The relationship between cluster-analysis derived walkability and local recreational and transportation walking among Canadian adults

Health & Place 18 (2012) 1079–1087

Contents lists available at SciVerse ScienceDirect

Health & Place journal homepage: www.elsevier.com/locate/healthplace

The relationship between cluster-analysis derived walkability and local recreational and transportation walking among Canadian adults Gavin R McCormack a,n, Christine Friedenreich b, Beverly A Sandalack c, Billie Giles-Corti d, Patricia K. Doyle-Baker e, Alan Shiell a a

Department of Community Health Sciences, University of Calgary, Alberta, Canada Department of Population Health Research, Alberta Health Services, Alberta, Canada c Faculty of Environmental Design, University of Calgary, Alberta, Canada d McCaughey Centre, Melbourne School of Population Health, University of Melbourne, Melbourne, Australia e Faculty of Kinesiology, University of Calgary, Alberta, Canada b

a r t i c l e i n f o

abstract

Article history: Received 21 November 2011 Received in revised form 5 April 2012 Accepted 30 April 2012 Available online 15 May 2012

We investigated the association between objectively-assessed neighborhood walkability and local walking among adults. Two independent random cross-sectional samples of Calgary (Canada) residents were recruited. Neighborhood-based walking, attitude towards walking, neighborhood self-selection, and socio-demographic characteristics were captured. Built environmental attributes underwent a twostaged cluster analysis which identified three neighborhood types (HW: high walkable; MW: medium walkable; LW: low walkable). Adjusting for all other characteristics, MW (OR 1.40, p o0.05) and HW (OR 1.34, approached po 0.05) neighborhood residents were more likely than LW neighborhood residents to participate in neighborhood-based transportation walking. HW neighborhood residents spent 30-min/wk more on neighborhood-based transportation walking than both LW and MW neighborhood residents. MW neighborhood residents spent 14-min/wk more on neighborhood-based recreational walking than LW neighborhood residents. Neighborhoods with a highly connected pedestrian network, large mix of businesses, high population density, high access to sidewalks and pathways, and many bus stops support local walking. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Built environment Physical activity Neighborhood self-selection Propensity score analysis Cluster analysis

1. Background There is increasing evidence that neighborhood design has the potential to contribute to health by encouraging more physical activity. Transportation and leisure walking is more common in attractive neighborhoods with connected street networks (Cerin et al., 2007; Hoehner et al., 2005) offering direct routes to local utilitarian and recreational destinations (Frank et al., 2005; Frank et al., 2004), increased population and employment density (Oakes et al., 2007; Frank et al., 2005), sidewalks (Duncan and Mummery, 2005; Lee and Moudon, 2006; Lovasi et al., 2008), and traffic control devices (Wendel-Vos et al., 2007; McCormack et al., 2004; Saelens and Handy, 2008). Moreover, the presence of and access to high quality parks and green space encourages walking and other physical activities (Kaczynski and Henderson, 2007; Coombes et al., 2010; McCormack et al., 2010). Even access to transit has the potential to increase physical activity as most n Corresponding author at. Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive, N.W. Calgary, Alberta, Canada T2N 4Z6. Tel.: þ 1 403 220 8193; fax: þ 1 403 210 3818. E-mail address: [email protected] (G. McCormack).

1353-8292/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.healthplace.2012.04.014

public transport trips begin and or end with walking (Besser and Dannenberg, 2005). While evidence suggesting that individual neighborhood attributes are associated with walking is encouraging, increasingly it is being recognized that the combined effects of many, rather than a few, environmental attributes might better explain walking behavior (Frank et al., 2007; Sallis et al., 2009b). Walkability indices, created by combining scores for variables that represent intersection density, land use mix, residential density, and commercial floor space area, are often found to be positively associated with walking (Sallis et al., 2009a; Frank et al., 2007; Owen et al., 2007; Learnihan et al., 2011). Specifically, Frank et al. (2007) found positive associations between neighborhood walkability and both transportation and leisure walking trips. Like others, (Sallis et al., 2009a; Owen et al., 2007), Learnihan et al. (2011) found positive associations between walkability and local transportation but not recreational walking. However, in this latter study the strength of the associations depended on the geographical scale of the index, with enhanced associations the smaller the scale. Greenwald and Boarnet (2001) derived a pedestrian environment index using neighborhood attributes including ease of street crossing, sidewalk continuity, street

