Environment and Behavior

Environment and Behavior http://eab.sagepub.com/

Design and Pedestrianism in a Smart Growth Development Julie Brand Zook, Yi Lu, Karen Glanz and Craig Zimring Environment and Behavior 2012 44: 216 originally published online 7 March 2011 DOI: 10.1177/0013916511402060

The online version of this article can be found at: http://eab.sagepub.com/content/44/2/216

Published by:

http://www.sagepublications.com

On behalf of: Environmental Design Research Association

Additional services and information for Environment and Behavior can be found at:

Email Alerts: http://eab.sagepub.com/cgi/alerts

Subscriptions: http://eab.sagepub.com/subscriptions

Reprints: http://www.sagepub.com/journalsReprints.nav

Permissions: http://www.sagepub.com/journalsPermissions.nav

Citations: http://eab.sagepub.com/content/44/2/216.refs.html

>> Version of Record - Feb 29, 2012

OnlineFirst Version of Record - Mar 7, 2011

What is This?

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Article

Environment and Behavior 44(2) 216–234 Design and Pedestrianism © The Author(s) 2012 Reprints and permission: in a Smart Growth sagepub.com/journalsPermissions.nav DOI: 10.1177/0013916511402060 Development http://eab.sagepub.com

Julie Brand Zook1, Yi Lu1, Karen Glanz2, and Craig Zimring1

Abstract Research on urban walkability does not always make a clear distinction between design features supporting walkability and those leading to a sense of urban liveliness. Walkability, for this article’s purposes, entails the oppor- tunity for continuous movement across some distance and therefore engages both the local and global street networks. Urban liveliness, by contrast, may exist in isolated pockets that provide limited support for physical activity. This case study of a large, urban smart growth development in Atlanta, Georgia, provides an example of a new development with characteristics that suggest a high degree of walkability. However, observational data show pedestrians are clumped on relatively few street segments rather than distributed throughout the site, indicating it is unlikely that the site is hosting much walking between the development and its surrounds. This descriptive case study is intended to contribute to more explicit theory of how environmental design contributes to walking.

Keywords urban design, walking, smart growth, public health, new urbanism

The impact of environmental design on physical activity is a heavily studied area across a variety of disciplines, including environmental psychology, public health, and urban and regional planning. Such intense research activity is well

1Georgia Institute of Technology, Atlanta 2University of Pennsylvania, Philadelphia Corresponding Author: Julie Brand Zook, Georgia Institute of Technology, 247 Fourth Street, Atlanta, GA 30332, USA Email: [email protected]

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 217 justified by the ongoing obesity epidemic, with an alarming two thirds of American adults being overweight or obese (Trust for America’s Health; Levi, Vinter, St. Laurent, & Segal, 2010). Researchers focused on the obesity epidemic note that human physiology has not changed: human environments have, especially in terms of increased food availability and the promotion of physical inactivity (Hill & Peters, 1998). The federal government-issued “Physical Activity Guidelines for Americans” suggest that for adults a minimum of 2.5 hr of moderate physical activity (e.g., walking) per week, which is equivalent to 30 min a day, 5 days a week (e.g., roughly 2 miles of brisk walking 5 days a week; Physical Activity Guidelines Advisory Committee, 2008). Slightly less than one third of Americans report that they achieve the recom- mended levels, whereas a little more than one third reports notching virtually no physical activity at all (CDC, 2005). The physical environment is widely understood as having the capacity to support active living and, inversely, to promote inactivity (Sallis, 2009). This article is focused on opportunities for physical activity, namely, walking, and how such opportunities are realized in a smart growth development in Atlanta, Georgia. This descriptive case study is intended to contribute to more explicit theory of how site design contributes to walking. Researchers in environmental psychology and public health have introduced a number of concepts and metrics to the study of walking and physical activity, including a number of audit tools that evaluate path segment attributes. For these, path segments are typically scored for numerous building and streetscape features, such as sidewalk size and condition, building setbacks, nearby land use, and the presence of various amenities and attributes from lighting to benches to curb cuts to bad smells (e.g., Day, Boarnet, Alfonso, & Forsyth, 2006; Clifton, Livi Smith, & Rodriguez, 2007; Pikora et al., 2002). The many variables assessed by these tools are often aggregated into higher-order neighborhood dimensions, such as safety and aesthetics, or are used to characterize general entities, such as sidewalks or land use. Many of these tools have been tested for reliability, especially interrater reliability. However, to the authors’ knowledge, only the Irvine-Minnesota Inventory has undergone a validation study for relationships to actual exercise behavior (Boarnet, Forsyth, Day, & Oakes, 2011). Planning research has emphasized fewer variables and was originally more interested in walking as an alternative to vehicle travel than as a direct source of health benefits (Sallis, 2009). Nonetheless, an increasing number of studies directly address walking. The literature has become numerous enough to support a recent meta-analysis by Ewing and Cervero (2010). This meta-analysis provided evidence of links between the propensity to walk and intersection

