land Bank, City of

Toledo, and Bronson Place Association. The building was demolished; the land was

cleared and then transformed into an arboretum filled with several different species of

trees and plants surrounded by fencing. The eyesore that was one the abandoned nursing

home is now a beautiful natural area that the public can utilize.

I was fortunate enough to walk with Rogalski to observe both Glory Road, the

Bronson Place arboretum, and the entirety of the Legacy Neighborhood as she explained

CPTED features and other projects along the way. There were numerous other areas in

the neighborhood where vacant homes were torn down and replaced with shrubs, trees

40

and green space. It was striking to me that simple changes to enhance sight lines

allowing residents to see across the street, along with creating opportunities to experience

nature and reducing crime opportunities, could have such a positive impact on the

community as whole and create a safer environment.

When I asked Rogalski about what types of crime were the most common in the

neighborhood at the start of the project, she said burglaries were the highest. After

CPTED features were put into place in several areas throughout the neighborhood, such

as planting shrubs 2 feet or lower and trimming trees to a height of 6 to 8 feet, she

claimed it helped decrease burglaries and property crimes in a period of three years. This

was mainly due to increasing line of sight and providing less ambush points (Rogalski,

2016). The Land Bank played an integral part in its ability to acquire and demolish

abandoned homes, which helped rid these areas once filled with crime and replacing them

with sustainable land for which residents could utilize. In making these once rundown

spaces usable again with attractive landscaping, it installed a sense of care and promoted

surveillance, which deters potential criminals away.

Another example that benefited the region in lowering vacancies involved the

help of NeighborWorks Toledo Region (NTR), a Toledo-based housing development and

redevelopment agency, and the Land Bank. In 2011, NTR collaborated with Cherry

Street Legacy to create a housing tax credit proposal known as Legacy Homes Project.

This program involved the Land Bank’s acquisition of a 40-lot area in Central Toledo. In

2013, 40 affordable single-family homes were constructed on these mostly vacant parcels

for low-income residents. The lease-purchase program allowed tenants to rent these

homes and eventually purchase them after 15 years. Overall, this project aided the region

41

in lowering vacancies by supplying more affordable housing options for low-income

residents in an otherwise blighted area. Building more homes also has potential to lower

crime due to increasing the amount of “eyes on the street”. This contradicts the statistical

finding, however, of lower population densities being associated with higher crime rates

since densities are being increased with new housing.

Using the Cherry Street Legacy Project as a case study presents a local example

of CPTED concepts on display through their work in the community that go beyond just

physical design. The effect of these projects does more than lowering the impact of

crime from these areas. Overall, these initiatives promote social cohesion, community

connectivity, community culture, and threshold capacity, which are second-generation

CPTED principles (Saville & Cleveland, 2006).

Social cohesion points to enhancing relationships between residents and

celebrating their diversity. This involves the collaboration of neighbors and other

community groups to establish the problems facing the community on which to apply

CPTED strategies to provide a long-term vision with a singular goal. This cohesion

began in 2009 by coordinating meetings with local government agencies, nonprofit

entities and neighborhood residents to establish a clear plan for what needs to change for

the community to prosper. The collaborative efforts with the City of Toledo allowed the

Cherry Street Legacy Plan to be adopted into the City of Toledo’s 20/20 Plan, which is

the city’s comprehensive master plan, making it an important part of the community

fabric.

This leads to the second concept of community connectivity, which states that

partnerships “are the foundation to coordinating activities and programs with and

42

between government and nongovernment agencies” (Love, 2015). The connectivity

developed through Cherry Street Legacy’s local partnerships are helpful in creating a

stronger sense and feeling of empowerment to the community. Nurturing these

relationships is detrimental to fulfilling many of its efforts. Since residents of the Legacy

Neighborhood feel that they are supported by other community groups in their efforts to

make a vibrant community, it instills a sense of pride that they have for their

neighborhood and makes them more apt to look out for others. This means they may feel

more inclined to report criminal behavior, take better care of their own property, and

support their fellow neighbors by reaching out to them in times of need.

Community culture is the next idea of new-age CPTED principles, which

establishes a sense of community by setting up events, such as festivals or cultural

celebrations. These things can make people’s perceptions of a community more positive

and encourage neighbors to look out for others’ well-beings. Even if it is a small

example, the creation of Glory Road’s urban farm gives the community a sense of culture

and optimistic outlook by allowing the residents to care for livestock, a vegetable garden,

and feel a sense of pride that otherwise was shadowed by fear of crime. The Goat Gals

have even had educational events after the establishment of the goat pasture in order to

create opportunities for the public to learn about and experience urban farming.