1080

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

connectivity, and topography and found it to be positively associated with the frequency of non work-related walking trips—suggesting that attributes in addition to land use mix and residential density might be important contributors to a neighborhood’s walkability. Other methods have also been used to classify neighborhoods based on their walkability. Using cluster analysis, Cerin et al. (2007) identified three neighborhood types based on dominant land-uses and found that respondents residing in commercial/ industrial dominant neighborhoods undertook more transportation walking than those residing in recreational dominant neighborhoods. Similarly, McNally and Kulkarni (1997) identified three neighborhood types using cluster analysis: neotraditional (few cul-de-sacs, many four-way intersections, high commercial area, and high population density), planned unit development (many cul-de-sacs, few four-way intersections, less commercial area, and low population density), and mixed. However, these researchers found no neighborhood differences in pedestrian activity. Adams et al. (2011) used latent profile analysis to identify four distinct neighborhood profiles based on perceptions of the built environment’s supportiveness for walking. Moderate-to-vigorous physical activity, leisure walking, and transportation walking were highest among residents of neighborhoods with the highest levels of residential density, land use mix and access, intersection density and bus or transit, and a proximate fitness facility and park (Adams et al., 2011). Walkability indices are useful for summarizing a neighborhoods’ potential for encouraging walking but they provide little information about the extent to which underlying environmental attributes coexist among different neighborhood types. Moreover, a high walkability score could reflect a combination of high and low individual environmental attribute scores. Walkability scores are often derived based on an additive structure, which requires all individual attribute scores to be positively correlated with the overall walkability score (Sallis et al., 2009a; Frank et al., 2007; Owen et al., 2007; Learnihan et al., 2011). This means that attributes such as parks, which are positively associated with walking (Kaczynski and Henderson, 2007; Coombes et al., 2010; McCormack et al., 2010) but which compete with other land uses for urban space (e.g., commercial) and are therefore negatively associated with the walkability score, are excluded. An incomplete description of the neighborhood built environment is likely when only those attributes that are positively correlated are combined to form a walkability score. Other procedures for determining neighborhood walkability, such as cluster analysis, might overcome some of these previous limitations by allowing a wider variety of neighborhood attributes to be considered. Despite advances in the field, few studies have examined the relationship between physical activity and neighborhoods that naturally cluster based on their objectively-measured built environment attributes and physical activity, including walking (Cerin et al., 2007; McNally and Kulkarni, 1997). Thus, the purpose of this study was to use cluster analysis to identify neighborhoods with homogeneous built environment attributes, and to examine whether or not these neighborhood clusters were associated with participation and the quantity of local walking for transportation and recreation among city-dwelling adults.

2. Method

telephone-interviews between August and October, 2007 and January and April, 2008. Eligible participants were Z18 years of age and in the case of multiple members of one household being eligible the individual with the most recent birthday was selected. Respondents provided their postal code and those residing outside the city limits were excluded. Response rates (RR¼completed/completed plus refused) for interviews completed in 2007 (n ¼2199; RR¼ 33.6%) and 2008 (n ¼2223; RR¼36.7%) were similar. A pilot study (n ¼117) preceded the main study, during which telephone survey items were assessed for test–retest reliability (i.e., administered twice, 2–5 day apart). The Conjoint Health Research Ethics Board at the University of Calgary granted ethics approval for this study. 2.2. Neighborhood-specific walking The Neighborhood Physical Activity Questionnaire (NPAQ: Giles-Corti et al., 2006) was used to capture usual weekly participation in and duration of neighborhood-based transportation and recreational walking (a 15-min walk of home). NPAQ items have acceptable reliability among adults (GilesCorti et al., 2006, McCormack et al., 2009a). Three variables were calculated: (1) non-participation ( o10 min/week) vs. participation ( Z10 min/week); (2) duration (min/week) in those who walked, and; (3) insufficient (10 to o150 min/week) vs. sufficient (Z150 minutes/week) neighborhood-based transportation and recreational walking, respectively. Current Canadian physical activity guidelines recommend the accumulation of at least 150min of moderate-to-vigorous intensity physical activity per week for adults to accrue optimal health benefits (Tremblay et al., 2011). 2.3. Neighborhood self-selection and length of neighborhood tenure Respondents reported the importance (not at all, somewhat, or very important) of physical and social characteristics considered when they chose to reside in their current neighborhood. Nineteen items captured the importance of proximity of recreational facilities, trails, parks, services, school/job, family/friends, transit, and downtown; the availability of places for physical activity, walking, cycling, attractive streets, and highways; ease of driving, and walking; safety from crime; sense of community, and; affordability. The items had acceptable test–retest reliability (rho¼0.52–0.85). Time spent residing in the respondents current neighborhood was also collected (rho ¼0.97). 2.4. Attitude towards walking Six items with five-point Likert scales captured respondent agreement (strongly disagree to strongly agree) that walking in the near future would be foolish, beneficial, useful, enjoyable, relaxing, and interesting and had acceptable test retest reliability (rho¼0.53–0.74). 2.5. Socio-demographic characteristics Home ownership status (i.e., own/buying versus non-owner), gender, age, highest education achieved (i.e. high school or less, college/technical college, university undergraduate, or university postgraduate) and number of dependents o18 years of age residing at home (i.e., none, one, or Z2 children) were measured.