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 218 Environment and Behavior 44(2) density and jobs-housing balance, among other variables. The meta-analysis was undertaken within a framework of “Ds,” the first three of which were introduced in 1997 (Cervero & Kockelman, 1997) and were subsequently added to as travel behavior became the most studied area in planning research (Ewing, Bartholomew, Winkelman, Walters, & Chen, 2008). The Ds are density, [land use] diversity, design (including both street connectivity and microscale features), destination accessibility, distance to transit, and demand management (i.e., parking supply and cost of parking; Ewing & Cervero, 2010). The Ds show demonstrated associations with travel behavior, albeit with periodic reshuffling of which variables, which operationalizations of variables, or which combinations of variables most account for which aspects of travel behavior. Such an approach is exemplary in methodologic rigor but tends not to provide detailed accounts of the decision to walk and decisions regarding which path to follow. The focus of this case study is design, the third D among the planning variables, which encompasses both path attributes and street network attributes. In spite of the research activity to date, one is still hard-pressed to find clear, evidence-based rules-of-thumb for urban and site designs that promote walking sufficient to accrue health benefits. One reason for this is that design features are difficult to measure and difficult to prioritize and have been defined by a range of attributes and objects (Ewing et al., 2008), from street network connectivity to cracks in the sidewalk. A second reason is that the physical activity impacts of microscale design features and larger-scale urban attributes are not always distinguishable (Ewing et al., 2008). We also describe an additional issue contributing to the lack of clarity about how urban and site design can support walking: the generally poor distinction between environments pos- sessed of urban liveliness at a local level and those offering support for continuous movement over a distance. Relatively few studies have addressed themselves to the description and measurement of local-to-global relationships, though such relationships con- stitute the potential to walk a substantial distance and duration. For example, if the concern is overall intelligibility of the path network, then path segment attribute tools do little to clarify overall path choice because they assess the walkability potential of path segments as discrete entities. Pedestrians do not usually choose routes by traversing a path segment, at the end of which they choose the next path segment based on the amenities that appear on and along- side it. If the concern is walking a distance, then path segment attribute tools are not a sufficient metric as currently constituted. Certain planning measures indirectly capture aspects of local-to-global relationships. Some of these have been associated with increased walking rates, including distances to key land uses or transit (Moudon et al., 2006),

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 219 measures of route directness (the ratio of actual distances to as-the-crow flies distances; Hess, Moudon, Snyder, & Stanilov, 1999), and other measures of connectivity, such as block sizes or intersection density (Frank, Schmid, Sallis, Chapman, & Saelens, 2005; Moudon et al., 2006). Space syntax research methodologies address themselves directly to local- to-global relationships (Hillier & Hanson, 1984) and have been used to examine the likelihood of street segment use as a function of its position in the larger street network (Hillier, Penn, Hanson, Grajewski, & Xu, 1993; Peponis, Hadjinikolaou, Livieratos, & Fatouros, 1989; Peponis, Ross, & Rashid, 1997; Lund, Wilson, & Cervero, 2006). Space syntax is a research program that describes relationships between society and space and is grounded in a general theory of the structure of space (Bafna, 2003). A preliminary syntax analysis of the Atlanta development is provided in the next section. We describe walking behavior in an urban smart growth development for the purpose of better understanding how site design supports or fails to support walking beyond the immediate, local environment. This case study is focused on conditions encountered by employees working at the development in both office and retail settings, though the data was collected on all users of the out- door space. Pedestrian counts are used to describe patterns of walking within the primary mixed-use area inside the development and between the primary mixed-use area and the surrounding areas. We will also test for within-site variability in pedestrian counts by comparing key pedestrian access points and relating pedestrian presence to key land uses that may attract pedestrians. Two related, forthcoming studies will report actual walking distance and duration information for participants before and after moving to residences and workplaces at Atlantic Station.1 This case study situates and provides a first pass at questions about design and walkability.

