Using Geographic Information Systems to Measure Walkability in Cincinnati, Ohio

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2 Using Geographic Information Systems to Measure Walkability in Cincinnati, Ohio A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Community Planning in the School of Planning of the College of Design, Architecture, Art and Planning by Jamie C. Lemon B.A. West Virginia University May 2010 Committee Chair: Christopher Auffrey, PhD Committee Member: Changjoo Kim, PhD

3 Abstract Preventable diseases resulting from unhealthy lifestyles are an unfortunate reality for many Americans. Because walking is one relatively easy way to improve individual health, the factors that influence people to walk are the subject of considerable research efforts. Thus, the purpose of this study is to investigate walkability through an exploratory analysis of built environment constructs that have been connected with active transportation behaviors. Additionally, an effort is made to measure walkability within a cluster of neighborhoods located in Cincinnati, Ohio. Results from this study suggest that a walkability coverage is attainable using quantitative measures and readily available GIS data. The methodology employed, a walkability index composed of four physical environmental factors identified from previous research, highlights variations within the study area and can easily be applied to other cities. Health data and travel behavior surveys can be used to enhance this type of walkability research, further expanding on our understanding of how urban environments influence physical activity behaviors. ii

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5 Acknowledgements I would like to thank my committee chair, Professor Auffrey, for his time, effort and patience in guiding me through this challenging process. I would also like to thank my other committee member, Professor Kim, and my supervisor at OKI Regional Council of Governments, David Shuey, for their GIS contributions and their willingness to help with all technical aspects of this study. Special thanks to Professor Zapata and Robyn for their unique insights and for motivating me to put pen to paper. Many thanks are due to my parents and my brothers; without their continual support, I would not have completed my graduate studies in one piece. Finally, I will always be indebted to Grace Ford, Rachel Love-Adrick, Yoonsun Chang, and Krishna Matturi for all of the absurdity, laughter, and encouragement that got me through the rough patches. Ya ll are awesome. iv

6 Table of Contents 1) Introduction ) Purpose of Study ) Research Questions ) Background: Public Health and Urban Planning ) Organization of Thesis ) Literature Review ) Obesity, Physical Activity and Planning Research ) Obesity in the United States ) Health Concerns for Cincinnati Residents ) Direct Medical Costs of Obesity ) Physical Activity ) Environmental Influences of Physical Activity ) Evidence from Transportation and Planning Research ) Density, Land Use Mixture and Street Connectivity ) A Review of Walkability Indices ) Factors Recognized from Previous Literature ) Methodology ) Using Cincinnati Data to Create a Walkability Index ) Study Area ) Spatial Units of Analysis ) Spatial Datasets Included ) Data Modifications ) Walkability Index Inputs ) Street Connectivity v

7 3.6.2) Retail Floor Area Ratio (FAR) ) Residential Density ) Land Use Mixture ) Calculating Walkability Scores ) Intersection Density, Retail FAR, and Residential Density Workflow ) Land Use Mixture Workflow ) Results ) Intersection Density ) Retail Floor Area Ratio ) Residential Density ) Land Use Mixture ) Composite Walkability Index ) Comparing High and Low Walkability Scores ) Validating the Walkability Index ) Discussion ) Further Developments and Applications of Walkability Indices ) Challenges in Measuring Walkability ) Challenges Specific to this Thesis ) Modifications to Existing Walkability Indices ) Users of the Walkability Index ) Conclusion Bibliography Appendix vi

8 List of Figures Figure 1: Change in National Obesity Rates from 1985 to 2010 (CDC, 2012)... 4 Figure 2: Prevalence of Obesity Among Adults Age 20 and Over (NCHS, 2012)... 7 Figure 3: Prevalence of Obesity Among Children (NCHS, 2012)... 8 Figure 4: Reported Health Conditions of Greater Cincinnati Adults (HFGC, 2010) Figure 5: Obesity Rates of Adults in Greater Cincinnati (HFGC, 2011) Figure 6: Equation Used to Calculate Walkability Scores (Frank et al., 2010) Figure 7: Walkability Index (Kuzmyak, Baber, & Savory, 2006) Figure 8: Walk Opportunities Index (Kuzmyak, Baber, & Savory, 2006) Figure 9: Walking Permeability Distance Index (Allan, 2001) Figure 10: Walking Permeability Time Index (Allan, 2001) Figure 11: Cincinnati Walkability Study Area (Author, 2012) Figure 12: Census Block-Groups Located Within Study Area (Author, 2012) Figure 13: Example of Unique Intersections (Author, 2012) Figure 14: Net Retail FAR Ration (Leslie et al., 2007) Figure 15: Retail Building to Commercial Parcel Ratio (Author, 2012) Figure 16: Land Use Entropy Score (Frank et al., 2010) Figure 17: Example of Heterogeneous Land Use Mixture (Author, 2012) Figure 18: Portion of Walkability Index Model Workflow (Author, 2012) Figure 19: Intersection Density by Census Block-Group (Author, 2012) Figure 20: Retail Floor Area Ratio by Census Block-Group (Author, 2012) Figure 21: Residential Density by Census Block-Group (Author, 2012) Figure 22: Land Use Mixture by Census Block-Group (Author, 2012) Figure 23: Composite Walkability Index (Author, 2012) Figure 24: Least Walkable Census Block Group in Study Area (Author, 2012) Figure 25: Most Walkable Census Block Group in Study Area (Author, 2012) Figure 26: Street Intersection Topology Error Example (Author, 2012) Appendix Figure 1: Alternate Land Use Mixture Model(Dr. Changjoo Kim, 2012)

9 1) Introduction 1.1) Purpose of Study A growing interest in improved individual health has led to collaborative efforts that investigate the relationship between built environment features and physical activity resulting from community design. Such collaborative efforts already exist in the Greater Cincinnati region, as indicated by WeTHRIVE movement. However more research is needed to gain a better understanding of the intersection of urban planning and public health, particularly in southwest Ohio. There is growing evidence that suggests moderate physical activity, such as walking, contribute to healthier lifestyles. Thus, the purpose of this thesis is to create a walkability index based upon objective built environment features and then apply it to a cluster of urban neighborhoods in Cincinnati using geographic information systems (GIS). This will be the first explorative study of its kind in Cincinnati, and the workflow created in this thesis can serve as a base for future research. The six neighborhoodswere chosen as the geographic area of analysis for a few reasons, which will be reviewed in later sections. Although various socioeconomic characteristics inevitably contribute to the health of those residing in the neighborhood (and cannot be overlooked when considering which factors influence personal health), the area serves as an acceptable foundation for analyzing select environmental features. 1.2) Research Questions The research questions presented in this section will define the focus of this thesis paper. The topic of interest is the relationship between the built environment and 2

