Retrofitting the Suburbs to Increase Walking: Evidence from a Land-use-Travel Study

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DOI: 10.1177/0739456X11427914

published online 30 November 2011Journal of Planning Education and ResearchKenneth Joh, Mai Thi Nguyen and Marlon G. Boarnet

Attitudes?Can Built and Social Environmental Factors Encourage Walking among Individuals with Negative Walking

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IntroductionAfter decades of automobile dominance in suburban America, walking is back in style. For a multitude of reasons, an inter-disciplinary group of scholars and practitioners have con-verged on the notion that walkable neighborhoods are once again desirable. Architects profess the potential for urban design to create a stronger “sense of community” and a “sense of place” (Calthorpe 1993; Duany and Plater-Zyberk 1991). Some transportation planners attest to the reduction in automobile travel by those living in more pedestrian-oriented neighborhoods. These transportation planners claim that well-designed, compact neighborhoods can shorten the length or reduce the number of automobile trips, induce individuals to shift from automobile use to alternative forms of transporta-tion, or encourage individuals to relinquish automobiles altogether (Boarnet and Crane 2001; Crane 1996; Ewing and Cervero 2001, 2010). Public health professionals have mea-sured the health benefits from an increase in physical activity for those living in pedestrian-oriented neighborhoods (Frank and Engelke 2001; Frank et al. 2006; Saelens, Sallis, and Frank 2003). The physical compactness of walkable neighborhoods also align with environmentalists’ desires to reduce the impact of development on the natural environment. This recent convergence of support for walkable neighborhoods has steered the direction of research away from questions about the benefits of walking toward determining which factors encourage more walking.

While there is broad-ranging consensus about the positive benefits of walking, scholars are less certain about the factors that promote walking in neighborhoods. Is it the built envi-ronment, social environment, or lifestyle attitudes that can explain why some individuals walk more than others? More vexing to scholars studying this question is the challenge of disentangling which of these factors has a greater influence on walking behavior. One of the key questions emerging from this literature is whether the design of the built environ-ment encourages more walking above and beyond individu-als’ attitudes toward walking or their predisposition to walk. In other words, can physical design of neighborhoods encour-age people to walk even if they hold negative attitudes toward walking?

We examine this question using survey information from 2,125 residents of eight neighborhoods in the South Bay area of Los Angeles County, California. We compare individuals who have positive attitudes about walking to individuals

427914 JPEXXX10.1177/0739456X11427914Joh et al.Journal of Planning Education and Research

Initial submission, September 2009; revised submissions, March and August 2011; final acceptance, September 2011

1Texas A&M University, College Station, TX, USA2University of North Carolina at Chapel Hill, NC, USA3University of Southern California, Los Angeles, CA, USA

Corresponding Author:Kenneth Joh, Department of Landscape Architecture and Urban Planning, Texas A&M University, 3137 TAMU, College Station, TX 77843-3137 Email: [email protected]

Can Built and Social Environmental Factors Encourage Walking among Individuals with Negative Walking Attitudes?

Kenneth Joh1, Mai Thi Nguyen2, and Marlon G. Boarnet3

AbstractWe investigate whether the design of the built environment encourages walking above and beyond individuals’ attitudes toward walking. With data from a regional travel survey, we use regression analyses to examine differences in neighborhood walking trips among residents with positive and negative attitudes toward walking. The results show that built and social environment factors have a differential impact on walking trips depending on a person’s walking attitudes. Therefore, strategies to promote positive walking attitudes should be pursued in tandem with land use policies to encourage neighborhood walking.

Keywordswalking, attitudes, travel behavior, built environment, pedestrian



2 Journal of Planning Education and Research XX(X)

who have negative attitudes toward walking to determine whether physical or social environments differentially affect walking trips in their neighborhood center, controlling for sociodemographic characteristics. While there have been quite a few studies that have examined the relationship between attitudes, physical environment, and social environment, very few studies have examined the relative importance of all three sets of factors. Moreover, Los Angeles area residents are notorious for their autocentricity and, therefore, this study may provide for a more stringent test of the influence of the physical and social environments on walking behavior.

This article is organized as follows. First, we will give a review of the literature, focusing on how the built environ-ment, crime and neighborhood safety, and individual atti-tudes affect walking behavior. We will then present the methodology of our research, followed by a summary and dis-cussion of empirical results and some policy implications.

Neighborhoods and WalkabilityMuch of the recent shift from auto-oriented to pedestrian-oriented neighborhoods can be credited to the New Urbanist (or neotraditional) design movement that emerged in the 1990s. New Urbanist designs call for more pedestrian-oriented neighborhoods that include elements such as narrower streets, tree-lined sidewalks, shallow setbacks, front porches, corner grocery stores, and a town center that houses civic buildings, retail shops, and public open spaces. There is evidence that these neighborhoods encourage more walking than other, more traditionally suburban, neighborhood developments .1 This urban design theory sparked a host of empirical studies testing the relationship between urban form and travel behav-ior (Boarnet and Crane 2001; Boarnet et al. 2011; Cervero and Kockelman 1997; Chatman 2009; Handy 1996a, 1996b; Handy, Cao, and Mokhtarian 2005; Kitamura, Mokhtarian, and Laidet 1997; Krizek 2003). In the past few years, several reviews on the topic have been published (Badoe and Miller 2000; Brownstone 2008; Crane 2000; Ewing and Cervero 2001, 2010; Handy 1996b, 2005). As such, we will only review the literature on the factors influencing walk-ing behavior.

Built Environment and WalkingInfluenced in part by the New Urbanism movement, several studies have focused on the relationship between urban form and walking and how land use patterns affect pedes-trian behavior (Badoe and Miller 2000; Cao, Mokhtarian, and Handy 2009b; Crane 2000; Greenwald and Boarnet 2001; Lee and Moudon 2006; Rodríguez and Joo 2004). Many of these studies have relied on travel data from the National Household Travel Survey (Agrawal and Schimek 2007; An and Chen 2007; Boer et al. 2007; Ferdous et al. 2011) as well as regional and metropolitan travel surveys (Boarnet et al. 2011; Cervero and Gorham 1995; Crane and Crepeau

1998; Handy 1996b). Recent attention has been given to studying walking behavior for different subgroups of the population, including school children, the elderly, disabled persons, immigrants, and other sociodemographic groups (Blumenberg and Evans 2006; Boarnet et al. 2005; Dumbaugh and Frank 2007; Hess 2008; Kerr et al. 2007; McDonald 2008; McMillan 2007; Zhu et al. 2011).The link between walking and physical activity and health has also been high-lighted in recent research (Boarnet, Greenwald, and McMillan 2008; Frank and Engelke 2001; Frank et al. 2006; Frank et al. 2008; Lathey, Guhathakurta, and Aggarwal 2009; Saelens, Sallis, and Frank 2003; Sallis et al. 2009; Zhu and Lee 2009).

