Iowa State University Capstones, Theses and Creative Components Dissertations

Fall 2020

Enhancing Walkability in a Downtown: A Case Study of Adel, Iowa

Yaw Kwarteng

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Recommended Citation Kwarteng, Yaw, "Enhancing Walkability in a Downtown: A Case Study of Adel, Iowa" (2020). Creative Components. 656. https://lib.dr.iastate.edu/creativecomponents/656

This Creative Component is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Creative Components by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Enhancing Walkability in a Downtown: A Case Study of Adel, Iowa

by

Yaw Yeboah Kwarteng

A creative component submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

MASTER OF COMMUNITY AND REGIONAL PLANNING

Major: Community and Regional Planning

Program of Study Committee: Monica Haddad, Major Professor Brian Gelder Sungduck Lee

The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this creative component. The Graduate College will ensure this creative component is globally accessible and will not permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2020

Copyright © Cy Cardinal, 2020. All rights reserved. ii

DEDICATION

This report is dedicated to my mum, Akosua Gyapomaa. Your love keeps me going. iii

TABLE OF CONTENTS

Page

LIST OF FIGURES ...... v

LIST OF TABLES ...... vii

ACKNOWLEDGMENTS ...... viii

ABSTRACT ...... ix

CHAPTER 1. INTRODUCTION ...... 1

CHAPTER 2. LITERATURE REVIEW ...... 5 Defining walkability ...... 5 Benefits of walkability ...... 7 Walk score for the city of Adel ...... 9 Empirical studies about walkability and planning ...... 10 Measuring walkability with GIS ...... 12

CHAPTER 3. METHODOLOGY ...... 19 My study area ...... 19 My methodological steps ...... 26 Calculating the Index of Walkability ...... 28 Land use mix ...... 28 Connectivity Index ...... 29 Density...... 31 Proximity ...... 31 Streetscape Evaluation using GSV ...... 33

CHAPTER 4. RESULTS AND FINDINGS ...... 36 Measuring the Connectivity Index ...... 36 Assessing Proximity of Land Uses ...... 36 Measuring closest facilities (Current Land Use) ...... 37 Measuring closest facilities (Future Land Use) ...... 39 Land Use Mix ...... 40 Measuring the Residential Density ...... 41 Reclassification of Values of the Dimensions ...... 42 Creating the Walkability Index ...... 43 Comparing Walkability Indices for Downtown Adel ...... 44 Google Street View (GSV) Measures ...... 46

CHAPTER 5. CONCLUSION...... 53 Recommendations ...... 53 Limitations of the Study ...... 55 Final Remarks ...... 55 iv

REFERENCES ...... 58

APPENDIX A. FUTURE LAND USE MAP FOR THE CITY OF ADEL ...... 62

APPENDIX B. QUESTIONNAIRE FOR ASSESSING DOWNTOWN'S WALKABILITY USING GSV ...... 63 v

LIST OF FIGURES

Page

Figure 1: Walk score for the city of Adel ...... 10

Figure 2: Map of the State of Iowa and Dallas County ...... 19

Figure 3: City of Adel ...... 20

Figure 4: Downtown Adel Boundary based on Adel Downtown Plan ...... 21

Figure 5: Current Land Use based on Adel’s Land Use Map ...... 22

Figure 6: Current Land Use Map Aggregated ...... 23

Figure 7: Future Land Use based on Adel Downtown Plan ...... 24

Figure 8: Future Land Use Aggregated ...... 25

Figure 9: Alleyway Beautification Areas ...... 26

Figure 10: Conceptual Framework ...... 28

Figure 11: Street Links and Nodes...... 30

Figure 12: Locations where Streetscapes were Examined using GSV ...... 34

Figure 13: Network Analysis (Current Land Use) ...... 38

Figure 14: Network Analyst (Future Land Use) ...... 40

Figure 15: Walkability Index for Downtown Adel ...... 43

Figure 16: Walkability Index with Alleyway Beautification Incorporated ...... 46

Figure 17: Sidewalk Classification Criteria ...... 47

Figure 18: What are the conditions of the sidewalk from the observed location? ...... 48

Figure 19: Can Two People Fit on the Sidewalk from the Observed Location? ...... 48

Figure 20: Is Pedestrian Crossing available at the Observed Location? ...... 49

Figure 21: Streetscape Features in Downtown ...... 50

Figure 22: Examples of Streetscape Features ...... 51 vi

Figure 23: Condition of Street Landscape ...... 52

Figure 24: Potential for Sidewalk Width Expansion and Crosswalk Development ...... 54 vii

LIST OF TABLES

Page

Table 1: The Four Dimensions of Walkability Mentioned by Various Authors ...... 27

Table 2: Density Measure and Category ...... 31

Table 3: Proximity Matrix ...... 32

Table 4: Dimensions of walkability and their Formula ...... 32

Table 5: Spatial Data Used in Case Study ...... 35

Table 6: Distances to Activity Centers (Current Land Use) ...... 39

Table 7: Distances to Activity Centers (Future Land Use) ...... 40

Table 8: Land Use Percentages in Downtown ...... 41

Table 9: Classification Matrix ...... 42

Table 10: Walkability Matrix ...... 45

viii

ACKNOWLEDGMENTS

I would like to express my profound gratitude to my committee chair, Monica Haddad, and my committee members, Brian Gelder, and Sungduck Lee, for their guidance and support throughout the course of this research.

As Well, I extend my heartfelt admiration to Dennis Kwadwo Okyere, you are a pearl. I would also like to acknowledge my friends, colleagues, the department faculty, and staff for making my time at Iowa State University a marvelous experience and a worthwhile dream. ix

ABSTRACT

Downtowns have the potential to be a main attention point for small communities, the easiest centers to turn into pedestrian-focused mixed-use areas. Additionally, downtowns have the strongest connection to the civic features of neighborhoods, as buildings with civic significance are located in downtown. Buildings with noteworthy meanings are in downtowns and serve as a point for public gathering and hearing. Therefore, maintaining and improving the quality of access to these buildings can spur socio-economic development.

In furtherance of these desires, the main goal of this study is to estimate the Walkability

Index, reflecting forms of walking to daily destinations in the downtown of the city of Adel.

There is a strong relationship between walkability and the set-up of the built environment where people live. It is thus imperative that the design of urban form supports physical human activities. Hence, the location of shops, health facilities, parks and open space, residential districts, and other land use, in relation to each other are crucial elements that influence the walkability of a place.

To effectively assess walkability, it is essential to identify the dimensions involved in measuring walkability. Connectivity, proximity, land use mix, and residential density are the necessary variables for estimating the index of waking in an urban setting. Using spatial analysis in Geographic Information System (GIS) is an established method to objectively automate the measurement of these dimensions. To help evaluate the city’s newly adopted future land use plan to guide the development of the downtown from 2020-2040, the index was calculated for both the current and adopted future land uses.

To calculate the overall index, the following steps were met: the network analyst extension of ArcGIS was used to measure proximity, gamma index was calculated for x connectivity, net residential density was used to measure density, and the entropy index calculation used to estimate the land use mix. Each one of these dimensions was reclassified with values ranging from 0-100. Additionally, the study uses Google Street View approach to evaluate streetscape features’ capability to provide opportunities for walking in downtown Adel.

The results of the study explicitly indicated that assessment of walkability standards can be performed on both existing built environment and proposed land use plans of a downtown neighborhood. The findings indicate that the proposed future land use will increase the index from 65.6 to 72.9, demonstrating that the recently adopted Downtown Plan will indeed make

Adel a more walkable community. Evaluation of the streetscape features revealed the need to improve infrastructure for pedestrians, such as pedestrian signages and urban street elements, to increase pedestrian walking experience. This methodological approach can be applied to other cities that want to measure walkability. This study can be used by urban planners and policymakers to assess whether future plans do create opportunities for pedestrians to increase the level of walking.

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CHAPTER 1. INTRODUCTION

Physical inactivity or sedentary lifestyles can result in preventable endemic health exposures such as obesity (Hruby & Hu, 2015; New York State Department of Health, 2020).

Obesity has become the second primary preventable cause of death in the United States (New York

State Department of Health, 2020). Society is cognizant of this needless nuisance, and therefore advocacy for healthier lifestyles, change in behavior, and opportunities for physical activities, such as walking, is on the rise (Zhang & Mu, 2019). To reinforce these facts, one of the United Nations’ goals for Sustainable Development “is to ensure a healthy life and promote well-being for all at all ages” (United Nations, 2019). Urban planners face a conundrum when finding a balance between development and community design to achieve an inclusive, safe, and sustainable urban environment. This evidence has buoyed planners and policymakers alike to recognize a walkable built environment as the surest way to improve public health (Adkins, Makarewicz, Scanze,

Ingram, & Luhr, 2017).

Access to open space goes hand-in-hand with walkability and plays an essential role in ensuring healthy lifestyles and promoting well-being. However, most cities have struggled considerably in providing convenient access to open spaces for their population. To illustrate, data from 2018 about 220 global cities depicted that only 21 percent of the population had access to open spaces, not because of their inadequate share of an urban area but their uneven spatial distribution across land uses in these cities (United Nations, 2019). Nowadays, national urban policies should target strategies that react directly to urbanization challenges, such as providing efficient walkability through deliberate and planned development.