10 physical activity behaviors and the following questions will attempt to be answered throughout this document: Which built environment features are most important when objectively measuring walkability? Which geographic scale is most appropriate for understanding walkability? 1.3) Background: Public Health and Urban Planning Altering the physical environment of cities as a means of improving the overall quality of life is not a new concept. A review of historical developments in most American cities reveals a time when communicable diseases and general squalor were serious concerns for residents and elected officials (Frumkin et al., 2004). While many at the time viewed urban living as unsanitary and dangerous, there existed far more opportunities for individual economic prosperity. The sudden influx of new residents, particularly during the Industrial Revolution, placed a strain on city infrastructure. Basic facilities that are taken for granted today, such as garbage collection and properly ventilated housing, were the result of planning efforts responding to the aforementioned health concerns. Fortunately, incidents of infectious disease epidemics are far less common in modern times thanks to advances in medical technologies and a greater awareness of personal hygiene (Frumkin et al., 2004). However, scholars have identified a new wave of health risks occurring among American children and adults. Over the past two decades, there has been a significant increase in obesity rates and instances of preventable diseases resulting from unhealthy lifestyles. The United States Centers for Disease Control and Prevention allows citizens to review the spatial 3

11 distribution of obesity on their website, using a map of the nation and its associated obesity rate as the measure. The change from 1985 to 2010 is very telling, as indicated by Figure 1 below (Centers for Disease Control and Prevention, 2012). Figure 1: Change in National Obesity Rates from 1985 to 2010 (CDC 2012) While there are countless explanations surrounding this dramatic change, perhaps the one most relevant to urban planning professionals and academics is how characteristics of the built environment influence human behavior. Specifically, it is believed that certain factors, such as street connectivity and mixed land uses, contribute to increased levels of physical activity among residents. 1.4) Organization of Thesis This thesis began with a description of the purpose behind this research study and followed with a brief overview of the interconnectedness of urban planning and public health. Due to the collaborative nature of the two respective disciplines, anoverview was necessary to provide context for upcoming chapters. Chapter 2 presents the literature surrounding obesity, physical activity and urban planning research. Deteriorating health conditions that are occurring throughout the United 4

12 States are introduced, with a more focused review of health statistics in the Greater Cincinnati region. Physical constructs of urban environments thought to influence walkability will subsequently be reviewed, followed by previous research that comprehensively measures community design based on said physical constructs. Chapter 3 introduces the methodology that was created based on previous research and the aforementioned research questions. The development of a walkability index applicable in a cluster of neighborhoods in Cincinnati is the end result. All methodological considerations were based on and informed by previous literature surrounding walkability. Chapter 4 presents the results obtained by using the defined methodology and identifies any discrepancies introduced in the research process. The paper will end with a discussion of future developments for walkability studies, among other considerations, and policy recommendations will be presented to compliment this type of research. The paper will end with conclusions drawn from the study based on the proposed research questions. 5

13 2) Literature Review 2.1) Obesity, Physical Activity and Planning Research To understand the context of a walkability index that is applied in Cincinnati, a comprehensive literature review that addresses obesity, physical activity, and measures of walkability must be presented. This review will begin with a look at the growing number of overweight and obese persons in the United States, followed by a more specific look at local health statistics in the Greater Cincinnati region. Increasing rates of obesity and other preventable diseases have various costs associated with them and there are potential savings in reducing these numbers. Physical activity is one way to combat health issues associated with obesity, and this solution will be reviewed at greater length in the second section of this chapter. The final section of this literature review will focus on the growing intersection of public health, transportation, and community design and the impact the local built environment has on walking behaviors. Concluding remarks will identify the characteristics used to create the walkability index for this study. 2.2) Obesity in the United States According to the Centers for Disease Control and Prevention (2012), approximately one in three United States adults is considered obese. Additionally, older Americans have higher rates of obesity, as indicated in Figure 2 below. The propensity of declined health with age will only continue to be a concern, as the number of Americans ages 65 and older is expected to double by 2050 (Jacobsen et al., 2011). It appears as though rates of obesity have been leveling off in recent years, but this is no 6

14 reason to discontinue researching how individuals can improve their personal health (Ogden et al., 2012). Figure 2: Prevalence of Obesity Among Adults Age 20 and Over (NCHS, 2012) Childhood obesity is another serious concern. Nearly 17% of all children and adolescents in the United States were considered obese between 2009 and 2012 (Ogden et al., 2012). The prevalence of obesity differs slightly between boys and girls; obesity rates among girls appear to be decreasing slight, while among boys it appears to be plateauing (see Figure 3 below). Programs exist to encourage children to develop healthier lifestyle habits, such as Safe Routes to Schools, however more work needs to be done (Health Foundation of Greater Cincinnati, 2010). 7

15 Figure 3: Prevalence of Obesity Among Children (NCHS, 2012) Other demographic characteristics have been associated with obesity and associated preventable diseases. In his recent book, Toward the Healthy City, author Jason Corburn approaches health issues from a social equity standpoint (2009). He suggests that political, social and economic constructs are significant indicators of public health and that a new policy framework is needed to ensure an improvement in the wellbeing of our communities. Corburn (2009) notes that health inequities are more prominent in disadvantaged groups, particularly among low-income, racial minorities, and women. Identifying why individuals are overweight or obese is a challenge many health professionals have been researching for quite some time. It appears as though many different variables, ranging from social factors to environmental factors, influence the 8

16 health and wellbeing of our communities. The fields of urban planning and public health have become so comprehensive that a large-scale review of all obesity related variables would be near impossible. Thus, the primary concern of this literature review and subsequent research process will focus on measurable built environment variables that may influence physical activity behaviors ) Health Concerns for Cincinnati Residents Unfortunately, obesity rates and other indicators of health among Cincinnati residents appear consistent with national statistics. In 2010, the Health Foundation of Greater Cincinnati published a Community Health Status Survey that provided an overview of the current health conditions existing within the metropolitan area. Various reports were issued, covering topics ranging from health insurance status, smoking habits, and fast food consumption. Survey methodology involved phone calls to a random sample of residents, with subgroups consisting of African Americans and those who only own a cell phone (Health Foundation of Greater Cincinnati, 2010). While there are always gaps in this type of data collection, the Greater Cincinnati Community Health Status Survey is a useful tool for understanding the general health of the region. For example, it was concluded that approximately 2 out of 3 Cincinnati adults were considered overweight or obese; this constitutes a 10% increase from 1999 (Health Foundation of Greater Cincinnati, 2010; Cincinnati Herald, 2010). This statistic suggests that while national obesity rates may have plateaued, there is still a steady increase in obesity rates existing in Cincinnati. 9

17 Figure 4: Reported Health Conditions of Greater Cincinnati Adults (HFGC, 2010) Given the predominance of obesity in the Cincinnati area, it is not surprising that other factors considered in the community survey displayed similar trends. Rates of physical activity, as determined by those meeting the Centers for Disease Control and Prevention s daily-recommended guidelines, decreased as displayed in Figure 5. 10

18 Figure 5: Obesity Rates of Adults in Greater Cincinnati (HFGC, 2011) 2.2.2) Direct Medical Costs of Obesity The long-term effect of poor health transcends individuals and their families. Health expenditures calculated at the national level have dramatically increased over the past few decades, with $2.6 trillion spent in 2010 versus $256 billion spent in 1980 (National Health Statistics Group, 2012). Many variables have been identified as possible drivers behind this increase, with little consensus over which is most influential. Estimating the total cost of obesity is not a simple task. There are two main components that have been classified in calculating obesity costs, direct medical costs and other indirect costs (Wolf & Colditz, 1998). Direct medical costs are easier to determine, as they include preventative, diagnostic, and treatment services related to personal health. A study published by the Centers for Disease Control and Prevention (2009) estimated that approximately 75% of all medical care expenses in the United States were associated with the treatment of some chronic disease. Examples of treated 11