The results from these studies have shown a fairly strong link between the built environment and walking behavior. In particular, the proximity of retail and commercial land uses has been attributed to an increased number of walking trips. Paired case studies by Handy and her colleagues (Cao, Mokhtarian, and Handy 2009b; Handy 1996a; Handy and Clifton 2001; Handy, Clifton, and Fisher 1998; Handy, Cao, and Mokhtarian 2006) have shown that the proximity to neighborhood businesses is a robust predictor of walking trips. Several other studies have also found that residents living in or near commercial or mixed-use areas took more frequent walking trips than those who did not (Boarnet et al. 2011; Frank and Pivo 1994; Joh et al. 2008; Krizek 2003; Shriver 1997).

Numerous studies have also shown that street and side-walk connectivity is correlated with walking behavior (for a recent review, see Ewing and Cervero 2010). In a study of twelve neighborhoods in the Puget Sound region, Moudon et al. (1997) found that pedestrian activity was higher in neighborhoods with direct pathways and a more complete system of pedestrian facilities (e.g., block size and sidewalk length). Other studies have shown a correlation between street geometry and walking trips, with higher rates of walk-ing trips reported in neighborhoods with traditional gridiron street patterns (Cervero and Duncan 2003; Saelens, Sallis, and Frank 2003; Handy, Cao, and Mokhtarian 2006).

The quality of the pedestrian environment, such as the presence of street lighting, benches, landscaping, trees, and other amenities has also been cited as a factor by some as a determinant of walking behavior (Forsyth et al. 2008). However, the empirical evidence suggesting this link has been weaker than the correlation between walking and prox-imity to neighborhood businesses or connectivity. For instance, Handy (1996b) found that differences between traditional and modern neighborhoods in terms of walking quality did not seem to affect the choice to walk. In addition, studies by Cervero and Kockelman (1997) and Kitamura, Mokhtarian, and Laidet (1997) showed that walking quality was not the most robust indicator for determining walking trips and appeared to matter mainly for nonwork trips and not com-mute trips.

While these studies show considerable evidence that built environmental factors matter for walking behavior, the



Joh et al. 3

question still remains: to what extent? Despite the large body of literature on this topic, the question of which characteris-tics of the built environment have the strongest association with walking is still under debate. Part of the reason, we believe, is the limited scope of these studies: while nearly all control for socioeconomic variables, very few studies include social environmental or attitudinal factors in their analyses of walking behavior, which may explain the propensity to walk more strongly than built environment factors (Handy 2005).

Crime, Safety, and WalkingWhile earlier studies have primarily focused on the relation-ship between the built environment and crime, exemplified by the concepts of Jane Jacobs’s (1961) “eyes on the street” and Oscar Newman’s (1972) “defensible space” emphasizing the importance of pedestrian-scale environments that pro-mote opportunities for informal community surveillance, an increasing number of studies have examined the relationship between crime and walking behavior (for a review of the literature, see Loukaitou-Sideris 2006). Built environment characteristics have been cited as key factors for promoting and mitigating crime in urban settings (Block and Block 1995; Loukaitou-Sideris et al. 2001). Several of these stud-ies have found that criminal activity tends to be concentrated in specific nodes or “hot spots” in the city (Nasar and Fisher 1993; Loukaitou-Sideris 1999), including bus and rail transit stations (Liggett, Loukaitou-Sideris, and Iseki 2003; Loukaitou-Sideris 1999; Loukaitou-Sideris, Liggett, and Iseki 2002).

A number of recent studies have examined the impacts of crime on walking and physical activity for certain sociode-mographic groups, including minority adults (McDonald 2008) and the elderly population (Satariano et al. 2010). McDonald’s cross-sectional study of walking behavior for minority adults in Oakland, California, showed a significant negative association between violent crime and amount of daily walking activity. Satariano et al. (2010) found that elderly persons who perceived their neighborhood to be unsafe spent less time walking than those who viewed their neighborhoods as safe. These findings suggest that crime rates impact walking trip rates and duration.

While many studies have focused on objective measures of crime, other studies have analyzed subjective measures of crime, such as fear and perceptions of neighborhood safety, on walking behavior. Fear of crime, while not synonymous with actual crime, often affects one’s decision to walk. While fear can be shaped by actual reported incidences of crime through media reports and accounts from victims, fears are often shaped by one’s perception of the environment and its safety, which may or may not correlate with actual crime rates. Studies have also shown that fear and perceptions of crime vary across sociodemographic groups (Day 1999; Koskela and Pain 2000; Ortega and Miles 1987; Pain 2001). For instance, women, especially those of minority groups, are more likely to avoid walking at night due to concerns

over personal safety (Atkins 1989; Ross 2000). While this may suggest that the gap between perceived risk and actual risk of crime may be larger for women and minority groups, these perceptions nevertheless are likely to have a highly negative impact on walking, even in neighborhoods with low actual crime rates.

While studies have shown that both objective and subjec-tive measures of crime affect walking behavior, the results have been inconsistent in terms of which set of measures has a stronger negative impact on walking trips (Foster and Giles-Corti 2008; Oh et al. 2010). Further research would be needed to separate associations between actual and perceived crime and walking activity. Nevertheless, the results from these studies generally point to the conclusion that improv-ing neighborhood safety is the key to promoting more walk-ing trips. A 1994 survey conducted by the U.S. Department of Transportation found that the majority of respondents would walk more if safety was not an issue, and other studies have also shown that perceived safety is an important environ-mental quality for walking (Bauman et al. 1996; Hawthorne 1989). For example, in a walking study based in Ontario, Canada, Hawthorne (1989) found that safety from crime was one of the strongest factors for walking. Therefore, these studies suggest that actual and perceived crime, shaped by built environment and social factors, have a strong impact on walking behavior.

Attitudes and WalkingThe role of individual attitudes in shaping travel behavior has often been overlooked in the travel behavior literature because of methodological difficulties in assessing personal attitudes. However, researchers have begun to acknowledge this gap and a number of recent empirical studies have incor-porated attitudinal factors in their analyses of travel behavior (Cao 2008; Cao, Handy, and Mokhtarian 2006; Frank et al. 2007; Handy, Cao, and Mokhtarian 2005, 2006; Joh et al. 2008; Khattak and Rodriguez 2005; Kitamura, Mokhtarian, and Laidet 1997). With respect to walking behavior, some have suggested that attitudinal factors may be a stronger deter-minant of walking than built environment factors (Kitamura, Mokhtarian, and Laidet 1997; Bagley and Mokhtarian 2002; Handy, Cao, and Mokhtarian 2005). For example, Handy (1996b) found that the quality of the walking environment did not appear to affect the choice to walk, while Moudon et al. (1997) reported high walking trip rates in neighbor-hoods that have been rated low in terms of pedestrian acces-sibility. While both studies did not include attitudinal variables in their analyses, their results suggest that built environment factors may not be as important for individuals who are strongly motivated to walk. This line of research points to the need to factor in individual attitudes in shaping decisions to walk.