Furthermore, a dependency on automobiles, which is a significant characteristic of lifestyle in American cities, contributes to substantial amounts of air pollution. In the U.S. in 2019, 2 automobile dependency resulted in over 55% of nitrogen oxide releases and 27% of pollution from transportation (USEPA, 2019). This happens because cities have created mono-functional car reliant regions where people have nothing to walk to (Dovey & Pafka, 2020), hence they drive to all destinations in the urban fabric.

Within this context, the objective of this study is to measure walkability in the downtown of a small town in Iowa that is developing a land use plan. The study poses the following spatial questions: How well does the current and future arrangement of land uses allow people to move around the urban fabric? How can spatial analysis be used to automate the measurement of these dimensions objectively? How does streetscape features provide opportunities for walking?

To answer these questions my main goal is to measure the level of connectivity, proximity of uses, residential density, and the different land use mix to estimate the Walkability Index, reflecting forms of walking to daily destinations in the downtown of the city of Adel. The methodological steps to be followed include: identify the dimensions of measuring walkability, identify variables of measuring the dimensions, use network analyst, to measure the index and use

Google Street View approach to assess streetscape features’ capacity to support walking in downtown.

Downtowns certainly can provide an alternative option for the suburban environment and its associated social and economic costs. Downtowns can be the most accessible centers to turn into pedestrian-focused centers, due to their mixed-use, and their position as the main attention point for small communities (Horan, Yang, & Eidt, 2015). Moreover, it has the strongest association with the civic features of a neighborhood, as buildings with civic significance are in downtowns. As there is the tendency to obtain and maintain a continued public-private sector partnership in these centers, they can ensure a robust future investment (Horan, Yang, & Eidt, 3

2015) and ensure a continual assembly of people in these centers in the community. Moreover, buildings with significant meanings in America are concentrated in downtowns and serve as a point for public gathering and hearing (Rypkema, 2003). Downtowns are of prime importance to every community in America. Therefore, cities must endeavor to improve and maintain the quality of access to these buildings in this central point in the city, to serve as an impetus for social and economic development. Thus, making downtowns more walkable offers vitality and a distinct sense of place for people.

According to Ackerman (2005), there is a strong relationship between walkability and the set-up of the natural environment. Therefore, the design of urban form must support human physical activities. The level of walkability of a place assesses its physical arrangement and has the potential to minimize or maximize environmental impacts from automobile use. Indeed, the location of shops, health facilities, parks, and open space, residential districts, and other land uses are crucial elements that influence the walkability of a place (Ackerson, 2005). For example, a place characterized by low walkability enforces over-dependence on automobiles for the day-to- day activities, which affects the sustainability of the area over time.

Knowing the level of walkability of a specific place can assist urban planners to either re- design, rectify, or sustain the urban form in a location. It is critical that urban planners assess the existing structure to determine the future urban form to influence physical activity. Consequently, providing urban pedestrian facilities that improve quality health lifestyles, between trip origination and destination points, is highly recommendable. Pedestrian infrastructure for people who might not be able to afford automobiles (Dobesova & Krivka, 2012) and its associated costs is an inclusive way of urban development. These changes could help people to minimize their 4 automobile dependency and indulge in physical activities that promote clean sustainable environments and personal health.

This study is organized as follows. The next section presents the literature review, focusing on previous research that evaluates and presents methods to assess walkability. The third section has information about the methodology, study area and data description. The fourth section discusses the dimensions of walkability, Google Street View (GSV) and the results. The final section describes the recommendations, limitations and the conclusion of the research.

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CHAPTER 2. LITERATURE REVIEW

In this section, I define walkability, its benefits and describe empirical studies about walkability in tandem with planning. This part also explains the use of GIS to measure walkability for effective urban planning, design, and development, and lastly, how Google Street View (GSV) can be used to assess walkability.

Defining walkability

In many instances, planning for walkability usually has faced the problem where the actual definition of walkability is not clearly outlined. This has allowed the pedestrians space to be minimized while attaining targets of easing vehicle flows, accommodating heavy automobiles, controlling only land uses and making more money (Lo, 2009). In other words, automobile had priority over pedestrians. While this kind of planning does not address the meaning of walkability, it influences the planning for pedestrians in any urban environment.

How walkability is portrayed has huge implications on general understanding and the design of urban spaces and connections in community settings (Lo, 2009).

The ability of a community to provide prospect for walking is often referred as walkability (Weinberger & Sweet, 2012). Walkability can be defined as a potential mode of choice for people to move between points (Dörrzapf, Kovács-Győri, Resch, & Zeile, 2019), usually from their default origins to attraction centers (home to schools, work, grocery store, etc.) to accomplish a certain purpose, mostly by walking. Walkability in a simpler term is the channels of opportunities to walk in an urban setting, and not actual walking behavior

(Weinberger & Sweet, 2012). Thus, walkability can be referred to as the physical environment, with its basic understanding extended to add pedestrians’ sentiment and sensitivity. 6

Although walkability has come to occupy a vital role in urban design and planning to address issues of public health and social equity, a definitive concept for the term has recently become elusive (Dovey & Pafka, 2020). According to Dovey & Pafka (2020), density, land use mix and network connectivity can be said to be the primarily recognized terms to define walkability, and any other single set of capacities or measures can result in a misconception, nor can the theory be reduced to just actual levels of walking. Thus, the concept now captures inter- relativity between abstract set of factors, namely, connectivity, land-use mix and density of buildings and people.

Practically, travel behavioral research has used density as a substitute for many features that affected walking (Weinberger & Sweet, 2012). Meanwhile, there are other equally important measures that affect walking in the built environment: Distance to possible transit, Street connectivity and the built environment design, Building and Land use mix, Accessibility of desired destinations, and Land use density in the built environment (Weinberger & Sweet, 2012).

Density alone cannot be used to substitute for any of these measures in a proper definition of walkability. Understanding the concept of walkability can aid approximating the amount of space to efficiently optimize for pedestrians in the urban settings (Lo, 2009).

Furthermore, neighborhood measures that through other literatures have been established to help augment the definition of walkability incorporate the following: pedestrian buffering from vehicular traffic; the absence of high-speed and heavy vehicular traffic, Landscaping and street trees; actual and Perceived safety, Sense of place, Continuous and properly maintained sidewalks; and path directness (Lo, 2009).

As much as density is an essential part of expanding opportunities of walking, it cannot stand alone as the only measure of assessing walkability of a neighborhood. Ineffectively 7 outlining walkability can result in induced demand where neighborhoods and downtowns suffer the most. The unanticipated consequences stemming from a diminished holistic approach reduces the chance of properly enlivening city life in downtowns and neighborhoods (Benfield,

2012). Thus, all efforts will result in more conveyance of more vehicular mobiles in sensitive places in the neighborhood environment, instead of critically treating these public spaces for the people.

Therefore, characteristics of the built environment that can increase walkability, throughout literature, should always include; Land use mix, the level of street connection to desired locations and the directness to required paths, well-maintained streetscapes, continuous sidewalks, land use diversity, convenience etc. These features of the urban environment are encompassing in evaluating conditions for walking, and as such, defining possible ways of improving these characteristics when lacking neighborhood’s setting.

Benefits of walkability

Walking is the most ordinary mode of moving to any destination (Kelly, Murphy, &

Mutrie, 2017). Traversing the urban environment includes some form of walking, whether going to work, home, or other places for social functions. Walking is convenient, low cost, low risk and accessible for most people (pedbikeinfo, 2010; Kelly, Murphy, & Mutrie, 2017).

Environmental benefits Adequately replacing shorter trips with walking can reduce the amount of energy consumed otherwise. Transportation is accountable for 80% emissions from carbon monoxide and a third of emissions from carbon dioxide in 2007 in the

United States (pedbikeinfo, 2010). Over-reliance on usage of automobile and provisions for its use do not affect pedestrian spaces, but also the environment that sustain the life of humans, trees, and water bodies. Improved walkability results in reduced land required to construct roads and for parking infrastructure (Litman, 2018). 8

Health benefits Investing in infrastructure that facilitates invigorating physical activity can tackle obesity and other ailments like diabetes, chronic illnesses, stroke, heart diseases etc.

(pedbikeinfo, 2010). Since walking is weight bearing activity, the body mass of heavier individuals makes them use more energy to walk a given distance, in comparison to lighter people (Kelly, Murphy, & Mutrie, 2017). A variety of health outcomes is also related with refining avenues for physical activity, like increase in metabolism, musculoskeletal function, mental well-being and immune abilities (Kelly, Murphy, & Mutrie, 2017).

Transportation benefits Pedestrians can sometimes circumvent stalemate traffic and arrive at their location faster by using walking or bicycles than they would have if they used automobiles. Integrating spaces for pedestrians and even bicycle riders can ensure people have access to different modal options to choose (pedbikeinfo, 2010). About 72% of trips less than three miles are made in cars, a characteristic of many trips in America (pedbikeinfo, 2010).