19 chronic diseases included in the study were diabetes, heart disease, stroke, and cancer. Medical studies have proven that physically active persons have a significantly lower risk of developing the aforementioned diseases, particularly heart disease and type 2 diabetes (Bassuk& Manson, 2005).While many individuals inflicted with such ailments can attribute them to environmental or genetic factors, others do not properly care for their health. 2.3) Physical Activity Studies indicating the connection between physical activity and improved health are countless. It has been well documented that moderate and regular physical activity is one of the best actions individuals can take towards achieving a healthier lifestyle (Owen, et al., 2000; Troped et al., 2001; Giles-Corti & Donovan, 2002; Brown, 2004; Bassuk & Manson, 2005). Physicians and other medical experts recommend that 30 minutes of physical activity is needed daily in order to obtain any relevant health benefits (Sallis et al., 2004). Walking is the simplest and most common form of physical activity among adults (Saelens, Sallis & Frank, 2003). Given this, it makes sense that most suggestions towards improving community health involve a physical activity component. Many local, state and national health agencies have initiated programs that attempt to promote physical activity, with an emphasis on walking and its health benefits. These initiatives were the result of years of research and despite this, more work must be done to improve the health of our communities. 12

20 2.3.1) Environmental Influences of Physical Activity Many studies have looked at the influence of the built environment on instances of physical activity. A recent review of pedestrian walkability indices listed and categorized the many variables that are thought to influence physical activity (Maghelal & Capp, 2011). This study proposed the following ten constructs of the built environment that influence walkability and can be measured using GIS software (Maghelal & Capp, 2011): Distance Sidewalks Roads Intersection Vehicles Lateral Separation Demographics Land-Use Safety Comfort/Convenience This study was not used to draw conclusions about the influence of the built environment and walkability, but rather create a comprehensive list of attributes that previous literature had included in their respective research projects. Out of the twenty five pedestrian indices that were audited, land use mixture and sidewalk availability were cited as the most frequently included constructs used in previous studies (Maghelal & Capp, 2011). 13

21 A similar review of physical activity research grouped eighteen different studies into different classes in order to provide a common framework (Humpel et al., 2002). The factors identified, such as access to bike paths and perceived safety, were all linked with increased physical activity among residents. Based on these factors, Humpel et al. (2002) grouped the eighteen studies into five different classes: accessibility, opportunity for activity, weather, safety and aesthetics. Unlike Maghelal and Capp s (2011) review of built environment constructs, whose main purpose was to identify the various standardized measures of the built environment, the aforementioned review of physical activity and walkability studies actually gave indications of which factors were positively or negatively correlated. Aesthetics and access were important in many of the studies, particularly when certain amenities like footpaths and parks were available; conversely, weather and safety factors did not seem to have any positive or negative association with physical activity (Humpel et al., 2002). Researchers Porta and Renne (2005) were interested in which neighborhood components enhanced urban environments; specifically, they wanted to know which measurable variables that existed within a geographic area promoted social sustainability. While sustainability is inherently difficult to define, yet alone operationalize and measure, it was interesting to review how the authors approached their research questions. Their comprehensive list of eight urban fabric indicators, many of which are subjective and difficult to quantify, consist of the following elements (Porta & Renne, 2005): 14

22 Accessibility (pedsheds): shows the interconnectedness of the street network as it pertains to realistic, accessible, and safe destinations within a buffer Land use diversity: measures the variety of land uses within a defined area Public/private realm: identifies where pedestrians can and cannot go Natural surveillance: also called front and back mapping, which identifies areas that exist in the street network that have active building frontages Permeability: describes the type and number of intersections that influence pedestrians in a street network Employment density: shows where employment exists within the urban environment Number of buildings Number of lots Based on these urban indicators, Porta and Renne (2005) concluded that their two areas of analysis were dramatically different in their ability to sustain social capital and sense of place. While this study was not explicitly measuring walkability or public health, it was indicative of the built environment features that influence human behavior. Access to transportation infrastructure is an important concept in many previous studies. For example, Wendel-Vous et al. (2004) used survey data and GIS to study access to walking and cycling pacts, as well as parks and other recreational areas. Using the survey data from participants, it was determined that the aforementioned built environment features influenced walking or bicycling (Wendel-Vous et al., 2004). This conclusion included walking or bicycling for both leisure and commuting, as well as perceived safety among survey participants. 15

23 Policy and environmental factors have also been included in previous studies. Sallis, Bauman and Pratt (1998) reviewed certain natural and man-made environments and their associated existing policies that allowed for the provision of infrastructure. Many factors were included in this study: transportation infrastructure, stairway and sidewalk availability, parks, separation of buildings from parking lot using green space, and even weather variables. Using these factors as a basis for measuring walkability, Sallis, Bauman and Pratt (1998) provided policy recommendations that could enhance or promote these facilities as they support physical activity. The physical activity literature presented in this section is not meant to be an allinclusive review, but rather a primer on how previous researchers have approached the subject of walkability and physical environments. Specifically, the topic of which physical constructs influence walking behaviors were presented. In the upcoming section, recent walkability indices will be presented and their methodologies and conclusions will be assessed. An understanding of the identification and relationship between different built environment characteristics is essential in order for any new advancement to occur. 2.4) Evidence from Transportation and Planning Research As presented in the previous section, there is recent emerging evidence that indicates a relationship between the built environments of our communities and resulting physical activity behavior among residents. An increasingly popular tool, walkability indices, has been introduced as one way for characterizing the built environment and ranking areas according to their capacity to support walking and other physical activity behaviors. The indices in this section vary in their methodologies; however the underlying concept is the same. Before elaborating on previous walkability 16

24 indices, the following characteristics will be addressed: density, land use mixture and street connectivity. These characteristics have historically been included as the base for walkability studies ) Density, Land Use Mixture and Street Connectivity Many previous studies have indicated that high density, a variety of land uses and street connectivity are more prevalent in walkable areas, whereas low-density development with homogenous land uses and minimal street connectivity do not promote walkability (Saelens, Sallis & Frank, 2003). Researchers have explored these characteristics and their ability to support non-motorized transport, specifically walking trips, as a means of influencing public health through increased physical activity. This section will delve deeper into the relationship between density, land use, and street connectivity and the associated behavioral impacts that affect walkabliilty. These characteristics deserve a more thorough look, as they are often key elements in constructing walkability indices. A 1995 National Personal Transportation Survey (NPTS) revealed that instances of walking and cycling were five times higher in higher density areas versus lower density areas, indicating a positive correlation between population density and physical activity (Saelens, Sallis & Frank, 2003). An earlier study by Newman and Kenworthy (1991), based on data from 32 cities from all over the world, suggests that there is a positive correlation between population density and the percentage of residents who walk or bicycle to work. Various studies have indicated that mixed land uses tend to support walking and by extension, have an influence on reported body mass index (BMI) (Brown et al., 17