Although there is a general consensus among scholars that attitudes should be measured, there are methodological




challenges to doing so. The ideal research method would involve a study of individuals moving from an auto-oriented neighborhood to a pedestrian-oriented neighborhood and measuring the individual’s attitudes toward travel before and after the move. This methodology would address questions about causality and residential self-selection bias. As with much social science research, it is rare that an experimental design of this nature is feasible with human subjects in the real world. But there have been several recent studies that have helped us learn more about these questions regarding attitudes and self-selection (see Cao, Mokhtarian, and Handy 2009a, for a recent review of these studies). Handy, Cao, and Mokhtarian’s (2005) paired case study of tradi-tional and suburban neighborhoods in Northern California revealed that differences in travel behavior across these two types of neighborhoods were largely attributable to attitudi-nal factors rather than built environment characteristics. Cao, Handy, and Mokhtarian (2006) investigated this ques-tion in their case study of pedestrian trips in Austin, Texas. They analyzed pedestrian trips (strolling trips and pedestrian shopping trips) and found that residential self-selection affected both types of trips, but especially for pedestrian shopping trips. However, the results also showed that the built environment did have a separate effect for certain types of walking trips, such as shopping trips, which appear to sug-gest that the built environment still matters. Schwanen and Mokhtarian’s (2005) study of mismatched urban and subur-ban residents in the San Francisco Bay area further support this notion, which seemed to show that the built environment had less of an impact on urban residents (i.e., those who pre-ferred to walk did) than for suburban residents (i.e., those who preferred to walk often had little choice but to drive). In contrast, Chatman (2009) argues that the effects of residen-tial self-selection does not appear to bias the effects of the built environment as much as others have suggested and therefore should not be considered a major factor in explain-ing differences in travel behavior.

While most of these studies have shown that observed pat-terns of walking behavior can be attributed to built environ-ment factors in addition to some residential self-selection, few studies have included attitudinal measurements for walk-ing behavior and no studies to date have segmented the walk-ing population by positive and negative attitudes toward walking. Thus, we use this novel approach to shed light on the existing literature and to determine how attitudinal dispo-sitions influence walking behavior above and beyond the effects of the built and social environments.

Study Areas and DataThe neighborhoods selected for this study are located in the South Bay area of Los Angeles County, which is situated in the southwestern portion of the Los Angeles basin, stretching from Los Angeles International Airport (LAX) to the Palos

Verdes Peninsula. With a population of more than 1 million, it is one of the most racially and ethnically diverse regions in the United States, containing fifteen incorporated cities and several unincorporated communities, including small portions of the city of Los Angeles. In the South Bay area as a whole, there is almost an even proportion of non-Hispanic whites, Hispanics, African Americans, and Asians (U.S. Census Bureau 2000); however, the racial and ethnic makeup varies widely across the neighborhoods in the study. For example, the majority of whites and Asians are concentrated in the communities bordering the Pacific coast such as El Segundo, Hermosa Beach, Redondo Beach, and Torrance, while Hispanic and African American populations tend to be concentrated further inland in the cities of Gardena, Hawthorne, and Inglewood.

Study AreasEight neighborhoods in the South Bay area were selected for our study: four “center” neighborhoods and four “corridor” neighborhoods.2 Figure 1 shows the location of these neigh-borhoods in the South Bay area. The “center” and “corridor” terms refer to the spatial layout of the neighborhood—the center neighborhoods have a cluster of commercial develop-ment in the center (typically the historic downtown and/or civic center) while corridors have commercial development concentrated along an arterial corridor. The selection of these neighborhoods was the result of a lengthy site-selection pro-cess to compare two different geometries with commercial concentrations surrounded by residential land uses. In gen-eral, the vintage of the center neighborhoods are older than the corridor neighborhoods, dating from the pre–World War II era. They have relatively compact development and grid street patterns. The corridor neighborhoods are more typical of suburban neighborhoods built during the postwar period and typically comprise of strip mall developments along a major arterial. All study areas (except Pacific Coast Highway)were divided into inner and outer rings corresponding to census block group boundaries, with the inner ring boundary approximately one-quarter mile from the center or arterial corridor and the outer ring approximately one-half mile.

DataThis study relies primarily on travel survey data from 2,125 households in the eight South Bay study areas (South Bay Travel Survey).3 This 155-question, primarily web-based survey asked questions about trip purpose, mode choice, and trip distance in addition to questions about attitudes and perceptions about walking and safety in the respondent’s neighborhood. Data from the travel survey was merged with other publicly available data sources, including the 2000 U.S. Census data, 2006 crime data from the Bureau of Justice Statistics, the infoUSA database (including NAICS



Joh et al. 5

code at the six-digit level, employees, and sales), and GIS parcel data maps obtained from the Los Angeles County Assessor’s Office. Built environment data were also derived from mapping software and satellite imagery from Google Maps.

Understanding the sample limitations such as low survey response rates (e.g., Hawthorne) and small sample sizes (e.g., Inglewood), we attempted to determine whether respondents of the survey were different from the general population in the South Bay area. To do so, we compared the demographic characteristics of survey respondents with neighborhood resi-dents in each of the eight study neighborhoods (see Table 1). Comparing the survey data with the census data suggests that whites tend to be overrepresented, while Hispanics and African American populations appear to be underrepre-sented. However, this discrepancy may be due to survey respondents who declined to state their race or ethnicity, which in most study areas was comparable to the gap between census and survey proportions for whites. While we acknowl-edge that the underrepresentation of minority populations limits our ability to compare attitudinal differences within sociodemographic groups, we believe that the analysis of attitudinal differences across the entire sample, the focus of this study, would be robust because of its relatively large sample size.

MethodsWe use regression analyses to analyze differences in walk-ing behavior for individuals with positive attitudes toward walking and those with neutral or negative attitudes toward walking. To compare these groups, the sample was stratified into two groups based on attitudinal differences, hereafter referred to as “high-walk” and “low-walk” attitudes. A walking attitude index was constructed based on an additive measure of three attitudinal questions from the South Bay travel survey. For each question, respondents were asked to rate on a 5-point ordinal scale (5 = very important; 4 = important; 3 = neutral; 2 = rather unimportant; 1 = not at all important).4 The median attitude index value of 10 was used as the threshold for stratifying the sample into high-walk and low-walk groups. Therefore, respondents who scored 10 or higher on the attitude index were included in the high-walk sample, while those who scored 9 or lower on the scale com-prised the low-walk sample, with index scores ranging from 5 to 15.

We tested six regression models to examine the relative strength of social environment (violent crime rates and atti-tudes toward neighborhood safety) and built environment measures for high-walk and low-walk respondents from our sample of eight neighborhoods. While our site selection pro-cess included center and corridor neighborhoods, the focus of this paper is on using the survey data to compare crime, attitude, and built environment factors across the entire sam-ple of neighborhoods; therefore, we do not make a distinc-tion between center and corridor neighborhoods in our regression analysis. Walking behavior, the dependent vari-able, is a count measure of the number of individual walking trips taken by the respondent in his or her neighborhood cen-ter as reported in the one-day travel diary. Neighborhood center is defined in the travel survey as the area within a ten-minute walking distance (approximately one-quarter-mile radius) from the respondent’s residence. Therefore, this measure captures neighborhood walking trips taken for a variety of trip purposes, including work and school trips in addition to nonwork trips (e.g., meals, personal shopping and services, and entertainment and recreational trips).