Economic benefits Car ownership and operation in America is expensive, with the typical ownership and operation costs pegged at 18% of typical household income (pedbikeinfo, 2010).

Improving avenues for walking can reduce the transportation costs incurred by individuals, end in health cost savings from improved physical activity and increase local business activities

(Litman, 2018).

Social benefits Walking creates an intrinsic benefit of producing neighborhood cohesion, community interaction and the chance for conserving historic resources (Litman, 2018).

Individuals living in high volumes of traffic neighborhoods have less chance of seeing their neighbors as compared to places with possibilities for walking. Improving walkability thus affects the livability of a community. Furthermore, seniors and young people can have a sense of 9 independence when there is a provision for more options to travel, especially when they cannot drive or choose not to (pedbikeinfo, 2010).

Walk score for the city of Adel

Walkscore.com is a very popular webpage created by a private company, which uses distances from one point of a locality to varying locations. The score is usually between 0 – 100 based on walking to destinations such as grocery stores, schools, parks, restaurants, and retail.

The aim of the score is to promote walkable neighborhoods, as it is the firmest avenue for the environment, health, and the economy.

Walk score uses a patented system to determine the walkability of a spatial location. The system examines hundreds of walking routes to closest amenities. Services within 5 minutes of walking (under 0.25miles) are awarded full points of 100. The system uses the distance decay method to measure services that are distant. The system also considers population density and road metrics like intersection density and block length to ascertain the friendliness of the neighborhood to walking. Services that are more than 30 minutes of walk time are deemed not walkable and given a point of 0. Walk score uses walking to the following categories: Dining &

Drinking, Groceries, Shopping, Errands, Parks, Schools and Culture and Entertainment, to determine the total walk points for the whole city of Adel (walkscore.com, 2020). Adel has a walk score of 61 out of 100 (see Figure 1), meaning the place is somewhat walkable and errands can be fairly accomplished on foot.

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Figure 1: Walk score for the city of Adel

(Source: walkscore.com)

Empirical studies about walkability and planning

State and local authorities are leaning towards land development and urban design as a measure to decrease automobile use and reduce its associated environmental and social costs

(Ewing & Cervero, Travel and the Built Environment, 2010). Ewing and Cervero (2010) conducted meta-analysis of around 200 studies that relate the measures of the built environment to travel and conclusively found that walking can be related to the land use diversity, density, and destinations with the walking distance. A well-connected network of streets, with sidewalks and destinations in proximity can invariably contribute to increased levels of walking and bicycling (Dill, Mohr, & Ma, 2014). 11

Additionally, if local authorities design land use policies that offer alternatives to drive less and tend to non-motorized modes more, residents will follow (Cao, Mokhtarian, & Handy,

2009). Though studies have found out that suburbanization supports the attitude of driving more, smart growth strategies can modify travel behavior for individuals. Thus, according to Cao,

Mokhtarian, & Handy (2009) observing travel behavior was a direct correlation between locality qualities and individuals’ travel decisions, for non-motorized travel and its frequency.

Badland, et al., (2017), identified 14 state level urban planning policies devised to promote walkability and developed spatial measures for further testing. Afterwards, geocoded population data were linked with these spatial measures and tested with walking behavior in adults. All the 14 state level policies spatially implemented were distinctively associated with neighborhood walking for transport. Remarkably, residents living in well-connected streets, shorter distances to activity centers, higher densities and availability of mixed uses had higher preference for walking.

Intriguingly, previous research on travel behavior noted an undisputable link between walkability and the built environment. Researchers that only used density as a proxy, not including all the other characteristics that affected walking, was an incomplete method

(Weinberger & Sweet, 2012). Considering the difficulty in using density as the only metric for all the recognizable predictor for walking in a neighborhood, Weinberger & Sweet (2012) used data from walkscore.com to develop models for measuring walking. The authors concluded that walk score can be a reasonable experimental method in evaluating trip impacts. The study also acknowledged other measures (land use accessibility, network connectivity etc.) that influence travel behaviors in the urban settings. 12

In summary, to efficiently plan for walkable neighborhoods, factors such as connectivity, land-use mix, and density of buildings and people considerably factored has the prospective of building unanimity for planning policies for walking. Factoring these respective variations in the built environment are in no doubt paramount to the achievement of the feat of walkable neighborhoods. Which in turn can help achieve healthy, low-carbon, productive, and creative cities.

Measuring walkability with GIS

GIS is a technology that can be used to objectivity perform complex urban functions, and moreover, measure elements that may affect walking in the physical environment. Several empirical studies have thrown luminance on similar but different approaches to measuring the neighborhoods’ walkability index, with strong review on the built environment and travel behavior.

To ascertain the degree at which the built environment component affects physical activities and transport, Leão, Abonizio, Reis, & Kanashiro (2020) proposed a combination of built environment components for evaluating walkability in the built environment. The components of walkability studied were the residential density, retail floor-area ratio, intersection density, Land-use mix (entropy), space syntax and integration, land parcels values, and real estate values. These variables were employed to measure walkability for mid-size cities in Brazil. The study highlighted how the variables selected for assessing the built environment have a level of positive effect on walking behavior. However, there was a remarkable weight of land use mix over walking behavior.

Connections between walkability and active transportation in children aged between 10-

13 were analyzed by Williams, Borghese, & Janssen (2018). Connectivity, proximity, infrastructure for pedestrians, and safety were measured, using GIS to establish an index for the 13 relationship between the built environment’s construct for walkability and active transport for children to schools, as well as other destinations. Children, living in most walkable zones, engaged in active transportation twice as much as those in the least walkable zones.

The walkability index has been further been assessed by studying the organization of the components of neighborhood walkability and walking levels (Stockton, et al., 2016). The study by Stockton et al. (2016) measured the neighborhood’s makeup for walkability and walking levels in adults from data from a spatially contiguous census area. Though there has been recognized positive correlations between various components of the built environment and walking, the study was designed to use three core components, that is street connectivity, residential dwelling density and land use mix, to measure levels of walking behaviors. Adults living in more walkable neighborhoods were noted with longer weekly walking time, thus the walkability construct can predict walking behavior in adults.

Walkable access is one of the very important elements in deciding either to walk or otherwise. Tiran, Lakner & Drobne (2019) used web survey to ascertain information about proclivity to walk to different services in Slovenia. The study modeled walking accessibility to different amenities in the study area by a network approach, combined these distances to obtain the overall accessibility and analyzed the overall distances in GIS. Distance decay functions

(proximity) and accessibility indices was the two components used to measure walkability to closest facilities. The study impartially considered the propensity to walk to different locations or services in an urban environment. The study established the propensity to walk to certain amenities is determined by their respective functions. Though walking is decided by the types of amenities, it can be further used to assess the residential settings’ condition and site development procedure for location of amenities. 14

Ackerson (2005) assessed a neighborhood’s streetscape and its walkability in Springfield and Bend, Oregon. The focus of the study was to evaluate how walkable a neighborhood is near middle schools where structures that boost pedestrian protection were provided. He used streetscape features to supplement neighborhood scale variables and to compare students’ trip behavior amid safety attributes identified in the respective neighborhoods. The emphasis of this research was comparing how walkability of suburban schools diverge from schools located in the urban core. The study also assessed how students with equally long routes will choose walkable and safer routes to school and the physical distribution of safety facilities between specific school neighborhoods and school districts affect such a decision. The study concluded that street segments with fewer dead ends, developed in terms of pedestrians’ amenities and sidewalk connections have higher walkability ratings. As well, students take shortest routes to schools, as they tend to be more walkable than longer routes.

Similarly, Mantri (2008) analyzed different models of walkability to recommend a standard GIS-based approach to assess walkability for dissimilar neighborhoods. The objective of the study was to identify measures of walkability and devise a GIS model for measuring the walkability index of a neighborhood. The main measures for walkability index identified from literature in this research were a neighborhood’s land use mix, street connectivity, proximity, density, and the safety of the neighborhood/place. Mantri (2008) after scrutiny of various research into walkability and identification of variety of features of walkability from literature, identified variables that define walkability and incorporated these variables into a model (GIS-

Based) to derive the walkability index for the neighborhood of interest. GIS based approach to measure the index can be an ideal method as it is a software that has the capability to analyze 15 distinct datasets, which may be spatial or otherwise (Mantri, 2008), especially ideal for a concept as walkability.

On the other hand, Dobesova and Krivka (2012) used a methodology developed by IPEN

(International Physical Activity and Environment Network) to measure the walkability index of

Olomouc City, Czech Republic. The methodology developed by IPEN comprises of putting together four partial indexes to obtain the walkability index. The partial parts are indexes of the

FAR (floor area ratio), connectivity, Entropy and Household density. As GIS is a practical technology for processing available urban spatial data and census data, a programmed system for collective processing was used. The input data utilized was in the form of a shapefile: land use, lines of roads, points of stores/commercial centers and their area, the urban unit with details about households, to facilitate convenient calculation of the index.