25 2009). BMI is one measure of general weight characteristics among individuals, using height and weight to obtain a value. This measure categorizes people into underweight, normal weight, overweight and obese (National Institutes of Health, 2010). The connection between personal health and physical activity indicates that this measure could be useful in determining the walkability of an area. Brown et al. (2009) reviewed land use and walkability, and separated measures into two groups: overall mixture of land uses and the distances to certain destinations. While there appears to be some consensus behind land use and its influence on walking behaviors, a few recent studies indicate otherwise. Forsyth, Hearst, Oakes & Schmitz (2007) concluded that only 2 out of 44 mixed land use measure had any relationship with walking. Additionally, proximity to certain destinations proved to be an insignificant variable when measuring walkability (Forsyth et al., 2007) ) A Review of Walkability Indices Dr. Lawrence Frank and his colleagues have developed, tested, and applied different variations of walkability indices over the past decade. Perhaps most cited and recreated is their 2009 walkability index created as a part of the Neighborhood Quality of Life Study (NQLS), an initiative that aimed to investigate built environment correlates of adults physical activity. To determine this correlation, researchers developed a methodology that accounted for four different constructs of the built environment that are believed to influence walking behaviors. For the purposes of the NQLS, the following variables were chosen as most appropriate due to data availability (Frank et al., 2010): 18

26 Net residential density: the ratio of residential units to the land area devoted to residential use per block group Retail floor area ratio: the retail building square footage divided by the retail land square footage Intersection density: the ration between the number of true intersection (3 or more legs) to the land area of the block group in acres Land use mix: an entropy score... where the mixture considered five land use types and values were normalized between 0 and 1, with 0 being single use and 1 indicating a completely even distribution of floor area across the five uses The above variables can be easily measured using public GIS data, meaning others can recreate this process. Upon calculating the values for each of the index variables, Frank et al. (2010) normalized each total by using a Z-score. The final index values were the sum of z-scores across the four measures, which can be computed using the expression indicated in Figure 6 below. Walkability = [(2 x z-intersection density)+(z-residential density)+(z-retail FAR)+(z-land use)] Figure 6: Equation Used to Calculate Walkability Scores (Frank et al., 2010) The purpose of this study was to develop a systematic methodology that objectively measured the built environment of designated communities using GIS software. After assigning walkability scores and separating the Census block groups within the study area into different deciles, Frank et al. (2010) were able to look at income and travel survey data within each unit that contained the highest and lowest 19

27 scores and use their analysis to inform transportation policy decisions. Their approach allowed for a more narrowed focus and attempted to draw more relevant conclusions given various constraints. Using the walkability index developed for the NQLS, Frank et al. (2010) concluded that the number walking trips were directly related to the number of vehicle miles traveled reported per household. Using Census data, it was also concluded more people living in walkable areas actually walked to work, regardless of income level (Frank et al., 2010). It was recommended that greater collaboration among different disciplines occur in order for more activity-friendly community transitioning. The Baltimore Metropolitan Council (BMC) introduced a similar study to the aforementioned NQLS walkability index, and it was the extension of a 1996 master s thesis that served as the project s base. Much like walkability studies previously conducted, one intended outcome was a better understanding of the relationship between urban planning and public health. However, instead of using a neighborhood or county, walkability was approached at a larger level. The BMC is a regional planning organization, and thus the scale of the study area included the entire Baltimore region. Additionally, the researchers backing this study were most interested in improving its ability to incorporate land use considerations in its regional transportation planning processes (Kuzmyak, Baber, & Savory, 2006). Due to the various resources available to the BMC (collaboration of trained planning staff and academic professionals, an array of GIS data, and very specific travel behavior surveys, to name a few), the nature of this study was very comprehensive. 20

28 Using a combination of variables, Kuzmyak, Baber, & Savory (2006) were able to create a walkibility index using the equation in Figure 7 below. Walkability = å n I i=1 i Figure 7: Walkability Index (Kuzmyak, Baber, & Savory, 2006) The variables above are identified as follows: I i is equal to one-half for three-way and four-way intersections that involve a principal roadway, or one for any four-way intersections that do not contain a principal roadway and n is equal to the number of intersections located within a 0.25 mile radius of the household (Kuzmyak, Baber, & Savory, 2006). Using this index, values were calculated and ranged from zero to approximately ninety. An interesting variation that the BMC researchers included was a walk opportunities index. They believed that walkability alone was only part of the regional transportation equation and were also interested in what kind of walking opportunities were available to residents. A walking opportunity can be defined as a destination worth walking to, such as a grocery store or a park (Kuzmyak, Baber, & Savory, 2006). To measure this, another formula was developed. Walk opportunities = Figure 8: Walk Opportunities Index (Kuzmyak, Baber, & Savory, 2006) å O n O i=1 W i * S i D i 21

29 In this formula O i is equal to the opportunity within a 0.25 mile buffer of a W i household, represents the importance weight for that opportunity, represents the size factor of the opportunity, and D i is the distance from the household to the opportunity (Kuzmyak, Baber, & Savory, 2006). Based on travel survey data, the actual walking pathway for each opportunity was mapped using GIS technology and a composite walk opportunities index was created. While conceptually, this measure of walk opportunities is intriguing, it may be impractical given time and other resource constraints. The walk opportunities index was an important contribution that allowed for a new quantitative measure of urban design, however the study s comprehensive approach towards the measurement of land use mixtures was particularly telling. These land use factors and their impact on travel patterns are a cause for further research, and reveal that any pedestrian planning decisions should account for land use and transportation connections. Chris Bradshaw presented an even earlier example of a rating system that classified walkability at the 14th International Pedestrian Conference in The characteristics of this index included the following (Bradshaw, 1993): S i An environment equipped with sidewalks, intersections, and other amenities that allow for safe travel by foot A variety of destinations within walking distance A natural environment that alleviates harsh conditions of extreme weather, as well as preventing air pollution and noise from automobiles A diverse local culture that encourages interactions among pedestrians 22

30 The quantitative elements used to measure the above characteristics varied from demographic data to safety perceptions to parking space allotment (Bradshaw, 1993). Using this walkability index, Bradshaw (1993) concluded that proximity factors and street connectivity were most important when determining the pedestrian environment of a neighborhood. A different approach was introduced by Andrew Allan (2001), who created a walking permeability index that was used to assess how well the design of a city caters to walking as a mode of transportation. Using the City of Adelaide, located in the southern part of Australia, as a case study, Allan (2001) measured distance and time variables within the city center. This method did not create a full walkability coverage for the city; however, it provided valuable insight into the GIS methods used to measure walkability. Additionally, it allowed for the analysis of the built environment at a smaller scale using two specific variables. Pedestrian permeability between two destinations can be measured using one of two permeability indices. These indices, seen in Figures 9 and 10 below, measure how easily a pedestrian can navigate through the urban environment. WDPI = DD AD Figure 9: Walking Permeability Distance Index (Allan, 2001) 23