Atlantic Station Smart growth is an umbrella term for development schemes that attempt to reconcile the tension between economic growth and environmental sustain- ability. The term smart growth is currently used by organizations with divergent agendas, spanning from the coalition Smart Growth America (supported by the Congress for New Urbanism, among others), to the Sierra Club, to the National Association for Home Builders, to the Environmental Protection Agency (EPA; Ye, Mandpe, & Meyer, 2005). When Ye and colleagues (2005) analyzed documents on smart growth issued by 10 national organizations, they found six consistent elements: planning, transportation options, economic development, housing policy, community development, and natural resource

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 220 Environment and Behavior 44(2) preservation, each of which they further decomposed into constituent parts. The authors noted that the implementation of multiple interventions was a defining characteristic of smart growth. Atlantic Station identifies itself as a smart growth, sustainable development based on Brownfield reclamation, sustainable building features, and the claim that the site elicits increased walking and transit use and decreased car use (retrieved September 11, 2008, from http://www.atlanticstation.com/faq. php#Anchor-How-47383). An additional smart growth attribute is that it was developed in an urban area, so the potential to walk extends beyond the bound- aries of the development. The Midtown location, near the center of the city, was one of four sites considered. The other sites were located in a perimeter suburb, an outlying suburb, and an ex-urban area. Atlantic Station was developed under unique regulatory circumstances. As of 1999, the Atlanta area was out of compliance with federal transportation conformity requirements, which stipulated that area transportation planning reduce air pollution to acceptable levels. Being out of compliance curtailed access to funds to construct a bridge needed to span the I-75/85 Interstate and connect the proposed development to the rest of Midtown. The Atlantic Station developers sought EPA project XL status, wherein restrictions are waived if the waiver will result in an environmental improvement. As a consequence, both the site selection and the site plan came under EPA scrutiny. The final design was a compromise between the developer’s initial plan and a plan proposed in a consultation with new urbanism firm Duany Plater-Zyberk & Company (Ewing et al., 2008). The EPA approved the project after enhanced travel demand modeling indicated selection of the Midtown site would generate vehicle miles and related pollution 34% below the most remote site under consideration, with an additional 2% reduction for the redesigned site plan (Cervero, 2006). Sub- sequent surveys conducted by the developer in October 2008 found that employed residents of Atlantic Station traveled an average of about 14 vehicle miles per day and employees working at Atlantic Station traveled about 12 vehicle miles a day. This compares favorably with statistics from the 20-county area for employed residents (about 19 miles a day) and for all residents (about 34 miles per day; retrieved May 18, 2009, from http://www.atlanticstation. com/concept_green_ projectXL08.php). However, other Atlanta residents who live or work in relatively central and compact Midtown are likely to travel similar vehicle miles per day to Atlantic Station residents and workers. Put simply, any central location is likely to reduce vehicle miles traveled. Further- more, these data tell us little about pedestrianism, as reduced vehicle miles traveled do not necessarily translate into increased walking.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 221

Before describing patterns of walking at Atlantic Station, a description of the physical environment is in order (Figure 1). Atlantic Station is a 138-acre mixed-use, urban development on a remediated steel mill site that includes retail, office space, and housing distributed across three zones that run west- east. The eastern-most of the three is the primary mixed-use area, where pedestrian distribution observations were made. The primary mixed-use area sites above a 7,000-space parking deck and is laid out on relatively small blocks. It includes office and residential towers and mixed-use, medium-rise buildings that house shopping and eating franchises as well as a movie theater, gym, and residences. In addition to retail outlets typical to shopping malls, this area also contains convenience retail (e.g., large grocery store, big-box discount department store with a pharmacy). There is also a central green space. Immediately west of the mixed-use area is a zone of medium-rise multifamily residences bifurcated around a constructed lake. The western-most zone contains both multifamily housing and a big-box discount Scandinavian-style furniture store. Parking supply is ample at Atlantic Station. The primary mixed-use area is built on top of a parking deck that also serves to cap contaminated soil. Sixteen stair towers connect the parking deck to the surface of the primary mixed-use area, with additional stairs leading directly into office towers. Visitors may park for free for 2 hr, with additional free parking available to moviegoers. The furniture and department store each provide unlimited free parking. Atlantic Station does not appear well connected to the surrounding street network. The street grid of the development can be described as nested zones of blocks; the smallest blocks lie at the center of the mixed-use area with larger blocks at the perimeter of the development as well as throughout the more residential areas. The blocks falling within a quarter mile of the primary mixed-use area are smaller (just more than three acres) than those falling between a quarter and half mile (about 5⅓ acres), or between a half mile and a mile (just more than 7 acres). Intuitively, it would appear that increased block sizes at the neighborhood edge create a buffer that discourages walking between the site and adjacent areas. In two directions, barriers between Atlantic Station and the surrounding neighborhoods were preexisting and irremovable: the old steel mill and railroad tracks to the north and an interstate highway to the east. To the south and west of the site, the Atlantic Station street grid and existing street network are offset, resulting in poor route directness for potential pedestrians. Furthermore, inhospitable pedestrian conditions, such as a long, raised overpass at the west end of the site and limited pedestrian crossings on a wide, busy road to the south, are entirely a function of site design. A space syntax analysis of the area surrounding the development does not indicate easy local-to-global connections. To briefly introduce space syntax,