Attributes like dwelling density, connectivity, land-use mix, and net retail area can also be used to audit the walkability of a place (Leslie, Butterworth, & Edwards, 2006). The study by

Leslie, Butterworth, & Edwards (2006) used these four attributes and readily available GIS data to decipher the walkability of a geographic jurisdiction to assist in influencing urban planning decisions in future transportation and urban design planning to influence walking. The study established the need for transportation investment and connecting cul-de-sacs to improve street connectivity, especially in mixed use and compact areas that offer little opportunity for navigation.

In summary, the path to achieving a walkable neighborhood requires critical attention to respective attributes to the achievement of the concept as connectivity, density, proximity, and land use mix in the urban set-up. Focusing only on an aspect of the built environment can affect 16 its resilience and sustainability. It is therefore imperative to have a critical look at the development and maintenance of these facets of the urban design.

Assessing walkability with GSV

Several in-person methods have been employed to evaluate walkability over the years.

However, using internet-based approach (such as GSV) can modestly reduce the costs of accurately collecting data for neighborhood audits (Clarke, et al., 2010). Clarke, et al., (2010) compared using GSV to capture neighborhood characteristics as an alternative to in-person method. They used data obtained in city of Chicago to assess the reliability of GSV by contrasting data obtained through virtual means to data acquired through in-person audits. They found that data obtained through virtual audit procedures can be reliable in assessing neighborhood characteristics including recreational facilities, environment for food and myriad uses of land.

Rundle, Bader, Richards, Neckerman, & Teitler (2011) evaluated the practicability of using Google Street View to examine neighborhood features. They compared neighborhood measurements data collected by GSV and coded in 2008 with prior audit data collected in 2007.

They collected around 140 items including aesthetics, physical disorder, pedestrian safety, motorized parking and traffic, sidewalk amenities etc. by in-person audit process. They found a reasonably high tally between these two audit systems (in-person and GSV).

Additionally, Lee & Talen (2014) did an extensive comparative study that reviewed some of these different methods used in calculation of the index. The study examined diverse studies done through different in-person and secondary sources for walkability evaluation by researchers and compared it with the possibility of a combination of a GIS and Google Street View audit methods. Though there are several audit methods for measuring the subject matter outlined, the main emphasis of the research focus on the physical elements used in only two methods, in- 17 person surveillance and GSV methods. The study conclusively reiterated the effectiveness of both up-to-date GIS layers and Google Street View in getting data relevant to walkability, and thus GSV measurement can replace in-person data acquisition techniques.

As there is increasing advancement in the application of technology in examining neighborhood streetscape, Badland, Opit, Witten, Kearns, & Suzanne (2010) conducted a survey to examine the efficacies of both physical and virtual streetscape audits. They examined built environment attributes closely related to walking in 48 neighborhood segments in New Zealand.

The neighborhood streetscape audits were conducted both on-site and remotely to compare and assess the level of agreement between the physical and virtual audits. Both physical and remote audits were within acceptable level of agreement thus GSV was identified as a resource efficient and acceptable alternative to in-person audits.

The advantage of virtual streetscape audits includes the ability of the researcher to remotely access locations, and reduction in research costs, like transportation costs and time to get to the location is considerably saved. These studies therefore depict a strong possibility of using GSV as a secondary source for collecting data for neighborhood characteristics to be used in examining walkability. Thus, GSV can replace in-person audit methods used to collect data for measuring walkability in assessing neighborhood characteristics.

The built environment features and walking

The concept of understanding walkability of an area should include ways the built environment influences walking as the manner in which buildings are arranged and placed in a setting affects the manner of walking in said environment. Conversely, the level of walking in an area increases exponentially when people feel a sense of ownership over the streets they walk

(Singh, 2016). On the other hand, if streets are not controlled in any manner, the streets fail to 18 promote walking for the residents in the area. Thereby, the built environment’s physical features affect the overall walkability and walking behavior that will be exhibited in a setting.

Physical streetscape features that affect walking include sidewalk width, street width, tree canopies and the arrangement of buildings in the built environment. Physical features that translate into urban design qualities like linkage, imageability, coherence, human scale, complexity, legibility, and enclosure (Ewing & Handy, 2009; Singh, 2016). These urban design qualities compositely promote a sense of safety, comfort, and interest, which translate into walking; Street landscape (imageability), Building appearance (complexity), Street furniture and other street items (Human scale), Sidewalk continuity (Linkage), Tree canopies (comfort), and

Pedestrian signage and elements (Coherence).

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CHAPTER 3. METHODOLOGY

This section includes a description of the study area, my conceptual framework, the methodological steps to be followed to answer the research questions, and the data to be collected.

My study area

The City of Adel is in Dallas County, centrally situated (see figure 2) in the State of

Iowa, between 41 37’ north latitude and 1.18’ west longitude. The county administration is located in Adel, which is the oldest town in Dallas County (Wikipedia, 2020). Adel covers a total land area of 3.58 sq. mi, has an estimated 2019 population of 4,030 and thus a population density of 861 per sq. mi, which is greater than the State’s average population density (US Census

Bureau, 2019). It lies west of Des Moines, the State’s capital, and west of West Des Moines and

Waukee.

Figure 2: Map of the State of Iowa and Dallas County (Source: Wikipedia)

Adel is part of the Racoon River Trail cities in the Des Moines Metropolitan Area, which attracts people to bike, walk and enjoy other recreational activities, as well as the small town feel of the built environment. In 2018, Confluence, a landscape architecture, planning and urban 20 design firm, was contracted to develop and update the comprehensive plan, helping the city in spatial planning and design of city, and addressing various land uses issues and community facilities and services projects (City of Adel, 2019). Below is the map of city of Adel (see Figure

3) with a highlight on the where downtown is located.

Figure 3: City of Adel

Specifically, the scope of the study will be narrowed to the downtown of the city. In

August 2019, the Iowa State graduate students in Community and Regional Planning, led by

Professor Monica Haddad, started working with city for ideas on how to specifically develop their downtown for the year 2040. These development insights show off signs of investment the city is laying into the future development of the place. One of the aims of the study was to 21 increase walking opportunities and develop downtown to be become pedestrian friendly. The downtown, which is located almost at the north-eastern part of the city, made up of around 160 parcels of various land uses (Figure 4).

Figure 4: Downtown Adel Boundary based on Adel Downtown Plan

The final downtown plan developed by the graduate students at Iowa State University, presented the current land use as depicted on Figure 5. The land uses divided into public, single, and multi-family residential, commercial, public, industrial and Office uses. It is important to highlight that there were 54 residential parcels and 108 parcels for other types of land use in the downtown. However, for future comparison in the spatial analysis, it was necessary to re-classify land use into residential, commercial, office, public and industrial land use spaces, as shown in

Figure 6. 22

Figure 5: Current Land Use based on Adel’s Land Use Map (Source: Adel Downtown Plan 2020-2040)

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Figure 6: Current Land Use Map Aggregated

After revision of the current land use, and the expressed desires of downtown residents through various community participation programs, a future land use plan for the downtown was developed, as depicted in Figure 7. Land Use categories were subdivided to separate commercial into those that serve the downtown neighborhood and the whole community, and public land use into community, city, and county properties. Residential land use was segmented into residential-single family, residential - medium density and residential high density. Additional categories that were included were open space and mixed uses.

The Mixed land use was included in the plan to ensure co-existence of more than one land use function, including commercial and residential, to increase dwelling opportunities in the downtown. Mixed uses were to give emphasis to diversity of uses and higher concentration of 24 people to provide the needed market for businesses, and even attract more businesses. The Dallas

County Court and other county offices in the downtown were described as Public spaces because they serve people in the Dallas County, as well as the local community. Churches and publicly owned parking spaces were categorized under Public spaces. Commercial land uses were located for the present use of the land and strategic location in downtown.

Figure 7: Future Land Use based on Adel Downtown Plan (Source: Adel Downtown Plan 2020-2040)

For spatial analysis of walkability of the future land use, it was necessary to reclassify the land uses into residential-single family, residential - medium density and residential high density, open space, mixed uses, commercial and public spaces, as shown in figure 8. 25

Figure 8: Future Land Use Aggregated

The study area was selected due to the expressed aspirations of the decision-makers and residents for a pedestrian-friendly and economic-viable environment. Walkable neighborhoods have been identified to present opportunities for physical activities and boost local businesses and economic activities. The study aimed to evaluate the current land use, in comparison with the proposed future land use to facilitate assisting decision and policy makers for the area in pursing the right principles in the development of the downtown. The study thus evaluates both present and future Land-uses for their walkability indices to provide feedback to policy makers as they plan the adoption of future land use, and possibly recommend steps to maintain and improve opportunities for pedestrians in the neighborhoods. 26

The study also assessed the impart the potential alleyway beautification areas will have on the walkability of Adel, which was included in the Downtown Master Plan. Figure 9 shows the alleyways beautification areas proposed in the Downtown Master Plan for Adel 2020-2040.