31 WPTI = ADT DDT Figure 10: Walking Permeability Time Index (Allan, 2001) For the walking permeability distance index, the DD variable is equal to the direct distance between the point of origin and the destination while the AD variable is equal to the actual distance executed through the most practical walking route (Allan, 2001). The variables contained in the WPTI equation are exactly the same, but instead of using distance as a tool for measurement, time is incorporated to offer a more realistic assessment. An ideal situation would yield a WDPI or a WPTI score of 1, as the direct distance or time would be equal to the actual distance or time. Allan (2001) chose numerous routes that were often traveled by pedestrians within the Adelaide city center. Perhaps unsurprisingly, most of the routes were deemed extremely walkable using the distance and time indices. Using the walkability data generated from the WPDI and WPTI equations, the varying degrees of accessibility were mapped using ArcView. Identifying which routes were least accessible by foot allows for a better understanding of the urban environment and how it influences pedestrian travel. Similar to walkability indices, which measure an environments ability to support walking, a group of researchers developed a sprawl index and applied it to eighty three large U.S. cities (Ewing, Pendall, & Chen, 2003). Recognizing that land development patterns, specifically suburban sprawl, can be the root of various problems, Ewing, Pendall, & Chen (2003) created a sprawl index to rate areas using the following factors: 24

32 Residential density Neighborhood land use mixture Strength of city centers Accessibility of street network These measures were used to determine the influence of sprawl, defined as low density, segregated land uses, lack of thriving central areas and limited travel choices, on six outcomes. The six outcome variables incorporated into the sprawl index were (Ewing, Pendall, & Chen, 2003): Average vehicle ownership Daily VMT per capita Annual fatality rate Maximum ozone level Share of work trips by transit Share of work trips by walking Based on the sprawl index created, the researchers determined that there was a statistically significant relationship between the input built environment features and the above outcomes. As sprawl decreases (meaning as land densities became greater), nearly all of the above outcomes decreased as well. The final determination of this sprawl index study was that density was particularly influential as it relates to transportation outcomes, while land use was not as significant (Ewing, Pendall, & Chen, 2003). 25

33 2.5) Factors Recognized from Previous Literature Alarming rates of obesity and other instances of unhealthy lifestyles have warranted an increase in public health research, specifically with an emphasis in understanding what has caused this epidemic (Centers for Disease Control and Prevention, 2004). The relationship between sedentary lifestyles and physical activity behaviors are well documented, and has thus resulted in more research into the reasons for this inactivity (Sallis, Bauman & Pratt, 1998). While social indicators, such as income levels and educational achievement, are one component of the livability equation, the area of interest in this study is the physical environment. Links between community design and walking have been addressed and complimentary research linking health statistics to the built environment are not uncommon. The purpose of this literature review was to identify a variety of characteristics that have been correlated with higher instances of walking and also review how previous researchers have comprehensively measured walkability. Density patterns, land use mixture, street networks and retail availability have all been shown to support walking behaviors. For this reason, a GIS-based measure of walkability to be used in Cincinnati will be based upon those four concepts. The following chapter will lay the foundation for the methodology employed, beginning with an overview of the study area. 26

34 3) Methodology 3.1) Using Cincinnati Data to Create a Walkability Index Previous literature indicates that many physical constructs of the built environment are linked with walking behaviors. These behaviors can be measured using GIS technologies and categorized into walk supportive or walk non-supportive environments (Coffee et al., 2005). The walkability index to be used in this study will be comprised of physical characteristics that have previously been identified as key variables in transportation and health literature (Saelens, Sallis & Frank, 2003). This study will draw heavily from the NQLS index created by Frank et al. (2010) introduced in the previous chapter. The walkability index was created for a cluster of six traditional neighborhoods located in Cincinnati, Ohio. 3.2) Study Area The spatial extent of this study is a cluster of neighborhoods located in the central part of Cincinnati, Ohio. Many previous walkability studies have reviewed the subject from a broader scale, such as county-wide or regionally (Kuzmyak, Baber, & Savory, 2006; Frank et al., 2010). This study will approach walkability from a different scale, using the neighborhood as the spatial extent of interest. A walkability index based on previous research was of particular interest, but it was not appropriate for use on one single neighborhood. Thus, a cluster of adjacent, similar neighborhoods based in Cincinnati, Ohio were chosen (see Figure 11 below). 27

35 Figure 11: Cincinnati Walkability Study Area (Author, 2012) The neighborhoods included in this research study are Avondale, Corryville, East Walnut Hills, Evanston, North Avondale, and Walnut Hills. While demographics vary across neighborhood boundaries, the area developed in a similar pattern during the same timeframe. The study area contains an estimated population of 38,921 residents and spans approximately 7.19 square miles (U.S. Census Bureau, 2010). 28

36 3.3) Spatial Units of Analysis Due to the variation of physical environments that exist within urban areas, attempts to measure and classify urban form should utilize the most detailed spatial unit available (Coffee et al., 2005). Larger, less detailed spatial units will account for more variability among urban landscapes while smaller, more detailed spatial units allow for more specified analysis of built environment features. Block group level data was chosen as the most appropriate unit because it allows for incorporation of socioeconomic data (income, demographic, household counts, etc.). While such data was not used in this study, similar research could incorporate it for more thorough analysis. 29

37 Figure 12: Census Block-Groups Located Within Study Area (Author, 2012) Within the study area, there exist 42 contiguous census block-groups (see Figure 12 above). Block groups are the mid-level Census measurement unit, with tracts being larger and blocks being smaller. They typically contain between 600 and 3,000 people, with an optimal population being 1,500 (U.S. Census Bureau, 2000). Given this range in population, it comes as no surprise that the size of Census block groups can vary considerably depending on the density of residents. 30

38 3.4) Spatial Datasets Included All spatial data used for this walkability index were taken from the Cincinnati Area Geographic Information System consortium. Various types of error are inherent with any dataset. While this introduction of error may influence the results, this should not completely undermine the purpose of this study. Additionally, measures were taken to allow for a more accurate representation of the built environment in. The following table displays all raw data used in the creation of the walkability index. All modifications to the raw data will be considered in the next section. File Type Description Census Block Groups Parcels Feature Class, Polygon Feature Class, Polygon Study Area, defined by 42 contiguous block groups Area of Parcel, Land Use Classification Streets Feature Class, Line Intersection Buildings Feature Class, Polygon Area of Building, Number of Dwelling Units Zoning Feature Class, Polygon Zoning Classification Table 1: Description of Data Inputs (Author, 2012) 3.5) Data Modifications Not all of the associated geographic and attribute data assigned to the GIS datasets were needed for this analysis. First, all data save for the parcel and land use layers were clipped to the study area boundary. Large polygon feature classes take considerably more time to redraw after each modification and can often times cause the 31