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 222 Environment and Behavior 44(2)

Figure 1. The Atlantic Station development and surrounding areas

units of analysis in space syntax are typically perceptual “chunks” that are available to building or street users from anywhere within the chunk without requiring users to make turns. For urban areas, the unit of analysis is typically the axial line. Axial maps are produced by drawing the fewest, longest straight lines that cover all streets segments in a network, according to conventions that ensure a reasonable degree of consistency between maps produced by different analysts. Axial lines are often angled (and therefore do not look like street centerlines or path segments) because they capture the maximum perceptual potential afforded to users rather than focusing on street characteristics in and of themselves. The axial map (Figure 2) is coded to show integration values. Integration is a space syntax measure that mathematically assesses the relative depth of lines in a system from one another. Higher integration values (shown by the darker lines) indicate lower depth and greater accessibility of street segments on a line, whereas lower integration values (lighter lines) indicate higher depth and greater segregation of street segments along a line. Integration values have correlated well with the distribution of users in buildings and street networks in previous research (Hillier et al., 1993; Peponis et al., 1989; Peponis et al., 1997). An integration analysis of the axial map for Atlantic Station shows the primary mixed-use area is not strongly integrated into the surrounding street

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 223

Figure 2. Integration analysis of area streets Note: Darker lines represent better-integrated portions of streets relative to the larger street network. Lighter lines represent more segregated portions of streets. network. The two best-integrated lines of the primary retail area are both edge streets that do not extend into the center. Of these two, the north-south running street hosts primary the backs and sides of buildings. The east-west running axial line is part of a street spans the interstate and reaches to the other edge of the site, however this street has with numerous direction changes that may hamper easy integration between the site and context. Highly local path (or microscale) attributes, however, appear to be favor- able to pedestrians at Atlantic Station, especially in the mixed-use area (Figure 3). A brief site evaluation in the primary mixed-use area using a limited number of criteria revealed that sidewalks are uniformly in good condition, with lighting, trees, street furniture, and parked cars buffering pedestrians from traffic, and curb cuts, signage, and crosswalk markings at every intersection. Many street segments host mixed land-use. Most streets are narrow with little traffic, with the exception of the streets at the south and west edge of the primary retail area, which are wider and host apparently faster traffic. Given our initial description and site analysis, we expect to find an active pedestrian presence in the center of the primary mixed-use area with relatively little pedestrian presence at the periphery.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 224 Environment and Behavior 44(2)

Figure 3. Pedestrian environment in the primary mixed-use area

Method Two types of data collection were undertaken. The first describes pedestrian distribution within the primary mixed-use area of the development. The second is focused on the movement of pedestrians between the primary retail area and the surrounding areas, both within and outside the property of the development. Using these data, we hope to provide a preliminary description of local movement (i.e., within the primary retail area) and local-to-global movement (i.e., between the primary retail area and surrounding areas). The research method was approved by institutional review boards at Emory University and the Georgia Institute of Technology.

Pedestrian Movement To and From the Site The observational technique of gate counting was used for gathering data on movement across thresholds established in and around the development. In gate counting, a number of thresholds (i.e., gates) are selected and, for a speci- fied time interval, individual researchers count the number of pedestrians who cross them, noting the direction of travel.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 225

Figure 4. Locations of gates for pedestrian movement counts

Five gates were selected (Figure 4). The number of gates was purposefully sampled to capture access to the primary mixed-use area from the three possible directions and by two types of paths connecting to the neighborhood to the south, straight paths and paths requiring turns. A final gate was established at the top of a central stair tower leading from the parking ramp into the primary retail area of Atlantic Station. This gate was included to provide a rough sense of comparison between pedestrian and car access to Atlantic Station. Four of the gates were along roads and represented potential approach routes to the development: the bridge spanning the interstate (No. 4), one of two paths connecting the residential zone to the primary retail zone within Atlantic Station (No. 1), and two neighborhood streets to the south of Atlantic Station. One of the neighborhood streets (No. 2) is the only one to maintain a straight-line connection into the site. The other neighborhood street (No. 3) requires two turns to access the primary mixed-use area and is a more typical connection between the primary retail area and neighborhood. All gates were along roads with continuous sidewalks, which ranged in condition from new,