Figure 9: Alleyway Beautification Areas (Source: City of Adel Downtown Plan 2020-2040)

My methodological steps

Though the concept of walkability has been explored, measuring it is still complex and intricate. Literature review and studies have delved considerably into how to best to measure this idea. For the purposes of this study, four dimensions of walkability, found in Table 1 was used to measure walkability of the study area.

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Table 1: The Four Dimensions of Walkability Mentioned by Various Authors Dimensions Authors

Land-use mix Dobesova and Krivka (2012); Leão, Abonizio, Reis, & Kanashiro

(2020); Stockton, et al., (2016); Leslie, Butterworth, & Edwards

(2006); & Mantri (2008)

Connectivity Williams, Borghese, & Janssen (2018); Stockton et al. (2016); Leslie,

Butterworth, & Edwards (2006); Dobesova and Krivka (2012); &

Mantri (2008)

Density Leão, Abonizio, Reis, & Kanashiro (2020); Stockton et al. (2016);

Dobesova and Krivka (2012); Leslie, Butterworth, & Edwards (2006);

& Mantri (2008)

Proximity Mantri, 2008; Williams, Borghese, & Janssen (2018); & Tiran, Lakner

& Drobne (2019)

The conceptual framework that guided the flow of the study is shown in Figure 10 below.

The dimensions of measuring walkability (connectivity, proximity, land use mix, density) was collectively assessed to find out the index of walkability. The index was calculated for the current land use, future land use and the future land use with the alleyways, which will inform urban planning and design for the future development of the study area (downtown Adel).

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Figure 10: Conceptual Framework

Calculating the Index of Walkability

Walking is a multidisciplinary activity and therefore creating the index of walkability involves four dimensions, i.e., connectivity, Land use mix, proximity, and residential density, earlier identified through literature. The following section defines the dimensions and identifies the best method to calculate the respective indices.

Land use mix

Land use mix entails the heterogeneity of land uses in a location. The neighborhood land use mix, often referred to as the entropy, is the proportion of number of land use categories to the actual percentage of individual land uses in an area. Measuring the land use mix of an area can be facilitated by using the entropy score (Frank, et al., 2010; Leão, Abonizio, Reis, & Kanashiro,

2020; Dobesova & Krivka, 2012). The entropy score determines how different land uses within a spatially defined area are scattered (Leslie, Butterworth, & Edwards, 2006). Residents who usually live in places with diverse opportunities of attraction tend to make more frequent shorter trips by walking (Bhadra, Sazid, & Esraz-Ul-Zannat , 2015). The level of diversity of land uses 29 display how interesting the urban form is and how favorable the land is to walk, to access different destinations.

The Entropy score is calculated as:

 (푃 In푃 ) Land Use Mix (LUM) = − 푘 푘 푘 퐼푛푁

where, k = Category of land use, P = proportion of land use devoted to a specific land use, N = number of total land use categories

The entropy score is usually between 0-1, 1 depicting complete heterogeneity of the specified area and 0 complete homogeneity. Homogeneity means, all the land uses are of one, same category, on the other hand heterogeneity indicates that the urban environment has uniformly distributed land uses.

Connectivity Index

Connectivity refers to the directness of going from one point to another (Mantri, 2008).

Understandably, walking or biking in an area where there are minimal connections can be extremely tedious, unnerving, and uninviting. Thus, the connectivity in a spatial region is instrumental to walking. If connectivity is high in a spatial location, it creates more direct and accessible links between two points in the city. The level of connectivity in an area determines travel distance, and the availability of options to a location. The level of connectivity in an area is determined through the links and nodes present in the location.

One of the ways of estimating the connectivity in area is through the gamma index.

Gamma index can be defined as the proportion of links to the maximum possible links between nodes in the area (Dill, 2004).

퐴푐푡푢푎푙 푁푢푚푏푒푟 표푓 퐿푖푛푘푠 Gamma index = 푀푎푥 푛푢푚푏푒푟 표푓 푙푖푛푘푠 푏푒푡푤푒푒푛 푒푥푖푠푡푖푛푔 푛표푑푒푠 30

Where, Max number of links between existing nodes = 3*(Number of nodes – 2)

The gamma index measures are usually between 0-1, where a greater number indicate a higher connectivity index in the area (Gori, Nigro, & Petrelli, 2014).

Link is the pathway between two nodes, from one intersection to another or from one intersection to a dead-end in a lane segment.

Node is the endpoint of a link, from a dangling or a straight end of a link. A node can also be at the dangling end of a link or at the center and joint of a long line of links, where streets or pathways meet.

Figure 11: Street Links and Nodes

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Density

Density encompasses the number of households’ inhabitation to the residential area in a location. The density of housing has greater impact on the proportion of walking trips (Lo,

2009). Density in an area can influence the number of trips or the average automobile mileage over time in an area. Thus, higher-density areas encourage variety of retail and service, resulting in shorter, walkable distances between facilities (Leslie, Cerin, duToit, Owen, & Bauman, 2007).

Net Household Density is the proportion of total number of Households to land dedicated to residential use in a neighborhood.

퐷푖 Net Dwelling Unit = 푅퐴푖

where, D is the dwelling count, RA is the residential area. The residential density can be classified into 4 categories using the classification by Mantri (2008).

Table 2: Density Measure and Category Measure Category

More than 20 households per acre High Density

15 to 20 Households per acre Moderate Density

10 to 15 Households per acre Low Density

Less than 10 Households per acre Sprawl

Proximity

Proximity is how different land uses are in relation to others. How land use is situated from activity centers affect travel behavior, and greatly influence how walkable a place can be.

Proximity defines the average distances a person uses to access respective destinations from origin in a location. It also affects the conduciveness of a place to walking, and one of the very critical dimensions to determine the walkability of an area (Mantri, 2008). 32

Ideally, there should be several necessary activities that should be within a walkable distance to different age groups in a neighborhood. Proximity for this study can be defined as the shortest possible walking distance using an ideal pathway available. Proximity can be classified into the following ranks, as shown in Table 3.

Table 3: Proximity Matrix Distance Rank

Less than 0.25 Miles Highly walkable

0.25 – 0.50 Miles Walkable

0.50 – 0.75 Miles Medium walkable

0.75 – 1.5 Miles Low walkable

More than 1.5 Miles Not walkable

In summary, to ease the measurement of the various indices of the dimension of walkability, there is the need to adopt a standard formula for estimation. The dimensions were assessed using the respective formula to ascertain the overall index of walkability. Table 4 depicts the dimensions with the identified principle to measure the index of walkability.

Table 4: Dimensions of walkability and their Formula Dimension Measuring Principle

Connectivity Gamma Index

Proximity Network Analyst

Land Use Mix Entropy Index

Residential Density Net Dwelling Density

The calculation of the entropy index was done with a Python script. The results derived was reclassified and summed to get the overall walkability index. Additionally, the result of the 33 measure was compared to the already existing walk score from walkscore.com to interpret how well it matches. Data collected in measuring the index is depicted in the Table 5.

Streetscape Evaluation using GSV

To measure physical features of the downtown, Google Sheets was used to prepare a questionnaire (see Appendix B) to help with the streetscapes’ evaluation in GSV. The questionnaire involves specific queries that probe into how to evaluate streetscapes in downtown.

Thus, the urban design qualities are accessed through using Google imagery to evaluate physical features that is related to walking in the location. Details that relate to comfort, aesthetics, linkage, coherence, human scale, safety etc. from urban design literature, are assessed through visual assessments facilitated by GSV. Figure 12 below displays where streetscape features were examined using GSV.

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Figure 12: Locations where Streetscapes were Examined using GSV

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Table 5: Spatial Data Used in Case Study Data Type Source

Parcels Polygon shapefile Dallas County

Residential Facilities Point shapefile City of Adel

Commercial Facilities Point shapefile City of Adel

Recreational Facilities Point shapefile City of Adel

Public Facilities Point shapefile City of Adel

Sidewalks Image Google Street view

Landscape features Image Google Street view

Signage Image Google Street view

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CHAPTER 4. RESULTS AND FINDINGS

The data analysis included the measurement of the level of connectivity, proximity of various land uses, land use mix and residential/dwelling density for both the current and proposed future land use of the downtown. The same calculations for the four dimensions of the walkability index of the downtown was done for both current and future land uses.

Measuring the Connectivity Index

The connectivity index in the area was calculated using the gamma index calculation. The process involves counting the actual number of links intersecting or inside the boundary of downtown. The street nodes connecting the links are also counted and inserted in the gamma index formula:

푁푢푚푏푒푟 표푓 퐷표푤푛푡표푤푛 푆푡푟푒푒푡 퐿푖푛푘푠 Gamma index = 3∗(퐷표푤푛푡표푤푛 푆푡푟푒푒푡 푁표푑푒푠−2)

50 = 3∗(35−2)

Therefore, the connectivity score was 0.505, for both land use maps since I assumed the level of connectivity would not change for the future land use in downtown.