39 ArcGIS program to crash. This step was necessary to clean up the raw data and only extract what is necessary for this study. After all of the needed input data was extracted from the original data, a secondary clean up of data attributes was conducted. Knowing that the walkability model would be using a spatial join tool (which will be explained at greater length in the following section), all unnecessary fields in each data layer were deleted in a batch process. This step is not needed to ensure the functionality of the model. However, it allowed for quicker review of the newly created attributes due to less field clutter. The final modifications to the input datasets were the result of reviewing what existed in the attribute fields of each layer and recognizing that steps could be taken to account for any missing information. Because the residential density calculation depended on land use categorizations included in the parcel polygon layer, it became evident that the significant number of vacant parcels needed to be addressed. Previous research has accounted for this void by reclassifying vacant parcels with their respective zoning classifications. This step was completed using a spatial join; approximately 50 of the 729 vacant parcels within the parcel polygon layer were not reclassified due to topology errors and a quick spot check was used to update the new land use category. Following this step, a reclassification of all land uses was needed to group all of the sub-categories into four general categories. 3.6) Walkability Index Inputs The Census block group is used as the building block for this walkability index and the remainder of this chapter reviews the necessary built environment features that will be measured. Each measure is identified and defined, with a few examples included 32

40 to clarify key concepts. This chapter concludes with the GIS models and workflow that were used to process the spatial data inputs, as well as discussing any discrepancies emerging due to the proposed methodology ) Street Connectivity The first environmental attribute included in the walkability index based in Cincinnati, Ohio is street connectivity. It will be determined using the street centerline data, with only unique street connections included in the total calculation. Unique street connections are defined as those intersections with three or more different intersecting streets and an example can be seen in Figure 13 below (Leslie et al., 2007). The density of these intersections will be measured based on the number per square mile located within each census block group. Figure 13: Example of Unique Intersections (Author, 2012) 33

41 3.6.2) Retail Floor Area Ratio (FAR) The second component to be included in the walkability index is a measure of net retail area. The validation for this measure is that it calculates the amount of retail floor area in relation to the total amount of land that exists to serve that retail use (Leslie et al., 2007). While this simple ratio inevitably has potential flaws due the integrity of data available, it allows for a general understanding of the commercial characteristics of an area. A ratio of or near one indicates that less parcel space is dedicated to parking and a distance to other destinations is shortened. To calculate this, equation in Figure 14 will be used. NRA = GRA P Figure 14: Net Retail FAR Ration (Leslie et al., 2007) Net retail area (NRA) is equal to the gross retail area (GRA) divided by the total retail parcel area (Leslie et al., 2007). To complete this calculation, information from the Cincinnati parcels and buildings data layers will be used. A visual representation of gross retail floor area ratio can be seen in Figure 15 below. 34

42 Figure 15: Retail Building to Commercial Parcel Ratio (Author, 2012) 3.6.3) Residential Density Another objective measure to be included in the composite walkability index is net residential density. According to Leslie et al. (2007), the number of dwelling units relative to the amount of land designated for residential use is an appropriate indicator of walkability as it indicates general density patterns. This means that a neighborhood with high residential density could also have increased instances of other uses, like commercial, because there are more people to support those services. Additionally, 35

43 denser development patterns in general make parking less available, thus creating environments that are more conducive to active transportation. Residential density will be calculated as the ratio of dwelling units to the area of land designated for that use within the census block group ) Land Use Mixture Based upon previous literature, it can be strongly argued that land use mixture is an important component in determining the walkability of an area. However, the measure of this variable is still in contention. To calculate land use mixture within each spatial unit, the equation in Figure 16 will be used. Figure 16: Land Use Entropy Score (Frank et al., 2010) In this equation, k represents the type of land use, p represents the proportion of total land within the census block devoted to that land use, and N represents the number of land use categories considered (Leslie et al., 2007). 36

44 Figure 17: Example of Heterogeneous Land Use Mixture (Author, 2012) 3.7) Calculating Walkability Scores Using the above built environment features, each Census block group was scored and subsequently ranked according to their respective capacity for walkability. Scores were assigned based on the aforementioned measurements; however normalization of each input was needed because not all returned values were calculated in the same unit of measurement. To normalize these varying units of measurement, the z-score for each built environment feature was calculated. This new number shows the variability with each of the four walkability inputs and indicates how far each raw data value deviates from the mean. The z-scores of each component were 37

45 then added together to provide a total walkability score for that block group. In upcoming sections, the process by which these scores were ascertained will be reviewed. Many modifications to this process occurred throughout the research timeframe due to unexpected results and technical difficulties. The two components of the GIS model created for this index will be reviewed in the following subsections. Both models and their respective workflows evolved throughout the research process, and this section will conclude with a proposed land use mixture modification that was not included due to said technical difficulties ) Intersection Density, Retail FAR, and Residential Density Workflow All of the spatial data inputs for the walkability index, save for the land use mixture component, were processed using the same model workflow. Due to inconsistencies in ArcMap10 s functionality, the model was unable to properly run without returning unneeded values. However, the workflow remains the same (see Figure 18 below). Residential parcels, retail parcels, intersections and retail building layers and associated attribute information were attached to the respective Census block group using the spatial join tool. 38

46 Figure 18: Portion of Walkability Index Model Workflow (Author, 2012) The spatial join tool attaches attribute information from a selected layer onto a target layer depending on the spatial relationship of the two layers. One new output feature class is created based off of the target layer and the joined attributes(esri, 2012). When a spatial join is executed manually, i.e. selecting the layer and navigating to the spatial join option, the user is given the option of summarizing attributes within the 39

47 target layer. This is not the case when utilizing the spatial join tool within ModelBuilder; no summing of attribute option is provided. In order to obtain the necessary results, meaning a compilation of built environment measurements contained within unique Census block groups, a manual spatial join was utilized. Additionally, all returned values contained within the new output feature class s attribute table were exported into Microsoft Excel format and calculations were executed in this file format ) Land Use Mixture Workflow The initial land use mixture measurement was going to be a variation of the land use input used in the previously mentioned NQLS walkability index. Instead of using a standalone entropy score that calculates land use within the individual Census blockgroup boundaries, residential units were to be included in order to provide a more realistic representation of walkability. This calculation would have been determined by creating a 0.25 mile buffer around each residential unit within the study area, applying the entropy score within the buffer, then averaging all buffered land use mixture values within the Census block groups. A model was created to determine these values, however successfully running the model proved to be a challenge (the workflow and model inputs for this land use mixture calculation can be found in the Appendix). Due to these technical difficulties and a short timeframe, the original NQLS land use calculation was used to complete the walkability index. 40

48 4) Results Using GIS technology to process intersection, residential, land use and retail datasets allows for a comprehensive walkability scoring system which can be applied to each of the forty two Census block groups. The results of this system are presented in this chapter as one approach of objectively measuring the built environment in Cincinnati, Ohio. The four individual components are addressed independent of the composite scoring system first, followed by a presentation of the final results that indicate general walkability based on the physical constructs included in the index. 4.1) Intersection Density Intersection density results yielded from the walkability index identified the frequency of unique intersections within each block group, as well as the number of intersections per square mile. The smallest number of intersections that existed in a Census block-group within the study area was 6, whereas the largest frequency was 107. While this contrast seems stark, the geographic size of the respective Census block groups indicates that one block group is over twice the size of the other. Given this, the chosen intersection density measure appears to give a better indication of connectivity within the study area. The number of intersections per square mile varies between a miniscule 0.07 up to 0.56 and the spatial distribution of this walkability component can be seen in Figure 19 below.census block-groups with higher instances of intersection density are, perhaps not surprisingly, located closest to the denser, more urban areas of the city. The northern half of the study area contains significantly lower intersection density measures compared to the southern half. 41