Since the dependent variable is count data, negative bino-mial regressions were used (Cameron and Trivedi 1998). The negative binomial regression models were constructed in which sets of variables were included additively with each subsequent model. For both high- and low-walk groups, the first model contains sociodemographic variables in addition to a control dummy variable based on whether persons live in the inner or outer ring of the neighborhood. The second model includes sociodemographic and social environment variables along with the inner ring variable. Finally, built environment variables are added in the third model (full model). For all regression models, standard errors were clus-tered by study area since the crime and built environment

Figure 1. Map of South Bay study areas



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Joh et al. 7

variables take on the same value within each study area (see Moulton 1990 for further discussion).

The sociodemographic variables used in our models are typical of those found in the travel behavior literature (Handy, Cao, and Mokhtarian 2006). These include race and ethnicity, gender, age, household income, employment status, children in the household, and foreign-born status. All of the sociode-mographic variables are dichotomous variables. However, the age category was stratified into four groups (younger than 26, 26 to 40, 41 to 65, and older than 65) and household income was stratified into three income brackets (less than $35,000, $35,000-$75,000, and greater than $75,000).5

For details about these variables and their coding, see Table 2.

The next set of variables included in our models are social environment (SE) variables, which includes a mea-sure of violent crime rates and a measure of individual per-ceptions about crime and neighborhood safety. The violent crime rate data, obtained from the Bureau of Justice Statistics (2006), measures violent crime rates for the respective cities where the study areas are located and is defined as the number of violent crimes per 100,000 per-sons. Violent crimes constitute the following criminal offenses: murder, manslaughter, forcible rape, robbery, and aggravated assault. The neighborhood safety attitude vari-able is obtained from a survey question designed to gauge how much importance respondents placed on being able to walk safely in their neighborhood.6

Table 2. List of Variables in Negative Binomial Models

Variable Variable Coding

Dependent variable Individual walking trips Actual counts of walking trips taken by the respondent in the neighborhood center as

reported in the one-day travel diaryIndependent variables Sociodemographic Race/ethnicity Hispanic Hispanic =1; else = 0 Asian Asian = 1; else = 0 African American African-American = 1; else = 0 Other Other race = 1; else = 0a

Gender 1 = female; 0 = male Age <26 years <26 years =1; else = 0 26-40 years 26-40 years = 1; else = 0 41-65 years 41-65 years = 1; else = 0b

Household income Low income <$35,000 =1; else = 0 Moderate income $35,000-$75,000 = 1; else = 0c

Employment status 1 = employed; 0 = unemployed Household with children 1 = at least one child in household; 0 = no children in household Foreign-born status 1 = born outside of the U.S.; 0 = born in the U.S. Social environment Violent crime rate No. of violent crimes per 100,000 persons for the city where the study area is located Neighborhood safety attitude 1 = not at all important; 2 = rather unimportant; 3 = neutral; 4 = important; 5 = very

important Built environment Neighborhood businesses per acre The number of neighborhood businesses per acre in the study area, defined by six-

digit NAICS codes Intersection density The number of intersections divided by the land area for each study area Four-way intersections The percentage of intersections within each study area that are four-way intersections Control Inner ring 1 = resident lives in the inner ring; 0 = resident lives in the outer ring

aNon-Hispanic whites were treated as a reference category and thus omitted.bThe “older than 65” group were treated as a reference category and thus omitted.cThe “high income” (>$75,000) group were treated as a reference category and thus omitted.




The models also included three variables measuring the built environment (BE): neighborhood businesses per acre, intersection density, and percentage of four-way intersec-tions. These variables were aggregated from census block groups comprising each study area. The data for neighbor-hood businesses were obtained from the infoUSA database and measure the concentration of neighborhood businesses within each study area. Neighborhood businesses were defined to be local population-serving retail and service establishments, categorized by six-digit NAICS codes (refer to the Appendix section in Boarnet et al. 2011 for definition of neighborhood businesses). We also included two mea-sures of street connectivity: intersection density (intersec-tions per square mile) and the percentage of four-way intersections for each study area. These were calculated from GIS parcel data maps in addition to mapping software and satellite imagery from Google Maps. These BE variables are typically used in studies examining how the “3 Ds” (density, diversity, design) affect travel behavior (Cervero and Kockelman 1997; Lee and Moudon 2006). While land use mix is also a common BE variable in the literature, we did not include it in our analysis because we selected neighbor-hoods with similar land use mixes (all eight study areas com-prise of commercial centers and corridors surrounded by mostly residential land uses).

In addition to these variables, we also include a dummy control variable for residents living in the inner and outer ring to control for the possibility that proximity to the neighbor-hood commercial center would encourage more walking. The inner ring comprises the area closest to the center (approxi-mately ! mile radius) while the outer ring comprises the area beyond the ! mile radius, roughly between ! and " mile from the center. The exact boundaries for the inner and outer rings were based on 2000 Census block group boundaries.

ResultsSummary of Overall Walking Trips by Study AreaSummaries of the overall measures of walking behavior, by study area, are presented in Table 3. The number of per person walking trips per day ranged from a low of 0.083 in Inglewood to a high of 0.276 in Riviera Village. The percentage of respondents walking in their neighbor-hood center ranged from 8.18 percent in Artesia Boulevard to 53.13 percent in Riviera Village. On average, center neighborhoods reported a higher number of daily indi-vidual walking trips (0.191 walking trips) than corridor neighborhoods (0.073 walking trips). In addition, 47.32 percent of respondents from center neighborhoods reported walking in their neighborhood center while only 24.41 percent of respondents living in corridor neighbor-hoods reported walking. These findings appear to support New Urbanist assertions that people tend to walk more in traditional mixed-use neighborhoods (Handy 1996b; Crane 1996).

Summary of High-Walk and Low-Walk GroupsAmong those who responded to the questions about atti-tudes toward walking, 1,045 respondents (53.81 percent) were categorized as “high-walk” and 897 respondents (46.19 percent) were categorized as “low-walk” based on the walking attitude index. The distribution of high-walk and low-walk respondents by study area is shown in Table 4.