Assessing Proximity of Land Uses

Network Analysis extension of ArcGIS was used to facilitate calculating proximity to various land use activity locations (attraction destinations from household locations) in both current and future land use in the study area. Calculating the proximity dimension of walkability index required getting nearest distances and average distances from trip origins (households’ locations) to destinations (activity locations) for the study area. Signing into ArcGIS online account was pivotal, as it served as the network dataset input to get options like Walk Time and 37

Walk Distances under the type of Mode. Units like kilometer and minutes, which may otherwise not be supported by personally creating a network dataset before performing the network analysis, were accessible from signing into ArcGIS online.

Measuring closest facilities (Current Land Use)

The calculation of the closest facilities was facilitated by using the “Closest Facility” function under Network Analyst. All parcels in the study area were converted to points, and residential lots were used as points of trip generation. All the other identified land uses

(commercial, industrial, Office, and Public spaces) were converted to activity locations (see figure 13). A total of 54 residential parcels (point of origin) and 108 points of activity centers

(Destinations) were identified for the current land use map in the downtown. Closest facility distances were automatedly calculated in the GIS environment, as routes.

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Figure 13: Network Analysis (Current Land Use)

The nearest distance was measured from residential parcels to activity centers in the study area. The distance from one residential parcel in downtown to one activity center, say a commercial parcel, was calculated, and the same process repeated for each residential parcel to all other respective activity centers in the downtown. The nearest distances and walk times for all activity centers were totaled, and the average of the distances was calculated from the respective totals. Table 6 shows the average distances and average walk time from residential lots to all destinations. 370 meters (0.23 miles) was computed as the average distance from origins to destinations in downtown. Consequently, residents will have to walk an average of 4.30 minutes to access respective locations in the area.

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Table 6: Distances to Activity Centers (Current Land Use)

Measuring closest facilities (Future Land Use)

The future land use map had a total of 118 (point of origins) and 120 points of activity centers (Destinations) was identified for the current land use map in the downtown. There are more points for origins and destinations because parcel locations for mixed uses delineated in the future Land use plan double counted for both residential land use and other attraction centers in the area. Figure 14 shows more points as origins compared to activity centers, because points for these activity centers are hidden below the origins.

Table 7 shows the total average distances to respective activity centers. 363 meters (0.226 miles) was computed as the average distance from origins to destinations in downtown for the future land use. Consequently, residents will have to walk an average of 4.25 minutes to access respective locations in the area in the future.

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Figure 14: Network Analyst (Future Land Use)

Table 7: Distances to Activity Centers (Future Land Use)

Land Use Mix

Calculating land use mix was done using the entropy index. It essential to calculate the individual land use percentages, for current and future land uses as shown in Table 8, before the calculation of the entropy index. The individual land use percentages for both future and current land use are calculated using the formula: 41

퐴푡푡푟푖푏푢푡푒 표푓 푆푡푢푑푦 퐴푟푒푎 (퐴푐푟푒푠) Land use percentage = * 100 푆푡푢푑푦 퐴푟푒푎 (퐴푐푟푒푠)

Table 8: Land Use Percentages in Downtown

After the calculation of land use percentages, the entropy index is calculated using a

Python script. The script was constructed to calculate the entropy index individually for the current and future land use of the downtown.

Measuring the Residential Density

The final measure in the walkability index was calculating the residential density in the downtown using the formula for net residential density. The values for residential household count and residential area are very paramount to the calculation of the index. Since the area is very small, a subset of the census block group, the calculation of the index was carried out using an assumption for values of household number. The total household number was measured with a critical look at the residential and mixed land use in downtown, assuming homogenous population distribution. 42

Land use delineated as residential-single family dwelling unit was deemed to have a single household size, residential-medium density as having 6 household sizes and 9 households for residential-heavy density (Municipal Code, 2019). The density score for respective land use maps (Current and Future) were calculated using the dwelling density formula, which is the proportion of household number to the total area of the land area for the area.

Current Land use = (57/11.21) = 5.08

Future Land Use = (242/21.31) = 11.36

Reclassification of Values of the Dimensions

The different values or scores from the four dimensions of walkability (connectivity, proximity, land use mix and residential density) are reclassified into values from 0-100. The scores of connectivity and land use mix which ranged between 0-10, were both reclassified to 0-

100. The proximity score was reclassified using the table 9 below.

The density score which ranges between 0-30 was reclassified into 0-100, using the table

9 below. The reclassification was done to get the same range for all the indices to facilitate summing up and averaging for the walkability index.

Table 9: Classification Matrix Proximity Distance Residential Density Score

Less than 0.25 Miles More than 20 households 100

0.25 – 0.50 Miles 15 to 20 households 75

0.50 – 0.75 Miles 10-15 households 50

0.75 – 1.5 Miles 5-10 households 25

More than 1.5 Miles Less than 5 households 0

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Creating the Walkability Index

The walkability index is calculated by using the above derived data from the four dimensions. The ranges were further summed and averaged to get the overall walkability index for the area. Figure 15 shows the final indices for the dimensions for the current and future land use, reclassified to be between the range of 0-100. The average of the final scores for the land uses gives the final index of walkability for the downtown.

Figure 15: Walkability Index for Downtown Adel

The connectivity for both land uses had the same value of 50.50 on the scale of 0-100.

The proximity of various land uses after reclassification, were also scored at 100.00 for both land-use maps as they fell in the categorization of very accessible in the proximity reclassification matrix. The Land use mix score for the current land use is 87.00 and 91.00 for the future land use. Finally, the density score is 25 for the current land use and 50 for the future 44 land use. Consequently, the overall walkability index, is 65.63 and 72.88 for the current and future land use, respectively.

Comparing Walkability Indices for Downtown Adel

Looking at the indices for the dimensions individually, connectivity and proximity for both land uses can be expected to remain the same, thus land uses will have the same level of accessibility for the future. Nonetheless, Land use mix will increase for the future land use, as more residential prospects will be made accessible in the downtown. The future land use will increase the residential density, as more households will be provided with housing opportunities to live in downtown.

As shown in Table 10, the overall walkability score falls within somewhat walkable for the current land use. Clearly, the blend of more residential land use opportunities in the future land use will also increase the walkability index to a high walkable downtown. Meaning more errands will be able to be performed with walking and thus more physical activity in the downtown environment. Adopting the future land use will improve opportunities for walking in the downtown of Adel. Local businesses will benefit from the development of more residential uses, as more people will live near to patronize businesses in downtown.

Hence, it can be concluded that adopting the future land use will increase prospects of walking for residents in downtown of Adel. Thus, the index can be a beneficial tool for local authorities in measuring walkability and finding ways of improving walking in their respective settings.

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Table 10: Walkability Matrix Walkability Score Description

91-100 Very High (Very Walkable)

70-89 High (Walkable)

50-69 Medium (Somewhat Walkable)

25-49 Low (Car Dependent)

0-24 Very Low (Car Dependent)

The study also measured the connectivity index to assess the difference it would make in the walkability index if alleyways proposed in the downtown were developed. Measuring the level of connectivity is conducted to ascertain the walking prospects for downtown when the alleyways are developed and beautified as stipulated in the downtown Master Plan.

Below is the connectivity index that is derived and improvements to the walkability index. Figure 16 shows a notable increase of the walkability index from 72.9 to 74.9. As the alley way beautification improves the connection in downtown, the level of walkability will consequently increase.

72 Connectivity Index = 3∗(43−2)

Score is 0.585

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Figure 16: Walkability Index with Alleyway Beautification Incorporated

Google Street View (GSV) Measures

The measures included evaluating specific physical features that tie with urban design features, like human scale, legibility, complexity, linkage, coherence and imageability, among others. To measure the physical features of the streetscape, all streets in the downtown was analyzed using the same 16 questions in Google Sheets, with some of the questions posed as a multi-grid question (see Appendix B). Each street intersection to another was evaluated using the whole 16 questions. In total, 46 questionnaires were administered in the downtown to assessed urban design qualities.

Sidewalks were generally available throughout downtown, with majority (80% of all sidewalks in the location) of having over 75% smooth surface. Depicting a considerably degree 47 of linkage in the downtown of Adel. Sidewalks were classified into well maintained, moderately maintained, poorly maintained using the classification identified in Figure 17.

Figure 17: Sidewalk Classification Criteria

The sidewalks were all paved with concrete, with fewer cracks with no potholes

(about 30% of sidewalks had cracks). More than half of all sidewalks were averagely maintained

(57%; see Figure 18). Notably, the 28% of sidewalks which were well maintained were found around the Dallas County Court House

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Figure 18: What are the conditions of the sidewalk from the observed location?

Only 15% of the sidewalks were identified to fit two people from respective observed locations. Most sidewalks linking the various activity centers in the downtown could enable only a person to walk at a time. Thus, it will be inconvenient for two persons to by-pass each other on the sidewalk.

Figure 19: Can Two People Fit on the Sidewalk from the Observed Location?

Pedestrian crossings, which ensures a degree of ownership over the streets people walk were generally absent from downtown. After the streetscape assessments, 37% of the streets had some form of pedestrian crossing. However, the design of these crossings was not sharp enough 49 nor did it have crosswalk signals to project the streets as opportunities to walking, over other modes of transportation.