49 Figure 19: Intersection Density by Census Block-Group (Author, 2012) 4.2) Retail Floor Area Ratio Unlike intersection density, which measures the availability and intensity of travel routes, retail FAR identifies the accessibility of local retail centers. Higher values exist in Census block-groups that contain retail centers, however walking trips to local independent shops are more common (Handy et al., 2002). The gross retail FAR values are depicted in Figure 20 below. It should be noted that 4 of the 42 Census block groups contained no retail properties due to the predominance of residential development. 42

50 Figure 20: Retail Floor Area Ratio by Census Block-Group (Author, 2012) 4.3) Residential Density Residential density was presented in this study as the number of total dwelling units relative to the amount of residentially designated land area within the Census blockgroup. This component measures the intensity of residential units within an area, assuming that higher instances of residential density mean that there exists a population that can support other uses (like retail or recreational). Residential density measures within the study area can be found in Figure 21 below. 43

51 Figure 21: Residential Density by Census Block-Group (Author, 2012) 4.4) Land Use Mixture Variation among land uses within an area is thought to increase opportunities for walking behaviors among residents; the more land uses available, the more walking destinations that exist. To capture the homogeneity or heterogeneity of land use within the study area, an entropy score was applied to each Census block-group. Scores ranged from 0 to 1, with 0 indicating a lack of diversity among uses and 1 indicating equal representation of different uses. Figure 15 displays the land use mixture scores across the study area. 44

52 Figure 22: Land Use Mixture by Census Block-Group (Author, 2012) 4.5) Composite Walkability Index Using the output values from the aforementioned four walkability components, a composite walkability index was created. Normalized values that were calculated in Microsoft Excel were joined to the attribute table of the Census block group feature class in ArcMap and symbolized according to their respective composite scores. Composite scores ranged from to This index was determined by adding the normalized values for each walkability component and no weighting scheme was 45

53 applied. While previous literature attempted to apply a greater weight on certain built environment factors (Frank et al., 2010), this study weighted all factors equally. Figure 23: Composite Walkability Index (Author, 2012) This walkability index displayed in Figure 23 above reveals that the older, more central areas are more supportive of walking behaviors while newer developments in the northern and eastern portion of the study area are less supportive of walking. The version presented in this thesis is very basic and can be expanded upon using different measures or weighting schemes. There are countless ways to measure the built environment and different combinations will yield different results. 46

54 4.6) Comparing High and Low Walkability Scores Based on the walkability scores derived from the walkability index, two Census block groups were categorized as the most walkable and the least walkable. This section reviews these geographic areas and the existing built environment features that contribute to the capacity for walking behaviors. While the validity of these scores cannot be determined without survey data that documents travel activity or personal health characteristics, it is still useful for drawing general conclusions about the environmental factors that are connected to walkability. Figure 24: Least Walkable Census Block Group in Study Area (Author, 2012) 47

55 The above image displays the Census block group, symbolized with the red polygon boundary, which scored the lowest based on the walkability index inputs. It is evident based on the aerial image that the area contains predominately low density, residential development patterns. Land use categories are almost completely homogenous within the block group, however different land uses exist in close proximity to the boundary. Had the proposed land use mixture calculation actually worked, a different score might have been assigned to this block group. Figure 25: Most Walkable Census Block Group in Study Area (Author, 2012) 48

56 Of the 42 Census block groups located within the study area, the above image represents the most walkable unit based on the four built environment factors included in the walkability index. The actual area of the most walkable block group is significantly smaller than the least walkable block group, however it does appear to be located in a more urban, dense part of the study area. In addition to a heterogeneous mixture of land uses and good street connectivity, the residential properties in this block group contain a larger number of units compared to the single family predominance in Figure ) Validating the Walkability Index In order to test the validity of the walkability index created for Cincinnati, Ohio, a survey of residents living in the study area would be needed. Due to the absence of this research tool, a more thorough assessment of the walking behaviors resulting from built environment features cannot be obtained. However, a field validation of the study area can provide some understanding of the existing environment and how it may enhance or hinder walkability. Neither of these approaches were incorporated into this thesis; however, the above walkability index can serve as a base for future research that accounts for residents physical activity behaviors. Additionally, a field validation check could be used to test whether the measures used in this study are an accurate representation of walkability. The walkability index presented in this chapter was developed based on previous research on health and environmental factors as they relate to physical activity, as well as how recent planning literature has approached measuring walkability. As mentioned in earlier sections, the index draws heavily from previous transportation and urban 49

57 planning research. Modifications were attempted and while ultimately they could not be included in the final index, the research process allowed for a better understanding of the relationship between physical activity and urban environments. 50

58 5) Discussion 5.1) Further Developments and Applications of Walkability Indices The framework for the model outlined in this thesis paper can serve as a base for future studies, as well as be replicated for use in similar projects. The environmental features used to create this objective classification of urban areas are conceptually supported by previous planning literature (Ewing et al., 2003; Handy & Clifton, 2001; Saelens, Sallis & Frank, 2003). Walkability indices are advantageous in their capacity to identify areas based on specified criteria as highly walkable or not walkable. The next section will present the challenges that emerged throughout the research process, followed by suggested modifications and refinements that could enhance future studies. This chapter will conclude with brief policy recommendations that may be supported by this type of walkability research. 5.2) Challenges in Measuring Walkability Understanding walkability as a form of transportation and a tool for improved health is crucial for any discourse on pedestrian planning and public health considerations. However, the concept of walkability is often times ignored when transportation planning objectives are set. Instead of identifying more pedestrian friendly routing options, decision makers can be more occupied with facilitating vehicle flow, accommodating fire trucks, regulating land uses, or making money (Lo, 2009). The technical reasoning behind this claim is interesting to review, but it is also too lengthy for review in this thesis (for reference, see: Lo, 2009; AASHTO, 2004; Engwicht, 1999). 51

59 What is important is how the definition of walkability influences the design of urban areas and public spaces. This definition will vary depending on who is asking. Many different parties, ranging from planners to engineers to pedestrians themselves, provide different interpretations of the concept. In order for any meaningful changes to occur, there must be a better understanding of walkability as it exists within a community and an expectation for how improved walkability will influence residents. This will remain a challenge in urban planning and public health research, as defining walkability remains a subjective endeavor. Errors arising from the input data used for this type of quantitative study are inevitable. Spatially inaccurate data or attribute features will inevitably influence the output data, or the results. This is especially true when models are created, where one data layer is processed using different tools and modifications. There has yet to be consensus over which methods ensure or reduce instances of error on spatial models (Bolstad, 2008). Additionally, accurate data may be expensive to collect or obtain from an outside source. Due to the expansion and popularity of GIS as a tool for measuring walkability and public health relationships, this propagation of data errors is inevitable. However, this reality should not deter further research. As previously mentioned, there exist a wide variety of built environment features that could potentially affect physical activity behaviors. This presents opportunities for a more comprehensive review of walkability. At the same time, this plethora of variables creates the challenge of validating which environmental features are actually associated with walkability. Additionally, the scale at which these variables should be measured can determine the quality of the results. For example, measuring street façade features 52