Based on our sample of the four center neighborhoods, El Segundo had the highest percentage of high-walk residents (62.79 percent) while Inglewood had the lowest (42.86 percent). High-walk residents comprised the majority in two

Table 3. Walking Trip Rates by Study Area

Study AreaPer Person Walking Trips

Per Day% of Respondents Who Usually Walk

in Their Neighborhood Center No. of Responses

Center neighborhoods El Segundo 0.133 50.16 324 Inglewood 0.083 30.88 72 Riviera Village 0.276 53.13 239 Torrance Old Town 0.225 41.07 178 All center 0.191 47.32 813Corridor neighborhoods Artesia Blvd. 0.087 8.18 526 Gardena Blvd. 0.032 8.85 285 Hawthorne Blvd. 0.036 12.21 278 Pacific Coast Highway 0.143 24.41 223 All corridor 0.073 11.97 1,312All study areas 0.119 25.49 2,125



Joh et al. 9

Table 4. High-Walk and Low-Walk Respondents by Study Area

Study Area High-Walk (n) % High-Walk Low-Walk (n) % Low-Walk

Center neighborhoods El Segundo 189 62.79 112 37.21 Inglewood 24 42.86 32 57.14 Riviera Village 105 51.47 99 48.53 Torrance Old Town 69 44.23 87 55.77 All center 387 53.97 330 46.03Corridor neighborhoods Artesia Blvd. 256 51.10 245 48.90 Gardena Blvd. 166 63.85 94 36.15 Hawthorne Blvd. 172 66.93 85 33.07 Pacific Coast Highway 64 30.92 143 69.08 All corridor 658 53.71 567 46.29All study areas 1045 53.81 897 46.19

center neighborhoods (El Segundo and Riviera Village). On average, the proportion of high-walk residents in center neighborhoods was slightly higher (53.97 percent) than low-walk residents (46.03 percent).

Among the corridor neighborhoods we sampled, high-walk residents made up the majority with the exception of Pacific Coast Highway, where low-walk residents outnum-bered high-walk residents by a 2:1 ratio. Overall, however, the proportion of high-walk residents to low-walk residents in the corridor neighborhoods was nearly identical to the proportion for center neighborhoods (53.71 percent high-walk, 46.29 percent low-walk).

It is notable that despite differences in land use charac-teristics and geometries, the share of high-walk versus low-walk residents is remarkably similar across center and corridor neighborhoods in the South Bay area of Los Angeles County. This suggests that self-selection was not a major factor in determining the residential location for these residents, since we would expect a higher proportion of high-walk residents in more pedestrian oriented mixed-use neighborhoods if residential self-selection was occur-ring. However, the proportion of high-walk to low-walk residents within neighborhood type was similar, suggesting that there were other factors aside from the built environ-ment influencing attitudes.

Descriptive StatisticsDescriptive statistics for sociodemographic, social environment, and built environment variables are presented in Table 5. These include descriptive statistics for all respondents, respondents with high-walk attitudes, and respondents with low-walk attitudes. However, our analysis will focus on interpreting differences between the high-walk and low-walk groups.

Across sociodemographic categories, the proportion of high-walk respondents was higher than low-walk respon-dents except for whites and the elderly (persons greater than 65 years of age). Comparing high-walk and low-walk atti-tudes across racial and ethnic groups, Hispanics reported the highest share of high-walk respondents (77.56 percent), followed by “other race” (69.44 percent), African Americans (61.04 percent), Asians (60.58 percent), and whites (48.16 percent). Among the age and income categories, persons aged 26 to 40 and those earning less than $35,000 a year had the largest proportion of high-walk respondents (60.20 and 56.98 percent, respectively). Other sociodemographic cate-gories (e.g., employment status, households with children, and foreign-born status) also reported a larger share of high-walk versus low-walk respondents. Whites and elderly persons were the only sociodemographic groups that had a greater share of low-walk respondents (51.84 and 63.21 percent, respectively).

For social environment variables, residents with high-walk attitudes tend to live in neighborhoods with slightly higher violent crime rates than low-walk respondents. This suggests that individuals with high-walk attitudes did not live in neighborhoods that were necessarily safer (i.e., lower vio-lent crime rate) and perhaps may be more tolerant of higher crime rates than those with low-walk attitudes. With respect to neighborhood safety attitude, high-walk individuals appeared to show a slightly higher concern for neighborhood crime rates than low-walk individuals.

In terms of the built environment variables, the results show differences for high- and low-walk individuals. Individuals with high-walk attitudes tended to live in neighborhoods with higher concentrations of neighborhood businesses, slightly higher percentage of four-way intersections, and higher intersection density than those with low-walk attitudes. Both residents living in the inner ring closer to the




Table 5. Descriptive Statistics for Sociodemographic, Social Environment, and Built Environment Variables

All Respondents High-Walk Attitudes Low-Walk Attitudes

Variable n Mean SD n Mean SD n Mean SD

All respondents 1942 1.0000 1045 53.81% 897 46.19% Sociodemographic White 1178 0.6566 0.4750 536 48.16% 577 51.84% Hispanic 164 0.0914 0.2883 121 77.56% 35 22.44% Asian 246 0.1371 0.3441 146 60.58% 95 39.42% African American 81 0.0452 0.2077 47 61.04% 30 38.96% Other race 41 0.0229 0.1495 25 69.44% 11 30.56% Female 926 0.5074 0.5001 465 53.94% 397 46.06% Age (years) <26 67 0.0364 0.1873 38 58.46% 27 41.54% 26-40 523 0.2841 0.4511 307 60.20% 203 39.80% 41-65 1019 0.5535 0.4973 522 53.43% 455 46.57% >65 232 0.1260 0.3320 71 36.79% 122 63.21% Income <$35k 191 0.1086 0.3112 98 56.98% 74 43.02% $35k-$75k 599 0.3405 0.4740 305 53.51% 265 46.49% >$75k 969 0.5509 0.4975 510 54.26% 430 45.74% Employed 1628 0.8124 0.3905 897 56.52% 690 43.48% Household with children 561 0.3745 0.4842 320 58.93% 223 41.07% Foreign-born status 249 0.1359 0.3428 158 66.67% 79 33.33% Social environment Violent crime rate 2125 434.24 245.70 1045 453.93 253.06 897 405.53 230 Neighborhood safety attitude 1819 4.15 1.23 936 4.34 1.07 800 3.94 1.35Built environment Neighborhood businesses per acre 2125 0.54 0.74 1045 0.59 0.78 897 0.49 0.69 Four-way intersections 2125 0.49 0.16 1045 0.50 0.16 897 0.48 0.17 Intersection density 2125 153.03 43.38 1045 157.12 42.50 897 149.29 43.12Inner ring 781 0.3675 0.4822 450 62.50% 270 37.50% Center resident 813 0.3826 0.4861 387 53.97% 330 46.03% Walking trips 2125 0.12 0.49 1045 0.15 0.57 897 0.09 0.40Vehicle per licensed driver 1801 1.06 0.42 918 1.06 0.40 796 1.07 0.46

neighborhood center as well as those living in centers had significantly higher proportion of high-walk individuals than low-walk individuals, with roughly two of three inner-ring residents and slightly more than half of center residents hav-ing favorable attitudes toward walking.

In addition to the variables included in our regression mod-els, we also compared individual walking trip rates between high-walk and low-walk individuals. As expected, individuals with high-walk attitudes took more walking trips than individ-uals with low-walk attitudes. However, one could argue that high-walk attitudes could be proxying for low-income status, since low-income status is often correlated with less driving. While we included dummy variables in our models to control for income, we also compared differences in vehicles per licensed driver between high- and low-walk individuals. The results show that vehicle ownership rates between high- and

low-walk individuals were nearly identical, suggesting that high-walk attitude is not proxying for limited car availability.7

Regression ResultsTo determine the relative effects of sociodemographic, social environment, and built environment variables, we analyze the results of six negative binomial regressions: three for high-walk persons and three for low-walk persons. These results are shown in Tables 6 and 7. We first interpret the results for indi-viduals with high-walk attitudes (refer to Models 1 through 3 in Table 6).