The last idea is threshold capacity alludes to neighborhoods as living ecosystems

with a certain amount of capacity for types of activities and land uses. Threshold

capacity speaks to incorporating social stabilizers, such as community gardens and

entertainments, while limiting crime generators, such as abandoned homes and bars. The

Cherry Street Legacy Project was cognizant of this threshold and created several social

43

stabilizers to offset its crime generators through its community gardens, arboretums, and

urban farm. Unfortunately, disorderly conduct, characterized by threats and menacing,

remains a major issue in the Legacy Neighborhood due to the amount of and proximity to

alcohol-related retail stores in the area, which include liquor stores and gas stations.

These establishments are not easy to eliminate from a community and are sometimes

unavoidable in the harm they cause. However, by continuing to implement these CPTED

strategies, overall perception of crime and wellbeing can improve over time.

44

Chapter 5

Limitations and Conclusion

5.1 Limitations

This study has some notable limitations. First, it is important to note that

statistical analyses utilizing aggregated data are subject to the modifiable areal unit

problem (MAUP), which means results may differ depending on the scale and shape of

the unit used to aggregate the data (Wong, 2009). In this case, 2016 Census block groups

were clipped to Toledo’s city limits, where crime point data was aggregated, along with

NDVI pixel data. The sociodemographic census data was already aggregated into block

groups also. Different variations of units, however, could skew the results in either

direction, which is cause for some concern. Overall, my results from the regression

models were consistent with several research papers whose units varied from census

tracts to block groups.

A second limitation is concern over the use of NDVI as a means to measure

vegetation. Although the measure of NDVI is accurate in assessing concentrations and

densities vegetation and has been widely applied to various studies as a strong predictor

of crime and other socioeconomic variables, it is a rather simple scalar measurement that

45

can potentially mask key variability in vegetation character that may moderate its

relationship with crime (Wolfe & Mennis, 2012). This variability includes NDVI’s

inability to identify the amount of specific types of vegetation and if they have an effect

on visibility as it pertains to criminal activity. The degree of maintenance and

management of a space, or cue to care, were terms mentioned throughout my research

that are speculative to assess when evaluating NDVI data. For instance, it is not possible

to determine whether a block group with a high NDVI value has several abandoned lots

that are unmaintained or if it contains a well-manicured lawn and is a frequented field for

recreational use. These land maintenance factors can adversely affect the rate of crime

occurrence, since lack of care can be a venue for criminal activity compared to a well-

kept lot (Kuo & Sullivan, 2001b). This would involve intricate data from various sources

to account for and accurately measure the degree of maintenance and vegetation type. It

would also be difficult to aggregate maintenance and measure vegetation type at the

block group level, which would prove more labor intensive given a citywide scale. It is

entirely possible, however, as Donovan and Prestemon (2012) analyzed the effect that the

number and size of trees had on crime at the parcel level in Portland, OR. Perhaps using

a smaller region or unit area could explain the situation more accurately, but my research

does an adequate job presenting a more holistic and broad view of the vegetation versus

crime relationship.

Although there was no clear evidence revealed through my research that

vegetation suppressed crime due to psychological factors, it would cause one to think that

crime would be displaced from one neighborhood to another with less vegetation since

lack of natural settings can cause precursors to violent behavior. Analyzing this would

46

require temporal crime and vegetation data from multiple years, which is entirely

possible. Wolfe and Mennis (2012) explained, however, that although crime patterns

may shift across different neighborhoods of a city over an order of several years,

concentration continues in mostly impoverished, disadvantaged neighborhoods. This

leaves me to wonder if I effectively analyzed factors concerning the relationship between

disadvantaged populations and crime. Poverty rate is the one variable I was hoping to

include, however, the pre-analysis phase led me to select per capita income to measure

economic stability based on the better fit and heuristics statistics garnered from the

results. Even though per capita income did not have significant association with crime, it

created better results when combined with the other control variables in the model.

Another shortcoming is that my research does not necessarily aid in the

application of CPTED features since it only views vegetation and crime from a citywide

lens and is not neighborhood-based. If I were to analyze the data from a closer

viewpoint, such as in the Legacy Neighborhood, I could potentially identify crime

hotspots and determine whether implementation of different types of vegetation and

landscaping could lower crime occurrences. Temporal studies of changes in crime

hotspots could be another part of future research to investigate whether the changes

created by Cherry Street Legacy Project actually influenced and lowered the number of

crimes in the neighborhood over time. Unfortunately, the project was inactive after the

year 2018 and I could not obtain concrete evidence and statistics involving real crime

reports from them. Despite these limitations, my quantitative research provides evidence

of a strong association between vegetation and crime rates in Toledo as a whole.