Figure 20: Is Pedestrian Crossing available at the Observed Location?

About 52% of streetscape features portrayed support for walking pleasure on one side of the street. Pedestrian signs to inform road users of the priority of pedestrian’s safety and convenience over all other modes of transportation were present on neither side of the streets in the downtown. Street planters and furniture, which promotes the human scale urban design quality were absent on neither side of streets. Street furniture were found only in 10% of streets, and on just a side of a specific street. Small planters were identified on sections of street intersections on Nile Kinnick Avenue and Main Street. Trees, on the other side, were found around 52% on both sides of streets in downtown, with 41% of streets having trees on one side of it.

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Figure 21: Streetscape Features in Downtown

Figure 22 depicts the examples of streetscape features that the study sought to identify at observing locations using the GSV. All streetscape images represented (in Figure 22) were seen in downtown, except for pedestrian signage. The Pedestrian signage in Figure 22 represents an ideal road usage symbol that should be incorporated in downtown to ensure a convenient walking opportunity and safe way of accessing sidewalks for pedestrians.

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Figure 22: Examples of Streetscape Features

Additionally, the streets landscape appeared 41% well maintained and 44% averagely maintained if they were present at the observed location. 11% of the evaluated streets had no form of landscape, like lawns around sidewalks, trees canopies, small planters, small trees etc. available at respective assessed locations.

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Figure 23: Condition of Street Landscape

The design and appearance of buildings has influence on a neighborhood’s friendliness to walking. Buildings in downtown in general are moderately well-kept (over 80% of residential buildings in downtown) when they are present at streets. Commercial and industrial structures were impressively well-kept in downtown. The façade of commercial buildings and recreational facilities are well-kept to attract patronage.

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CHAPTER 5. CONCLUSION

This section will discuss the recommendations, limitations of the study, and lastly, the conclusions of the study.

Recommendations

The Adel Downtown Plan 2020-2040, which improves housing opportunities, will ensure more walkable neighborhoods. Additionally, the alley beautification plan (see Adel Downtown

Plan) will ensure increased connectivity. The city can therefore focus on completion of these alleyway beautification plan stipulated in the Downtown Master plan, and at the same time increase the number of linkages in the location.

Pedestrian signages and urban street elements that stimulate pleasure for walking must be ensured in downtown to increase imageability and the extent of human scale in the urban streetscapes. Signages can add to the visual interests, create a sense of place, and makes public places like the public library and Dallas County Courthouse, more inviting. Installation of more street furniture and small planters must be ensured in downtown, as it also enhances the perception of human scale.

There is also a need to provide adequate spaces for pedestrians. Satisfactory protection of pedestrians will encourage residents to walk more in the built environment. The study illustrates the influence that the built environment characteristics has on walking. Improving infrastructure that prioritize pedestrians over automobiles will increase the level of walking in the area and creating a safe built environment for pedestrians should be a top priority.

Therefore, there should be a collaborative effort to expand sidewalk width all around downtown. Sidewalk expansion will increase human scale intensity and promote opportunities for walking in downtown Adel. Maintenance practice for sidewalks in downtown should be 54 prioritized and kept at a high standard thoroughly. Figure 24 suggests locations in downtown that can have sidewalk width increased and have prominent crosswalk designs for pedestrians. The crosswalks can feature crosswalk signs that informs other road users of the opportunities specifically created for pedestrians in downtown.

Figure 24: Potential for Sidewalk Width Expansion and Crosswalk Development

Street trees should be optimized through intense planting and maintenance. Rows of trees on both of streets can also humanize the streets and create a perceived impression of comfort when walking. Street trees must also be properly spaced to provide a sense of enclosure and safety for pedestrians. 55

Additionally, the dimensions need to be individually considered and critically studied to decipher the appropriate means of boosting their values for the built environment. Though the future land use plan has an increase in the walkability index there is still room for improving walkability in Downtown Adel. The good news is that the future land use (see Appendix A) of blocks adjacent to the downtown boundary have some variety on land use types, which will increase walkability.

Limitations of the Study

Though walkability is, the study faced challenges. The household number for the delineated downtown boundary was not possible to acquire because the downtown was a small area inside the census block group for the city of Adel. It is imperative to consider the neighborhood size and availability of population data to facilitate an accurate estimate of the density index. Additionally, the Google Street View images used for streetscape assessment had inconsistency in imagery dates. GSV imagery were dated in 2009, 2011 and 2018, with 63% of streetscapes assessed dated in 2011. However, the results revealed that assessing streetscapes using GSV remains a reliable virtual audit.

Final Remarks

The walkability index provides insight into the factors that encourage a pedestrian friendly downtown. Altering the components provides a significant opportunity to develop centers that favor the right connectivity and accessibility to promote both physical activity and healthy lifestyles. There exist disparities in measures of walkability by researchers over the years and researching and finding the best measures for walkability is a way of mitigating the health and environmental unfortunate circumstances that befall urban centers.

Hence, the walkability evaluation of a neighborhood requires the requisite analysis of the attributes of the built environment in respective neighborhoods. The study assessed 56 characteristics of the urban environment that influence walkability in downtown Adel for both current and future land uses. According to the values of the determined dimensions and analysis, the overall walking for the downtown can be considerably improved in the future land use, if it is developed per the proposed elements outlined in the plan.

The results of the study can be a suitable guide for policy makers and urban planners to increase the walkability of their downtown or respective neighborhoods. Evaluating walkability of an area can be a very pivotal point in the stages of planning. The results of such an endeavor, whether the assessment results in a higher or otherwise value of the index, can facilitate further planning of the area. Mostly, higher walkability index means a proper arrangement of the city to support pedestrians in the urban fabric such that daily errands do not require the use of an automobile. Contrarily, low walkability means there is automobile dependency and almost all errands in the built environment requires the use of an automobile. Hence, encouraging minimum physical activity and questionable health standards as residents have nowhere to walk.

The study has shown clearly that assessment of walkability standards can be performed on both existing built urban environment and proposed land use plans for a neighborhood.

Subsequently, the study presents prospect for urban planners and policy makers to assess proposed plans to know whether their plans support daily errands carried out by walking without much reliance on cars. The prospect can also influence urban policy makers and planners to adequately support efforts to increase diversity, connectivity, density, and proximity of various land uses to support pedestrians’ convenience and safety.

Urban planners should endeavor to find a sustainable solution to achieve the probability of having the shortest distance between trip origins and destinations. The prospect of optimal social cohesion and healthy lifestyles should be a focus in designing urban centers by urban 57 planners. Prudent arrangement of land uses like residential districts, recreational centers, businesses, commercial areas, etc. is then vital for the urban environment and urban life forms.

One way to assure urban planners are sticking to this metric rule is to measure how walkable our built environments are, and to find appropriate solutions to remedy the issues. 58

REFERENCES

Ackerson, K. J. (2005). A GIS Approach to Evaluating Streetscape and Neighborhood Walkability. University of Oregon. Electronic Thesis or Dissertation. Adkins, A., Makarewicz, C., Scanze, M., Ingram, M., & Luhr, G. (2017). Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context? Journal of the American Planning Association, 83(3), 296-314. doi:10.1080/01944363.2017.1322527 Badland, H. M., Opit, S., Witten, K., Kearns, R. A., & Suzanne , M. (2010). Can Virtual Streetscape Audits Reliably Replace Physical Streetscape Audits? Journal of Urban Health: Bulletin of the New York Academy of Medicine, 87(6), 1007-1016. doi:10.1007/s11524-010-9505-x Badland, H., Mavoa, S., Boulangé, C., Eagleson, S., Gunn, L., Stewart, J., . . . Giles-Corti, B. (2017). Identifying, creating, and testing urban planning measures for transport walking: Findings from the Australian national liveability study. Journal of Transport & Health, 151-162. doi:10.1016/j.jth.2016.08.010 Benfield, K. (2012). Can We Quantify a Good Walk? Retrieved from CityLab: https://www.citylab.com/design/2012/07/can-we-quantify-good-walk/2755/ Bhadra, S., Sazid, A., & Esraz-Ul-Zannat , M. (2015). An objective assessment of walkability in Khulna City: A GIS based approach. 3rd International Conference on Green Energy and Technology (ICGET) (pp. 1-5). Dhaka: IEEE. doi:10.1109/ICGET.2015.7315092 Cao, X. J., Mokhtarian, P. L., & Handy, S. L. (2009). The relationship between the built environment and nonwork travel:. Transportation Research Part A: Policy and Practice, 43(5), 548-559. doi:10.1016/j.tra.2009.02.001 City of Adel. (2019). Envision Adel 2040: Comprehensive Plan. Retrieved from adeliowa: https://adeliowa.org/envision-adel-2040/ Clarke, P., Ailshire, J., Melendez, R., Bader, M., & Morenoff, J. (2010). Using Google Earth to Conduct a Neighborhood Audit: Reliability of a Virtual Audit Instrument. Health Place, 16(6), 1224–1229. doi:10.1016/j.healthplace.2010.08.007 Dill, J. (2004). Measuring Network Connectivity for Bicycling and Walking. TRB Annual Meeting CD-ROM, (pp. 1-20). Portland. Dill, J., Mohr, C., & Ma, L. (2014). How Can Psychological Theory Help Cities Increase Walking and Bicycling? Journal of the American Planning Association, 80(1), 36-51. doi:10.1080/01944363.2014.934651 Dobesova, Z., & Krivka, T. (2012). Walkability index in the urban planning: A case study in Olomouc city. In B. Juroslav (Ed.), Advances in Spatial Planning (pp. 179-196). Olomouc city: InTech. 59