For individuals with positive attitudes toward walking, sociodemographic characteristics were generally not a sig-nificant predictor of walking trip rates, with the exception of households with children, which were associated with fewer



Joh et al. 11

walking trips after controlling for social environment and built environment factors. African Americans and Asians also took fewer walking trips, but they were significant at the 5 percent level in Model 1 only. Social environmental vari-ables (violent crime rates and perceptions of neighborhood safety) were significant only in Model 2, and lose statistical significance in Model 3 after built environment variables are added. With respect to built environment factors, the concen-tration of neighborhood businesses was the only variable that was robust, correlated with an increased number of walking trips. In addition, there were no significant differences in walking trip rates between inner-ring and outer-ring residents after controlling for social and built environment factors.

For persons with negative walking attitudes (refer to Models 4 through 6 in Table 7), there were many more factors that predicted individual walking trips. Most of these factors had a negative impact on walking trip rates. In terms of sociodemographic characteristics, females, persons of “other race,” foreign-born, and persons younger than 26 years of

age took fewer walking trips after controlling for social envi-ronment and built environment factors. The only sociodemo-graphic group that was correlated with more walking trips was the age 26-40 group (this is the only variable that had statistically significant positive coefficients). Among social environment factors, violent crime rate was the most robust, having a strongly negative impact on walking trips. However, attitudes toward neighborhood safety, indicating the impor-tance of reducing crime for neighborhood travel, did not appear to have a significant impact on walking. Built envi-ronment factors did not appear to have much of an impact on walking trips for individuals with negative attitudes toward walking, with the exception of intersection density, which was correlated with fewer walking trips. Furthermore, there was no significant difference (at the 5 percent level of significance) for walking trip rates between inner-ring and outer-ring residents.

To summarize the key findings from the regression results, we can observe the following differences between

Table 6. High-Walk Regression Results

Model 1: SociodemographicsModel 2:

Sociodemographics + SEModel 3: Sociodemographics

+ SE + BE

Independent Variables Coefficient Z Statistics Coefficient Z Statistics Coefficient Z Statistics

Sociodemographic Hispanic !0.782 !1.17 !0.286 !0.35 !0.239 !0.29 African American !1.506 !2.03 !1.059 !1.25 !0.996 !1.18 Asian !1.100 !2.91 !0.753 !1.82 !0.799 !1.92 Other race 0.095 0.15 0.154 0.25 0.211 0.36 Female !0.018 !0.05 !0.036 !0.11 !0.049 !0.14 Low income (<$35k) 0.303 0.55 0.571 1.12 0.489 0.97 Moderate income ($35-$75k) !0.609 !1.24 !0.423 !0.90 !0.663 !1.60 Employed 0.316 0.82 0.189 0.41 0.232 0.51 Household with children !0.450 !2.35 !0.394 !2.09 !0.421 !2.22 Age (years) <26 !0.453 !0.42 !0.587 !0.53 !0.423 !0.38 26-40 0.396 0.86 0.450 0.90 0.526 1.04 41-65 0.468 0.82 0.457 0.77 0.528 0.83 Foreign-born status 0.058 0.24 0.042 0.16 0.069 0.27 Inner ring 0.544 1.76 0.546 1.84 0.070 0.21Social environment (SE) Violent crime rate !0.001 !2.06 !0.001 !1.28 Neighborhood safety attitude !0.240 !2.32 !0.193 !1.72Built environment (BE) Businesses per acre 0.386 2.03 Four-way intersections !0.074 !0.06 Intersection density 0.003 0.56Constant !2.485 !4.90 !1.032 !1.59 !1.833 !1.24No. of observations 828 825 825 Pseudo R-squared 0.037 0.053 0.064

Note: Coefficients that are statistically significant at the 5 percent level (two-tailed test) are indicated in bold type; and coefficients that are statistically significant at the 10 per cent level are shown in italics.




individuals with positive and negative attitudes toward walk-ing based on our sample of South Bay Area residents.

1. In terms of built environment effects, having nearby destinations to walk to (i.e., neighborhood busi-nesses) generates increased walking trips among those with positive attitudes but does not appear to have a significant impact for those with neutral or negative attitudes. Street connectivity did not appear to affect walking trip rates for either group with the exception of intersection density for low-walk individuals.

2. In terms of social environment effects, walking trip rates for high-walk individuals were less affected by violent crime rates than low-walk individu-als. Attitudes about neighborhood safety were less robust than the crime rate for the low-walk group.

Therefore, the results suggest that certain social environ-ment and built environment factors have a differential impact on high-walk and low-walk individuals, after controlling for sociodemographic characteristics.8

Sensitivity TestsTo investigate the robustness of our models, a series of sen-sitivity tests were conducted. The primary concern was the possibility of correlation between walking attitude and built environment characteristics. With the former being the dis-criminating variable, it is important that the attitude measure isolate the effect of attitude on walking rather than proxy for neighborhood destinations. Therefore, we modified our regression models by (1) using different thresholds for high-walk and low-walk respondents, (2) an alternative attitudinal index, and (3) controlling for possible correlation between

Table 7. Low-Walk Regression Results

Model 4: Sociodemographics

Model 5: Sociodemographics + SE

Model 6: Sociodemographics

+ SE + BE

Independent Variables Coefficient Z Statistics Coefficient Z Statistics Coefficient Z Statistics

Sociodemographic Hispanic !0.895 !0.86 !0.340 !0.26 !0.441 !0.35 African American !0.334 !0.58 0.990 1.13 0.688 0.79 Asian !0.402 !0.54 0.118 0.16 !0.037 !0.04 Other race –13.339 –19.96 –14.063 –19.64 –24.982 –39.64 Female –0.698 –2.28 –0.588 –1.74 –0.697 –2.34 Low income (<$35k) 0.481 0.61 0.902 1.19 1.123 1.46 Moderate income ($35-$75k) 0.315 0.65 0.478 0.99 0.379 0.77 Employed !0.825 !2.15 !0.452 –1.21 !0.465 –1.16 Household with children 0.475 1.77 0.342 1.68 0.214 0.97 Age (years) <26 !11.870 !21.4 !13.132 !18.98 !23.580 !34.12 26-40 1.653 2.57 1.316 1.94 1.561 2.11 41-65 1.148 1.71 0.722 1.17 1.002 1.61 Foreign-born status –0.770 –2.74 –1.008 –2.73 –1.130 –2.60 Inner ring 0.134 0.27 0.345 0.76 1.007 1.88Social environment (SE) Violent crime rate –0.003 !2.56 !0.003 !3.10 Neighborhood safety attitude 0.026 0.27 0.077 0.85Built environment (BE) Businesses per acre 0.557 1.56 Four-way intersections !0.587 –0.47 Intersection density !0.017 !3.83Constant !2.964 !5.27 !2.347 !2.80 !0.097 !0.09No. of observations 609 605 605 Pseudo R-squared 0.037 0.049 0.067

Note: Coefficients that are statistically significant at the 5 percent level (two-tailed test) are indicated in bold type; and coefficients that are statistically significant at the 10 percent level are shown in italics.