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 226 Environment and Behavior 44(2) smooth, and wide to older and cracked, but without obstructions and set back three to eight feet from the street. Each gate was observed for 30 min on each of 18 sessions; observation sessions were divided evenly between morning (8:30-9:00), noon (12:30- 1:00), and midafternoon (3:00-3:30) sessions. The morning and noon sessions were hypothesized to be times when employees at Atlantic Station are likely to be out on the streets and sidewalks, arriving at work or having lunch. The third session was chosen as a point of contrast, to get a sense of the distribution of nonemployee site users, such as residents and shoppers. Because the focus of the study was employees, observations were not made on the weekends. All observations were made in the fall of 2008, on days when temperatures reached or exceeded 50°F. Although some observation sessions were distinctly cool, the weather was not so extreme as to preclude the conduct of activities of daily living. No observations were made when it was raining.

Pedestrian Distribution Within the Primary Mixed-Use Area Behavior mapping is an observational technique used to record the activities of an individual or a group occupying a space. It is a well-validated, nonintrusive approach to objective data collection on physical activity in real settings (Ittelson, Rivlin, & Proshansky, 1970; Marcus, 1990). The participants’ activities are coded as a set of symbols and recorded on a map of the building or site. This set of symbols can represent different types of motion and interaction between participants, including walking, standing still, sitting, and talking. Behavior mapping was implemented as follows. A route was established through the primary mixed-use area of Atlantic Station. The path segments constituting the route were also purposefully sampled to include segments passing by land uses with demonstrated associations with walking in existing research literature (e.g., grocery store, pharmacy; McCormack, Giles-Corti, & Bulsara, 2008; Moudon et al., 2006). It was also planned to represent all types of pedestrian paths present within the site, including those along both wide and narrow roads, paths adjacent to various types of land uses (including unbuilt areas), and pedestrian-only paths (between buildings but not along a vehicle right-of-way). This route yielded 22 path segments, each of which began and ended in at an intersection. The route formed a circuit, which was walked by one individual out of a set of trained researchers, who recorded the location, identity, and activities of site users as they passed by the walking researcher. Interrater reliability

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 227 testing was not conducted. Identities were recorded using one symbol for construction workers and a different symbol for all other users. Parts of Atlantic Station remained under construction, so construction workers constituted a sizable but temporary pedestrian presence on the site. A second layer of sym- bolic notation indicated whether each user was sitting, standing, or walking; the facing direction of standing users and the movement direction of walking users were further recorded. The route measured slightly longer than one mile and took from 20 to 40 min to walk. The variation in time reflected the density of pedestrians to be recorded. However, it is possible that disparities in duration worked the other way—that longer sessions lent themselves to recording greater numbers of pedestrians. For each session, the route was walked twice, generating two maps. Behavior mapping was conducted during windows of time in the morning (7:30-9:30), at noon (11:00-1:00), and during the early afternoon (2:00-4:00) under the same weather conditions as the gate counts. Data on construction workers were excluded from subsequent analysis to focus on more typical site use, and data on seated users were excluded because of the focus on physical activity. For the purpose of analysis, standing users were combined with walking users because users tended to move fluidly between walking and standing. Window shoppers, for example, were frequently recorded walking, standing, and walking again in a fluid sequence. The distinction from pedestrians was clearer for seated users, who were often consuming food or engaged in conversation and did not seem apt to suddenly commence walking.

Results For the nine half-hour observations for each of the five gate locations, a one- way analysis of variance (ANOVA) was used to test for differences in pedestrian counts among five gates. Pedestrian counts differed significantly across the five gate locations, F(4, 40) = 16.924, p < .001. Tukey post hoc comparisons of the five gates indicate that the pedestrian count at the stair gate (M = 41.7, SD = 23.6) was significantly higher than that at the other four locations: bridge (M = 11.7, SD = 4.5), p < .001; Atlantic Station residential (M = 9.11, SD = 4.6), p < .001; path from neighborhood requiring turns (M = 3.8, SD = 1.7), p < .001; and straight-line path from neighborhood (M = 6.8, SD = 4.9), p < .001. Comparisons among the four nonstair gates were not statistically significant at p < .05 level. The behavior mapping data focus on patterns of movement within the primary mixed-use area. Visual inspection of the behavior mapping data revealed that the most heavily-used segments (greater than 100 pedestrians