47

5.2 Conclusion

Despite these limitations, several important conclusions can be made from this

research. Toledo, OH is another example of a city whose vegetation was directly

associated with lower rates of violent, property, and total crime. The statistical evidence

in addition to the case study findings adds to the evidence that creating more attractive

vegetative environments in urban areas may contribute to a reduction in crime. As was

discussed through my case study, this depends on the type of vegetation, its placement,

and the degree of which it is maintained as big factors for how it will influence crime and

fear of crime. For example, an overgrown lot with dense shrubbery will not have the

same effect as a well-maintained garden or delicately landscaped property.

Sociodemographic factors also held weight in explaining the association with crime.

There was great significance between higher housing vacancies and increased crime

rates, along with lower population densities being associated with more crime. GIS

played a significant role in these findings and the Cherry Street Legacy Project’s efforts

supported these results through their efforts.

Using a mixed methods approach, in the form of regression analyses and a case

study, was an important aspect of this research. By attempting different statistical

methods from contemporary research studies, I discovered that the OLS regression alone

does not provide the best results since spatial autocorrelation was a common problem.

Utilizing spatially adjusted regressions (spatial error and spatial lag) to analyze my data

allowed me to determine that the lag model held the most predictive power. By including

the Cherry Street Legacy Project as a local example of an organization attempting to

lower crime through CPTED, it allowed my statistical findings to be verified and

48

compared to a real-world application. I found that a few variables, vacancy rate and

population density, were being addressed through Cherry Street Legacy’s efforts by

removing vacant houses and replacing them with usable green space to lower crime. It is

unclear what the exact impact of this has had, however, given the lack of quantitative

data from the organization, even though Rogalski stated property crimes were reduced

since the efforts began.

There is potential for these findings to provide insight into implications for

citizens and agencies throughout Toledo, OH and the region. The Cherry Street Legacy

Project exemplifies the strategy of community connectivity by establishing relationships

with agencies that have become assets for the organization, such as the City of Toledo,

Lucas County Land Bank, Glass City Goat Gals, and countless other nonprofit

organizations. These partnerships were beneficial in helping to plan and coordinate

events and activities, along with allowing Cherry Street Legacy to establish a stronger

sense of community through their work that goes beyond the Legacy Neighborhood.

Other cities that are struggling with urban crime in Northwest Ohio can look at this as a

model to follow if they want to tackle the same objectives and be more connected with

each other.

If it is proven that the actions taken by Cherry Street Legacy have indeed

decreased crime as previous research suggests, more people may want to move to these

areas over time if they have the perception of being safer. Additionally, this will have a

positive impact by increasing property values. If these residents have more pride for their

community, they may in turn want to maintain their own properties better given that the

surrounding areas have been transformed. This could even benefit nearby businesses as

49

the potential for greater population densities may increase the amount of people

frequenting stores, which may boost spending habits and thus positively affect the local

economy.

The projects shared by this local effort surround the concept of vegetation and its

importance in an urban setting to alleviate crime, which is the main point of this research.

It also demonstrates how cohesion with community partners, creating a greater sense of

connectivity, culture, and balance of activities and land uses can improve a community’s

wellbeing and create a positive outlook. Overall, this project explains the importance of

promoting more vegetation in cities as it has positive impacts that can help cities thrive

and become livable for future generations.

50

References

Baxter, J. (2016). Case studies in qualitative research. In I. Hay (ed.), Qualitative

Research Methods in Human Geography, Fourth Edition, 130-146. Canada:

Oxford University Press.

Coley, R. L., Kuo, F. E., & Sullivan, W. C. (1997). Where Does Community Grow?: The

Social Context Created by Nature in Urban Public Housing. Environment and

Behavior, (29)4:468-494.

Donovan, G. H., & Prestemon, J. P. (2012). The Effect of Trees on Crime in Portland,

Oregon. Environment and Behavior, 44(1): 3-30.

Flyvbjerg, B. (2011). Case Study. In N. K. Denzin and Y. S. Lincoln (eds.), The Sage

Handbook of Qualitative Research, 4th Edition, 301-316. Thousand Oaks, CA:

Sage.

Gilstad-Hayden, K., Wallace, L. R., Carroll-Scott, A., Meyer, S. R., Barbo, S., Murphy-

Dunning, C., & Ickovics, J. R. (2015). Research note: Greater tree canopy cover is

associated with lower rates of violent and property crime in New Haven, CT.