Joh et al. 13

the attitudinal indices and the built environment.9 The signs and significance of the built environment and crime rate vari-ables were, in virtually all cases, the same as those shown in Tables 6 and 7.10 Full results are available on request from the lead author.

We also ran the regressions in Tables 6 and 7 with an additional independent variable that measured the number of vehicles per licensed driver in the respondent’s house-hold, to be certain that the results did not proxy for vehicle availability. The results were largely unchanged when add-ing the “vehicles per licensed driver” variable, and “vehi-cles per licensed driver” was not significant in the regressions. We also performed multicollinearity tests for the built environment and attitudinal variables, and found only weak correlation between the built environment and attitudinal variables (r = 0.02-0.39), with the exception of neighborhood businesses and intersection density, which showed fairly high correlation (r = 0.67).

DiscussionThese results reveal key insights about how the social and built environment impact walking behavior for individuals with positive and negative attitudes toward walking. With respect to social environment factors, violent crime rates appear to have a stronger impact on walking trips than atti-tudes toward neighborhood crime and safety. Individuals with neutral or negative attitudes toward walking were much more deterred by crime rates than those with positive atti-tudes after controlling for built environment measures such as neighborhood destinations or street connectivity. Furthermore, our results show that certain sociodemographic groups within the high-walk and low-walk group were more likely to walk than others. For instance, among high-walk individuals, households with children were less likely to walk than others, while for low-walk individuals, women and “other race” persons reported fewer walking trips. While the negative impact of crime on walking for certain sociodemo-graphic groups (such as whites and females) has been cited in previous studies (Liska, Lawrence, and Sanchiroco 1982; Pain 2001), very few of these studies, if any, differentiate by walking attitudes.

In terms of built environment impacts, the presence of nearby neighborhood businesses had a stronger positive impact on walking trip rates for high-walk individuals than those with low-walk attitudes. This suggests that having nearby destinations to walk to matter more to residents with positive attitudes, and that not all residents equally take advantage of nearby shopping and service opportunities, even if they live in a walkable neighborhood such as Riviera Village. While other studies have shown that providing nearby shopping opportunities and neighborhood businesses gener-ate walking trips (Boarnet et al. 2011; Handy and Clifton 2001), our study illustrates that neighborhood destinations

matter most to those who prefer to walk. On the other hand, traditional street connectivity measures (intersection density and the percentage of four-way intersections) did not have much of an impact on walking after controlling for crime rates and perceptions of neighborhood safety for high-walk persons, while intersection density had a negative impact for low-walk individuals. While this may be anomalous to the South Bay region, it could also indicate pedestrian safety concerns and the shortage of mid-block and signalized pedestrian crossings in the area. However, a more detailed inventory of built environment features would be needed to gain such insights, which could be conducted in a future study using an audit tool such as the Irvine-Minnesota Inventory (Day et al. 2006).

Based on these results, we can infer that while the built environment and social environment matter for walking, atti-tudinal bias affects walking trips. Planning scholars and prac-titioners have largely focused on urban design strategies to promote walking (i.e., how to create or retrofit neighborhoods to make them more pedestrian friendly) while strategies to promote a more positive walking attitude have received far less attention, primarily because of the complexity of attitu-dinal factors in shaping walking behavior. While a detailed analysis on the explanatory factors that account for differ-ences in walking attitude is beyond the scope of this paper, we offer the following explanations. First, individuals with positive attitudes toward walking may have been shaped from previous experiences of living in a pedestrian oriented set-ting, may have acquired a habit of walking from an early age (e.g., from walking to school), or were influenced by family members and friends to adopt a more active lifestyle. Cultural and societal factors may also play a role in influenc-ing walking attitude, illustrated by the prevalence of walking and bicycling in many European countries, and supported by the common perception that Europeans tend to be more “green conscious” and more physically active than their American counterparts (Bassett et al. 2008; Pucher and Dijkstra 2003). Second, individuals with neutral or negative attitudes toward walking may not have walked much as a child, had unpleasant walking experiences in the past due to poor social and built environments, or prefer a more seden-tary lifestyle. While it is unclear from our data what factors were most influential for shaping positive and negative atti-tudes for walking, we can observe that these attitudinal biases have a notable impact on how residents viewed their social and built environment with respect to walking.

ConclusionPlanning for sustainable and healthy communities has arguably become the dominant paradigm for urban and community planners in the twenty-first century, with the promotion of walkable communities and walking behavior as its primary thrusts. However, planning scholars and practitioners have




largely focused on urban design–oriented solutions to pro-mote walking, such as improving the streetscape and promot-ing mixed use. The result of these efforts has usually been a modest increase in walking trips, while not necessarily reduc-ing automobile trips (Crane 2000; Ewing and Cervero 2001, 2010; Joh et al. 2008). While pursuing these policies is gener-ally a desirable goal, they may not lead to a positive impact on walking trip rates for individuals who have negative atti-tudes toward walking. Therefore, we should pursue a multi-pronged approach of improving the built and social environment, as well as promoting positive walking attitudes.

Based on the evidence from our study, we can see that the built environment matters more for those with positive attitudes. Individuals who enjoy walking will walk even more if there are interesting places to walk to. In contrast, the built environment does not appear to have much of a positive impact on those with negative attitudes. For those with nega-tive attitudes, the focus should be on changing attitudes to emphasize the benefits of physical activity and to encourage walking. While this is a certainly a challenging task, it should be an equally important policy goal for urban planners, as investments in “retrofitting” neighborhoods to be more pedestrian oriented are likely to yield greater dividends in terms of increased walking trips as individuals adopt more positive attitudes. Hence, the built environment will have a greater impact on walking as we see a shift in individual walking attitudes.

However, what about the role of self-selection? If planners and policy makers are successful in changing people’s atti-tudes toward walking, would they choose to relocate to a neighborhood that more closely matches their preferences for walking? Some may, perhaps, but we believe that the major-ity will not. Based on our results, there is little evidence to indicate that self-selection based on walking preferences is occurring in the South Bay area, as the split between high- and low-walk respondents is closely balanced between center and corridor neighborhoods, and nearly two-thirds (65.0 percent) of the residents from our sample survey have lived in their current residence for at least 6 years. This is especially the case in California because of its unique property tax structure (Proposition 13) that discourages residents from selling their homes to relocate. Another reason supporting the notion that most existing residents choose not to relocate is because neighborhoods with good pedestrian environments typically command a premium in terms of home prices and rents, mak-ing such places unaffordable to a large segment of the market. While this may be due to many of these communities being located in expensive housing markets such as San Francisco and Washington, D.C., it could also be reflective of the lack of supply of such neighborhoods, due to zoning regulations that limit compact development (Levine 2005).