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 228 Environment and Behavior 44(2) total) were adjacent to retail land use. Linear regression analysis was conducted to identify the relationship between number of pedestrians on a segment and various physical characteristics of the segment. If pedestrians were evenly distributed between paths, then longer paths would have more pedestrians than shorter paths. Therefore, the number of pedestrians was normalized by the length of the path segment. In other words, the dependent variable was the ratio between number of pedestrians and the length of a path segment. A correlation analysis was conducted with the number of sides having shop entrances on the path segment (0, 1 side, or 2 sides) and normalized number of pedestrians. A positive relationship was observed between them, r(19) = .801, p < .001. A separate correlation was then conducted with the number of shop entrances on path segments and normalized number of pedestrians, with the result also indicating a positive relationship, r(19) = .745, p < .001. Because the land use variable is highly collinear with the form variable (r = .871), a hierarchical linear regression analysis was conducted. When the number of sides with retail was entered first and the number of shops was entered second, the number of shops failed to gain significance. This analysis indicates that the number of shops, as a predictor, does not account for our dependent variable after controlling for the number of sides with retail. However, when the number of shops was entered first and the number of sides was entered second, the number of sides retains significance, with acceptable tolerance for col- linearity (.298), indicating that the number of sides with retail as a predictor accounts for the dependent variable after controlling for the number of shops on that segment (Figures 5a and 5b). The gate count statistics indicate that pedestrian access between the primary mixed-use and surrounding areas may be low. The degree to which this ten- dency should be attributed to street network and pedestrian path design versus parking supply is beyond the scope of this case study. However, we can make comparisons to similar data collected in another mixed-use neighborhood in Atlanta (Özbil, 2007; Özbil & Peponis, 2007). Virginia Highlands is an urban, turn-of-the-century, pedestrian-friendly neighborhood. North Highland Avenue, a main street, hosts a number of shops, bars and restaurants in what is otherwise a single and multifamily residential area. When gate counts were taken along 20 segments of North Highland Avenue, the average number of pedestrians per segment ranged from 12 per half hour to 177 per half hour. Along segments on a perpendicular street, counts averaged 42 pedestrians per half hour. Finally, segments along a street two turns removed from the main avenue averaged 12 pedestrians per half hour. These counts, ranging from 12 to 177 pedestrians per half hour, are considerably higher than Atlantic Station counts, averages of which range from about 3.8 to 11.7 per half hour. This indicates that

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 229

Figure 5a. Total pedestrians counted along path segments pedestrian access between the Atlantic Station and its surrounds can reasonably be characterized as low. In terms of the pedestrian distribution in the primary mixed-use area, pedestrians were not uniformly distributed across segments, even after adjusting for segment length, but were concentrated where there is retail on both sides of the street. They also concentrated near retail generally, but not in as strong a pattern. Retail on both sides of the street does not necessarily indicate that a purely formal variable, such as spatial enclosure, is at work. On the Atlantic Station site, a side of the street without retail may still be built up with nonretail uses, such as office and/or residential. Alternately a nonretail side may be open space or it may house a nuisance, such as a construction site.

Discussion This case study indicates that (a) few pedestrians accessed the Atlantic Station primary mixed-use area on foot; (b) walking within the primary mixed-use area was concentrated in a subset of street segments; and (c) pedestrian activity was greater in street segments with retail land use on both sides of the street.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 230 Environment and Behavior 44(2)

Figure 5b. Number of sides of street hosting retail per street segment

It can reasonably be inferred that the pattern of use at the primary mixed- use area of Atlantic Station is similar to that at a traditional, enclosed shopping mall, with the great majority of users approaching by car, browsing stores, spending time in common areas, and then departing by car. The advantage Atlantic Station has is that the common areas are outdoors and they evince more urban liveliness than shopping malls. These areas cannot be said to be public, as all of the common areas—streets, parks, alleys, and sidewalks—are private property, yet they are less emphatically private than the interior spaces of an enclosed shopping mall. Although the possibility exists to walk from the development to areas beyond it, site users do not appear to be realizing this potential. Pedestrians are concentrated on relatively few street segments within the primary mixed- use area, and their presence diminishes beyond the edges of the primary mixed- use area. There may be several design factors at play here. The benefits of locating the development near the city center are quite strong in terms of reducing vehicle miles traveled. However, the railroad tracks and interstate presented preexisting barriers to pedestrian flow and the raised