Dörrzapf, L., Kovács-Győri, A., Resch, B., & Zeile, P. (2019). Defining and assessing walkability: a concept for an integrated approach using surveys, biosensors and geospatial analysis. Urban Development Issues, 62(1), 5-15. doi:10.2478/udi-2019-0008 Dovey, K., & Pafka, E. (2020). What is walkability? The urban DMA. Urban Studies, 57(1), 93- 108. doi:10.1177/0042098018819727 Ewing, R., & Cervero, R. (2010). Travel and the Built Environment. Journal of the American Planning Association, 76(3), 265-294. doi:10.1080/01944361003766766 Ewing, R., & Handy, S. (2009). Measuring the Unmeasurable: Urban Design Qualities Related to Walkability. Journal of Urban Design, 14(1), 65-84. doi:10.1080/13574800802451155 Frank, L., Sallis, J., Saelens, B., Leary, L., Cain, K., Conway, T., & Hess , P. (2010). The Development of a Walkability Index: Application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, 44(13), 924-933. doi:10.1136/bjsm.2009.058701 Gori, S., Nigro, M., & Petrelli, M. (2014). Walkability Indicators for Pedestrian-Friendly Design. Journal of the Transportation Research Board, 2464(1), 38-45. doi:10.3141/2464-05 Horan, E., Yang, Y., & Eidt, T. (2015). Downtown Gresham Walkability Study. University of Oregon. City of Gresham: Sustainable City Year Reports. Hruby, A., & Hu, F. B. (2015). Epidemiology of Obesity: A Big Picture. Pharmacoeconomics, 33(7), 673-389. doi:10.1007/s40273-014-0243-x Kelly, P., Murphy, M., & Mutrie, N. (2017). The Health Benefits of Walking. In C. Mulley, K. Gebel, & D. Ding (Eds.), Walking: Connecting Sustainable Transport with Health (Vol. Transport and Sustainability). Emerald Publishing Limited. Koohsari, M. J., Owen, N., Cerin, E., Giles-Corti, B., & Sugiyama, T. (2016). Walkability and Walking for Transport: Characterizing the Built Environment Using Space Syntax. International Journal of Behavioral Nutrition and Physical Activity, 1-9. doi:10.1186/s12966-016-0448-9 Leão, A. L., Abonizio, H. Q., Reis, R. S., & Kanashiro, M. (2020). Walkability Variables: An Empirical Study in Rolândia - PR, Brazil. Associação Nacional de Tecnologia do Ambiente Construído, 20(2), 475-488. doi:10.1590/s1678-86212020000200410 Lee, S., & Talen, E. (2014). Measuring Walkability: A Note on Auditing Methods. Journal of Urban Design, 19(3), 368-388. doi:10.1080/13574809.2014.890040 Leslie, E., Butterworth, I., & Edwards , M. (2006). Measuring the walkability of local communities using. Walk21-VII, “The Next Steps”. Melbourne, Australia. Retrieved from https://www.researchgate.net/publication/241592167 Leslie, E., Cerin, E., duToit, L., Owen, N., & Bauman, A. (2007). Objectively Assessing’ Walkability’ of Local Communities: Using GIS to Identify the Relevant Environmental 60

Attributes. In P. Lai , & A. Mak , GIS for Health and the Environment. Lecture Notes in Geoinformation and Cartography (pp. 91-104). Berlin, Heidelberg: Springer. Litman, T. A. (2018). Economic Value of Walkability. Victoria Transport Policy Institute. Retrieved from https://www.vtpi.org/walkability.pdf Lo, R. H. (2009). Walkability: what is it? Journal of Urbanism, 2(2), 145-166. doi:10.1080/17549170903092867 Mantri, A. (2008). A GIS Based Approach to Measure Walkability of a Neighborhood. Electronic Thesis or Dissertation. Retrieved from https://etd.ohiolink.edu/ New York State Department of Health. (2020). Obesity Prevention. Retrieved from Department of Health: https://www.health.ny.gov/prevention/obesity/ Rundle, A. G., Bader, M. D., Richards, C. A., Neckerman, K. M., & Teitler, J. O. (2011). Using Google Street View to Audit Neighborhood Environments. American Journal of Preventive Medicine, 40(1), 94-100. doi:10.1016/j.amepre.2010.09.034 Rypkema, D. D. (2003). The Importance of Downtown in the 21st Century. American Planning Association, 69(1), 9-15. doi:10.1080/01944360308976290 Singh, R. (2016). Factors affecting walkability of neighborhoods. Social and Behavioral Sciences, 643 – 654. Stockton, J., Duke-Williams, O., Stamatakis, E., Brunner, E. J., Mindell, J. S., & Shelton, N. J. (2016). A Novel Walkability Index for London Predicts Walking Time in Adults. Proceedings of the Universities' Transport Study Group: 48th Annual Conference (pp. 1- 12). Bristol, UK.: Universities' Transport Study Group. The Pedestrian and Bicycle Information Center. (2010). The National Bicycling and Walking Study: 15–Year Status Report. Federal Highway Administration, U.S. Department of Transportation. Retrieved February 24, 2020, from http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf Tiran, J., Lakner, M., & Drobne, S. (2019). Modelling walking accessibility: A case study of Ljubljana, Slovenia. Moravian Geographical Reports, 27(4), 194-206. doi:10.2478/mgr- 2019-0015 United Nations. (2019). Sustainable Development Goal 11. Retrieved from Sustainable Development Goals: https://sustainabledevelopment.un.org/sdg11 United Nations. (2019). Sustainable Development Goal 3. Retrieved from Sustainable Development Goals: https://sustainabledevelopment.un.org/sdg3 United States Environmental Protection Agency. (2019). Smog, Soot, and Other Air Pollution from Transportation. Retrieved from Environmental Protection Agency: https://www.epa.gov/transportation-air-pollution-and-climate-change/smog-soot-and- local-air-pollution 61

US Census Bureau. (2019). ACS Demographic and Housing Estimates. Retrieved from United States Census Bureau: https://data.census.gov/cedsci/table?d=ACS%205- Year%20Estimates%20Data%20Profiles&table=DP05&tid=ACSDP5Y2016.DP05&g=0 400000US19_1600000US1900505 Weinberger , R., & Sweet, M. N. (2012). Integrating Walkability into Planning Practice. Transportation Research Record: Journal of the Transportation Research Board, 2322(1), 20-30. doi:10.3141/2322-03 Wikipedia. (2020). Adel, Iowa. Retrieved from Wikipedia: https://en.wikipedia.org/wiki/Adel,_Iowa Williams, G. C., Borghese, M. M., & Janssen, I. (2018). Neighborhood Walkability and Objectively Measured Active Transportation among 10–13 year olds. Journal of Transport & Health, 8, 202-209. doi:10.1016/j.jth.2017.12.006 Zhang, X., & Mu, L. (2019). The perceived importance and objective measurement of walkability in the built environment rating. Environment and Planning B: Urban Analytics and City Science, 0(0), 1-17. doi:10.1177/23998083|9832305

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APPENDIX A. FUTURE LAND USE MAP FOR THE CITY OF ADEL

Source: City of Adel Comprehensive Plan 2020-2040

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APPENDIX B. QUESTIONNAIRE FOR ASSESSING DOWNTOWN'S WALKABILITY USING GSV

Walkability Survey

1. What is the street name at the observing location?

2. What is the observed latitude?

3. What is the observed longitude?

4. Date of imagery?

Example: January 7, 2019

5. How many lanes does the street have?

Mark only one oval.

1 2

3 4 Other: 64

6. What are the conditions of the street from the observed location?

7. Are sidewalks visible from the observed location?

Mark only one oval.

Yes No 8. What are the characteristics of the sidewalk from the observed location? 65

9. What are the conditions of the sidewalk from the observed location?

Mark only one oval.

Well maintained averagely maintained

poorly maintained Other:

10. Can two people fit on the sidewalk from the observed location?

Mark only one oval.

Yes No 11. Can a person easily ride a bike on the sidewalk?

Mark only one oval.

Yes No 66

12. Conditions of the properties?

13. Streetscape features at the observed location?

14. Is pedestrian crossing available at the observed location?

Mark only one oval. 67

Yes No

15. Is there street landscape available at the observed location?

Mark only one oval.

Yes No

16. What is the condition of the street landscape at the observed location?

Mark only one oval.

Well maintained

averagely maintained

poorly maintained

Other:

Forms