60 that create a comfortable environment might not be appropriate when looking at walkability from a regional scale. Determining what exactly to include in an analysis of an area will continue to be a challenge ) Challenges Specific to this Thesis Using a cluster of neighborhoods in Cincinnati as the study area presented a few distinct challenges, a few of which were addressed in the above section. Deciding on a geographic area and an appropriate spatial unit of analysis required much consideration. Using Census block-groups to measure walkability at the neighborhood scale did not appear to yield significant statistical results. Thus, the study area was expanded to include adjacent, similar traditional neighborhoods to allow for more accurate calculations. The input data predominately came from the Cincinnati Area Geographic Information Systems consortium, which ensured some integrity. However, error is inherent with any dataset. This fact became evident after reviewing a test run of the methodology, when it was revealed that the Census block-group boundaries did not coincide with street centerlines. From a 1:8,000 scale, this was not evident. Upon closer review, topological errors became more noticeable (see Figure 26 below). This data discrepancy was corrected manually (which does not ensure that all errors were accounted for). The study area was small enough that this correction did not require a considerable time commitment; for larger areas, this type of error may have a greater impact on final calculations. 53

61 Figure 26: Street Intersection Topology Error Example (Author, 2012) Another technical challenge that seriously hindered the research process was the functionality of certain ArcGIS tools. Modelbuilder tools did not always have the same capabilities as manually executing the exact same tool in ArcMap. Additionally, the model created to measure land use had to geoprocess approximately 10,000 residential points and calculate new land use mixture values for each point. While the process appeared to work, the model crashed after 12 hours of running on a personal desktop computer. Land use mixtures were still incorporated and the walkability scores were not compromised as a result. However, technological difficulties definitely hindered the research process. 54

62 5.3) Modifications to Existing Walkability Indices Many physical constructs have been associated with walking behavior, indicated by the literature review presented in a previous chapter. It is evident that some constructs are positively correlated with increased walking, however more research is needed to identify more relationships. Using GIS as a tool for analysis, interested parties are able to collect, manipulate, and draw conclusions from spatial datasets. Thus, the capacity for new knowledge gained from this type of research remains large. The availability of data typically plays a significant part in determining which factors are incorporated into walkability studies. For this thesis, four factors were applied to a study area based on past research. It is evident, however, that other data might be available and appropriate for similar studies. This indicates a starting point for additional research and the development of future walkability indices must reflect other factors identified in urban planning and public health literature. For example, the inclusion of recreational facilities, footpaths, street aesthetics, perceived safety, or transit access could all easily be incorporated into a different version of a walkability index. This study dealt exclusively with the existence of certain built environment features; it did not explicitly measure access to services or walking distances to certain amenities. These distance-based measures are likely to influence walking behaviors and could be incorporated into future research. Additionally, subjective influences such as perceived safety and crime instances could be used to measure the walkability of an area. A statistical measure of these factors would be difficult to determine, as the quantitative calculations may not accurately reflect perceptions. 55

63 Another advancement in walkability studies could be the utilization of walkability indices developed from public health and urban planning research at the residence level. Instead of scoring geographic areas like Census block-groups or counties, built environment features could determine walkability scores for individual households. This type of analysis would use actual residential locations, which would allow for a more robust measure of walkability. This approach for quantifying walkability has been used in many previous studies, but this thesis could have been validated by the use of survey data to account for actual walking behaviors. Due to time and resource constraints, this adaptation simply was not feasible. Surveys as a tool for data collection introduce certain discrepancies, such as over or under reporting physical activity instances. However, they would provide more insight into the relationship between the urban form of the community and how conducive it is to walking. 5.4) Users of the Walkability Index The need for more walkable communities as a public health objective has been well stated throughout this thesis paper. Certain characteristics were identified as potential indicators of walkable behaviors and thus, the importance of measuring built environment factors became evident. However, measuring the built environment is a fruitless task if no real change occurs as a result. This section will provide a few suggestions for how walkability indices and similar research studies can be used to inform policy decisions. 56

64 The purpose of this study was to categorize areas according to their level of walkability based on certain factors. Areas that score the lowest according to the assigned methodology can be given priority for walkability projects. Some municipalities have indicated walkability as a desired outcome and prioritized projects based on their ability to enhance pedestrian-friendly physical environments for residents. This tool could allow for a quick inventory of existing conditions. Walkability studies allow for a better understanding in why mixed uses, street connectivity, building access, and other factors increase levels of walking. It is important that local zoning codes and comprehensive plans reflect these development styles in areas that can be improved by doing so. By codifying and regulating how development occurs, walkability goals are more easily attainable. The final potential users of the proposed walkability index are local, state and federal health organizations. Given that these organizations often times have limited resources, this type of tool would allow for quick analysis using readily available data sources. A general overview of walkability characteristics within a designated area would be presented and can be combined with health data or travel data to further analysis. 57

65 6) Conclusion Many western countries are grappling with the effects of an unhealthy populous, with the United States experiencing alarming rates of overweight, obesity and a lack of physical activity. These preventative diseases and lifestyle habits are of serious concern to elected officials, as they result in billions of dollars of direct health costs with additional indirect expenses (Finkelstein, Fiebelkorn & Wang, 2003). It has been determined that regular moderate exercise, such as walking or bicycling, can reduce the health burden associated with the obesity epidemic (Giles-Corti & Donovan, 2002). Given this, it is important that a better understanding of what supports walking habits is pursued. Certain features of the built environment have been positively correlated with physical activity based on previous urban planning research, particularly higher density areas, mixed land uses, an integrated street network and access to retail. A walkability index based on measured characteristics is presented in this study based on Cincinnati data as means of understanding walking activities. GIS is used to collect and manage the process of creating this walkability index and is the basic tool needed for any analysis. The findings from this study cannot be compared to previous research, as there was no survey component. However, it can serve as a base for future researchers interested in walkability in Cincinnati. More work is needed to refine the input factors included in the walkability index, specifically the land use mixture component. This factor could be expanded on to better reflect good land use mixtures and bad land use mixtures, using walkability as the 58

66 framework for this measurement. The inclusion of other objective variables and spatial units of analysis are important considerations and warrants additional research. One suggestion for future research lies in the application of walkability indices at an individual level. Using objectively measured variables that have been identified in both public health and urban planning literature, this comprehensive tool of measurement could be applied to households to gain a better understanding of walkability at a much smaller scale. The works presented in this thesis paper can serve as a base for future studies, particularly in urban areas where an interest in improved public health exists and resources are available for analysis. 59

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72 Appendix Appendix Figure 1: Alternate Land Use Mixture Model(Dr. Changjoo Kim, 2012) 65

73 Appendix Table 1: Walkability Index Raw Data and Z-Scores (Author, 2012) 66