While our study clearly shows differences in walking behav-ior based on attitudinal disposition, the question of what factors shape attitudes about walking is an important one

that merits further research. Can attitudes be shaped simply through increased education about walking? Or are attitudes about walking primarily shaped through past experiences related to walking? While various attitudinal theories have been proposed by scholars in an attempt to explain travel behavior, our understanding of the role of attitudes on travel, especially with respect to walking, remains limited. Therefore, the direction of future research should focus on measuring how individual attitudes are shaped—for instance, what fac-tors lead to one adopting a positive or negative walking atti-tude—as well as how these attitudes translate into actual travel behavior.

AcknowledgmentsThe authors thank Wally Siembab and Bill Fulton for their help in administering the research that led to data collection and for their helpful comments. We thank Jacki Bacharach, the execu-tive director of the South Bay Cities Council of Governments, for her role in administering the SBCCOG’s funding of the research and for her helpful comments. We also thank Aaron Engstrom and Rocco Pendola for their research assistance. Boarnet’s contribution to this research was completed while he was a professor in the Department of Planning, Policy, and Design at the University of California, Irvine.

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the South Bay Cities Council of Governments (SBCCOG) and the Southern California Association of Governments (SCAG). These two organizations provided financial support for the data collection of the travel survey used in the research. The authors alone are responsible for the analyses and interpretations in this paper, and the funding provided by the SBCCOG and SCAG does not necessarily imply endorsement by those organizations of any interpretations or policy statements made in this paper.

Notes 1. New Urbanist theory also posits a reduction in automobile

use, but the empirical findings to support this are quite mixed depending on how automobile use is operationalized. For a complete review of studies that examine the relationship between the built environment and automobile travel, see Saelens and Handy (2008). It is beyond the scope of this study to address the empirical findings of these studies.

2. The “center” neighborhoods include El Segundo, Inglewood, Riviera Village, and Torrance Old Town. The “corridor” neigh-borhoods include Artesia, Gardena, Hawthorne, and Pacific Coast Highway (PCH). All study area names represent the city where they are located, with the exception of Riviera Village



Joh et al. 15

(located primarily in Redondo Beach) and PCH (located in Torrance). All center neighborhoods (with the exception of Riviera Village) are centered on the downtown/civic center district of their respective cities, while corridor neighborhoods are centered on a commercial strip along a major arterial highway.

3. The South Bay Travel Survey was funded by the South Bay Cities Council of Governments and the Southern California Association of Governments, and was conducted in three phases, annually, from 2005 to 2007. Study areas were added in phases due to limited year-to-year funding. Phase I included Riviera Village, Torrance Old Town, Inglewood, and Pacific Coast Highway. Phase II surveyed residents in El Segundo and Artesia, and Phase III included Gardena and Hawthorne. The survey was primarily web-based and response rates varied from 3.8 percent in Hawthorne to 11.9 percent in El Segundo.

4. This response was based on the following questions on the South Bay Travel Survey:

1. “Thinking about your neighborhood as a good place to live, how important is it to you that you can walk to nearby stores and restaurants?”

2. “Thinking about your neighborhood as a good place to live, how important is it to you that you can walk to work?”

3. “Thinking about your neighborhood as a good place to live, how important is it to you that your neighborhood has a vibrant street life?”

5. Age was stratified into four groups that correspond to the fol-lowing age segments: youth (25 or younger), young adults (26-40), middle-aged adults (41-65), and the elderly (older than 65). Income was stratified into three groups to represent low (less than $35,000), moderate ($35,000-$75,000), and high income (greater than $75,000). Note that the average household income for the South Bay neighborhoods is approximately $75,000.

6. This response was based on the following question on the South Bay Travel Survey: “How important would REDUCING CRIME in your neighborhood be in your decision to travel more often in the center/corridor?”

7. We added vehicle ownership (vehicles per licensed driver) in our regression models as a robustness test, and found no sig-nificant difference in the results. Therefore, we did not include it in our final models. In addition, we were concerned about correlation between the income and auto ownership variables.

8. We also tested for differences in the coefficients of key vari-ables from the high- and low-walk models. We approached this by testing for interaction effects for two key SE and BE variables: crime rate and neighborhood business density. This was conducted by pooling the entire sample and interacting crime and business density with a high-walk dummy variable, where 1 = high-walk and 0 = low-walk. We regressed walking trips on all sociodemographic, SE, and BE variables and added two interaction variables for crime and business density using OLS regression. The regression results showed a positive and statistically significant coefficient (t statistic = 2.50) for the

interacted neighborhood businesses variable, suggesting that persons with high-walk attitudes generally have walking behaviors that are more responsive to the presence of nearby destinations. The interaction term for crime, however, was not statistically significant (t statistic = 0.04), indicating that differ-ences in the coefficients for high- and low-walk with respect to crime may be due to sampling error.

9. On the latter point, we constructed a measure of attitudes that is orthogonal to the most important built environment variable. Note that one of the questions in the attitude index asked respondents how important it was to be able to “walk to nearby stores and restaurants.” To ensure that the attitude variable did not reflect, e.g., how many places one could walk to, we regressed the attitude measure on the number of neigh-borhood businesses and then took the residual, which by con-struction is correlated with attitudes but orthogonal to the number of neighborhood businesses—in other words, the residual is the attitude variable purged of any correlation with neighborhood businesses. We then stratified the sample into high- and low-walk based on the residual from that first-stage regression. The basic form of this test involved running a first-stage regression of the form

Attitude index = a + b (neighborhood businesses per acre) + u

and then stratifying the sample into high- and low-walk attitudes based on the residual from that first-stage regression.

10. These three sensitivity tests gave eight different alternative tests that mirrored the regressions reported in Tables 6 and 7. In all of the sensitivity tests, violent crime was negatively associ-ated with walking among persons with low-walk attitudes, and neighborhood businesses per acre was positively associated with walking among persons with high-walk attitudes. In addi-tion, in only one instance was neighborhood business density positively associated with walking among persons with low-walk attitudes and in two instances neighborhood businesses were negatively associated with walking among persons with low-walk attitudes. These disagreements with the results in Tables 6 and 7, in three of thirty-two variables, are similar to what would occur by chance if the null hypothesis of no dis-agreement with the results in Tables 6 and 7 is true. On net, we find the results in Tables 6 and 7 to be very robust to changes in the walk index, cut points, and our test that purged the walk index of correlation with the number of neighborhood businesses.

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Bios

Kenneth Joh is an assistant professor in the Department of Landscape Architecture and Urban Planning at Texas A&M University. His research interests include sustainable transportation and the built environment, pedestrian and cycling behavior and safety, and accessibility and equity issues in transportation.

Mai T. Nguyen is an assistant professor in the Department of City & Regional Planning at the University of North Carolina at Chapel Hill. Her research interests include contemporary immigration policy, affordable housing policy, the mobility of Section 8 voucher recipients, residential preferences and segregation, growth manage-ment, urban sprawl, and the relationship between urban design and mobility.

Marlon G. Boarnet is a professor of Policy, Planning, and Development at the University of Southern California. His research interests include travel behavior interactions, urban growth patterns, infra-structure investment, and economic development.



Retrofitting the Suburbs to Increase Walking: Evidence from a Land-use-Travel Study

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