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 231 overpass connected to the west of the site and offset grid and wide roads at the south of the site presented designed impediments to pedestrian safety and comfort. Our observations indicated pedestrians are clumped where the positive walking conditions are concentrated, at the primary retail area, with its small blocks, pleasant pedestrian paths, and shops. The pedestrians, for the most part, go no further than these particular walking conditions do. To blame site design is not necessarily to blame the designers or the developers, who revised their site plan in the direction of improved walkability (Ewing et al., 2008). Good intentions can further be seen in the regular, free shuttle that circulates through Atlantic Station across the bridge to a light rail and bus hub in Midtown. However, an environmental statement for the development indicates that better integration of the site with the surrounding com- munities was actively and sometimes litigiously opposed by adjacent neighborhoods whose concerns about increased traffic volumes and so-called “cut-through” traffic led to measures to preempt increased traffic volumes through neighborhoods (U.S. Environmental Protection Agency, 2000). It is likely that street networks that support local-to-global continuity for walking were largely disposed of together with the potential for the through-movement by cars. Speaking more broadly, regulatory practices that limit access to neighborhoods have been institutionalized in the United States since the 1930s. As part of its mortgage insurance standards, the Federal Housing Administration adopted design recommendations that advocated neighborhood as distinct from surrounding areas (Federal Housing Administration, 1935). This development scheme was later elaborated by transportation engineers to prescribe few, indirect routes through neighborhoods as well as a hierarchy of streets that divert both human and automobile traffic around rather than through neighborhoods (Marks, 1957, 1961). Such patterns preclude opportunities to walk from one neighborhood to another without encountering wide, fast arte- rial roads inhospitable to pedestrians. These patterns are part of the cultural and regulatory environment against which more innovative forms of development take place today. This case study supports two main points of discussion, one methodological and the other theoretical. Although this case study used pedestrian counts, it also points to the limitations of this approach relative to the study of walkability. If walking for health is a concern, then the focus should be on actual pedestrian paths, rather than pedestrian “clumps.” Pedestrian paths can be recorded either using observational path-tracking methods (e.g., unobtrusively following pedestrians using behavior tracking techniques) or GPS [Global Positioning System] technology, and such data would reveal more about walking behavior than pedestrian counts do.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 232 Environment and Behavior 44(2)

The research methodology has some limitations. First, interrater reliability was not measured. Although the form of behavior mapping was relatively straightforward, without interrater reliability statistics the counts are not defini- tive. Second, more crowded path segments took longer to walk as researchers annotated pedestrian presence; the longer duration on the path may have dif- ferentially impacted the number of pedestrians encountered and recorded relative to less crowded paths that took less time to travel. Walkability and urban liveliness are not the same thing, nor does the presence of one guarantee the presence of other. If increased walking for health is an aim of strategies such as smart growth, or developments such as Atlantic Station, then measures of walkability used during planning and design need to better account for conditions that support walking a sufficient distance to gain health benefits. In particular, increased attention should be paid to local-to-global path conditions and continuity. Extension of a limited number of continuously safe and comfortable paths that do not require multiple direction changes to access the greater street network may have better satisfied the exigencies of pedestrianism without overrunning adjacent neighborhoods with through-traffic.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding Atlantic Station Health Study (ASHS) and Study of Employee Quality of Life (SEQOL) is funded by the U.S. Centers for Disease Control and Prevention, 2006-2009.

References Bafna, S. (2003). Space syntax: A brief introduction to its logic and analytical techniques. Environment and Behavior, 35, 17-29. Boarnet, M., Forsyth, A., Day, K., & Oakes, J. M. (2011). Irvine Minnesota inventory short forms: Results of a predictive validity study. Environment and Behavior, 43(6), 735-775. Centers for Disease Control and Prevention. (2005). Trends in leisure-time physical inactivity by age, sex, and race/ethnicity—United States, 1994–2004. Morbidity and Mortality Weekly Report, 54(39), 991-994. Cervero, R. (2006). Alternative approaches to modeling the travel-demand impacts of smart growth. Journal of the American Planning Association, 72, 285-295. Cervero, R., & Kockelman, R. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2, 199-219. Clifton, K. J., Livi Smith, A., & Rodriguez, D. (2007). The development and testing of an audit for the pedestrian environment. Landscape and Urban Planning, 80, 95-110.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 Zook et al. 233