Landscape and Urban Planning, 143:248-253.

Jeffrey, R. C. (1971). Crime Prevention Through Environmental Design. American

Behavioral Scientist.

Kaplan, S. (1995). The Restorative Benefits of Nature: Towards An Integrative

Framework. Journal of Environmental Psychology, 15:169-182.

Kelly, M. (2000). Inequality and Crime. The Review of Economics and Statistics, 82(4),

530-539.

Kuo, F. E., & Sullivan, C. W. (2001). Environment and Crime in the Inner City: Does

Vegetation Reduce Crime? Environment and Behavior, 33(3), 343-367.

51

Kuo, F. E., & Sullivan, W. C. (2001). Aggression and Violence in the Inner City: Effects

of Environment via Mental Fatigue. Environment and Behavior, 33(4), 543-571.

Love, P. C. (2015). A Review and Current Status of Crime Prevention through

Environmental Design (CPTED). Journal of Planning Literature, 30(4):393-412.

Matthews, S. A. (2006, June 9). GeoDa and Spatial Regression Modeling. State College,

Pennsylvania, USA.

McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and

Stan. Boca Raton: CRC Press.

Mercy Health Partners. (2009). The Cherry Street Legacy Plan.

Nagelkerke, N. (1991). A note on a general definition of the coefficient of determination.

Biometrica, 78(3), 691-692.

Nasar, J. L., & Fisher, B. (1993). ‘Hot spots’ of fear and crime: A multi-method

investigation. Journal of Environmental Psychology, 13(3):187-206.

Newman, O. (1972). Defensible Space: People and Design in the Violent City. London,

UK: Architectural Press.

Palmer, M., & Chelmu, N. (n.d.). The Cherry Street Legacy Project - Mercy St. Vincent

Medical Center. Retrieved from LocalWiki:

https://localwiki.org/toledo/The_Cherry_Street_Legacy_Project_%E2%80%93_

Mercy_St._Vincent_Medical_Center

Rogalski, K. (2016, October 17). Cherry Street Legacy Project. (T. Kosmyna,

Interviewer)

Sauter, M. B., Frohlich, T. C., & Comen, E. (2016, November 6). 25 Most Dangerous

Cities in America. Retrieved from 24/7 Wall St.: https://247wallst.com/special-

report/2017/11/06/25-most-dangerous-cities-in-america-2/7/

52

Saville, G., & Cleveland, G. (2006). CPTED and the social city: The future od capacity

building. In G. Saville, C. Genre, & M. Galeano, The CPTED Journal (pp. 1(2),

43-50). Millford, CT: Alternation.

Schrank, A. (2006). Case studies: Case-based research. In E. Perecman & S. R. Curran

(eds.), A handbook for social science field research: Essays & bibliographic

sources on research design and methods, 21-38. Thousand Oaks: Sage

Publications.

Shaffer, G. S., & Anderson, L. (1985). Perceptions of the security and actractiveness of

urban parking lots. Journal of Environmental Psychology, 5(4):311-323.

Snelgrove, A., Michael, J., Waliczek, T., & Zajicek, J. (2004). Urban Greening and

Criminal Behavior: A Geographic Information System Perspective.

HortTechnology, 14(1), 48-51.

Troy, A., Grove, J. M., & O'Neil-Dunne, J. (2012). The relationship between tree canopy

and crime rates across an urban-rural gradient in the greater Baltimore region.

Landscape and Urban Planning, 106:262-270.

U.S. Census Bureau. (2018). U.S. Census QuickFacts. Retrieved from U.S. Census

Bureau: https://www.census.gov/quickfacts/toledocityohio

U.S. Department of Justice. (2016). Offenses Known to Law Enforcement. Retrieved from

2016 Crime in the United States: https://ucr.fbi.gov/crime-in-the-u.s/2016/crime-

in-the-u.s.-2016/topic-pages/offenses-known-to-law-enforcement

Wilson, J. Q., & Kelling, G. L. (1982, March). Broken Windows. The Atlantic.

Wolfe, M. K., & Mennis, J. (2012). Does vegetation encourage or suppress urban crime?

Evidence from Philadelphia, PA. Landscape and Urban Planning, 108: 112-122.

Wong, D. (2009). Chapter 7: The Modifiable Areal Unit Problem (MAUP). In S.

Fotheringham, & P. A. Rogerson, The SAGE Handbook of Spatial Analysis (pp.

105-122). London: Sage Publications.