Day, K., Boarnet, M., Alfonzo, M., & Forsyth, A. (2006). The Irvine-Minnesota inventory to measure built environments: Development. American Journal of Preventive Medicine, 30(2), 144-52. Ewing, R., Bartholomew, K., Winkelman, S., Walters, J., & Chen, D. (2008). Grow- ing cooler, evidence on urban development and climate change. Washington, DC: Urban Land Institute. Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265-294. Federal Housing Administration. (1935). Subdivision development: Standards for the insurance of mortgages on properties located in undeveloped subdivisions: Title II of the National Housing Act, circular 5. US Government Printing Office: Washington, DC. Frank, L., Schmid, T., Sallis, J., Chapman, J., & Saelens, B. (2005). Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine, 28, 117-125. Hess, P. M., Moudon, A. V., Snyder, M. C., & Stanilov, K. (1999). Site design and pedestrian travel. Transportation Research Record, 1674, 9-18. Hill, J. O., & Peters, J. C. (1998). Environmental contributions to the obesity epidemic. Science, 280, 1371-1374. Hillier, B., & Hanson, J. (1984). The social logic of space. New York, NY: University of Cambridge. Hillier, B., Penn, A., Hanson, J., Grajewski, T., & Xu, J. (1993). Natural movement: Configuration and attraction in urban pedestrian movement. Environment and Planning B: Planning and Design, 20, 29-66. Ittelson, W. H., Rivlin, L. G., & Proshansky, H. M. (1970). The use of behavioral maps in environmental psychology. In H. M. Proshansky, W. H. Ittelson, & L. G. Rivlin (Eds.), Environmental psychology: Man and his physical setting (pp. 658-668). New York, NY: Holt, Rinehart & Winston. Levi, J., Vinter, S., St. Laurent, R., & Segal, L. M. (2010). F as in fat: How obesity threatens America’s future. Washington, DC: Trust for America’s Health. Lund, H., Wilson, R. W., & Cervero, R. (2006). A re-evaluation of travel behavior in California TODs. Journal of Architectural and Planning Research, 23, 247-263. Marcus, C. C. (1990). From the pragmatic to the spiritual. In I. Altman & K. Chris- tensen (Eds.), Environment and behavior studies: Emergence of intellectual tradi- tions (pp. 111-140). New York, NY: Plenum. Marks, H. (1957). Subdividing for traffic safety. Traffic Quarterly, 11(3), 308-325. Marks, H. (1961). Geometrics of local and collector streets. ITE Proceedings, 31st annual meeting. ITE: Washington, DC. McCormack, G., Giles-Corti, B., & Bulsara, M. (2008). The relationship between destination proximity, destination mix and physical activity behaviors. Preventive Medicine, 46, 33-40.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012 234 Environment and Behavior 44(2)

Moudon, A. V., Lee, C., Cheadle, A. D., Garvin, C., Johnson, D., Schmid, T. L., Weathers, R., & Lin, L. (2006). Operational definitions of walkable neighborhood: Theoretical and empirical insights. Journal of Physical Activity and Health, 3(Sup. 1), S99–S117. Özbil, A. (2007). Pedestrian movement in three Atlanta neighborhoods. Unpublished manuscript. Özbil, A., & Peponis, J. (2007). Modeling street connectivity and pedestrian movement according to standard GIS street network representations. Proceedings, 6th International Space Syntax Symposium, Istanbul, 01801-01810. Peponis, J., Hadjinikolaou, E., Livieratos, C., & Fatouros, D. A. (1989). The spatial core of urban culture. Ekistics, 334-335, 43-55. Peponis, J., Ross, C., & Rashid, M. (1997). The structure of urban space, movement, and co-presence: The case of Atlanta. Geoforum, 28, 341-358. Physical Activity Guidelines Advisory Committee. (2008). Physical Activity Guide- lines Advisory Committee Report, 2008. Washington, DC: U.S. Department of Health and Human Services. Pikora, T., Bull, F., Jamrozik, K., Knuiman, M., Giles-Corti, B., & Donovan, R. (2002). Developing a reliable audit instrument to measure the physical environment for physical activity. American Journal of Preventive Medicine, 23, 187-194. Sallis, J. F. (2009). Measuring physical activity environments: A brief history. Ameri- can Journal of Preventive Medicine, 36, S86-S92. U.S. Environmental Protection Agency. (2000). Finding of no significant impact for 17th street extension [GDOT Project NH-7141-00(900), P.I. Number 714190] and Atlantic Steel Redevelopment Project Fulton County, Georgia. Retrieved from http://www.epa.gov/projectxl/atlantic/fonsi.pdf Ye, L., Mandpe, S., & Meyer, P. B. (2005). What is “smart growth?”—Really? Journal of Planning Literature, 19, 301-315.

Bios Julie Brand Zook is a doctoral student in the College of Architecture at the Georgia Institute of Technology.

Yi Lu is a doctoral candidate in the College of Architecture at the Georgia Institute of Technology.

Karen Glanz is a Penn Integrates Knowledge (PIK) professor of Medicine and Nursing at the University of Pennsylvania.

Craig Zimring is a professor in the College of Architecture at the Georgia Institute of Technology.

Downloaded from eab.sagepub.com at UNIV OF PENNSYLVANIA on March 3, 2012