2.1. Sample 2.6. Built environment characteristics The study design and methods have been previously described (McCormack et al., 2009b). In brief, two independent random cross-sectional samples of Calgary adults were recruited during

Participant postal code, collected during the telephone-interview, was used as a proxy for their household street address.

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

A latitude and longitude co-ordinate representing the centroid of the respondents’ six digit postal code was determined using the Statistics Canada Postal Code Conversion File. In Canada, a single six digit postal code typically includes 15–20 households and approximately 88% of geocoded postal code locations are within 200 m of geocoded household street addresses (Bow et al., 2004). A 1.6 km (km) line-based network buffer or walkshed (Oliver et al., 2007) was estimated for each household’s postal code and represented the distance that could be walked in any direction within 15-min. Increases in walkshed area (km2) reflected increased pedestrian network connectivity. Geographical information systems (GIS) data were available at the walkshed level or in a more aggregated form (i.e., the Calgary administrative neighborhood boundary level). The mean 7standard deviation and median (range) geographical area of the neighborhood administrative boundaries included in our study (n ¼194) was 2.4071.96 km2 and 1.97 km2 (0.25 to 17.37 km2), respectively. Values for environmental characteristics available at the administrative neighborhood level were assigned to respondents based on their postal code when walkshed level data were not available. The City of Calgary provided business license data (2008). The database included business name, address, and service information for all registered businesses in the Calgary metropolitan area (n¼ 33190). All businesses were coded according to the primary type of service or good offered (restaurants, bakeries, video stores, convenience stores, casinos, cinemas, coffee shops, drugstores, supermarkets, etc.). Duplicate entries, businesses with no fixed address (i.e., postbox only, online business), or that did not provide a service or good directly to the public (e.g., caterers, warehouses, advertisers, contractors, home-based and industry) were removed. The addresses of the remaining business (n¼14747) were geocoded and the total number of businesses per walkshed square kilometer was estimated. Using City of Calgary data with geocoded public bus stops and sidewalk locations, the number of bus stops and length of sidewalk (meters) per walkshed square kilometer were estimated. Data for Calgary metropolitan recreational destinations including the addresses of parks (i.e., neighborhood parks with playgrounds, neighborhood parks without playground, provincial parks, nature areas or reserves, city parks, and off-leash dog park) and other facilities (i.e., athletic arenas, recreation centers, community centers, outdoor pools, indoor pools, and private and public golf courses) were extracted from a publically available source in 2009 (City of Calgary: http://www.calgary.ca/) and geocoded. From these data two variables: (1) count of the different types of parks, and; (2) count of the different types of recreational facilities (excluding parks), per walkshed square kilometer, were derived. Environmental data available at the Calgary administrative neighborhood boundary level included population density (total population/km2), ratio of green space to total neighborhood area (including playgrounds, playing fields, parks, and green strips along roads but excluding school yards and vacant lots), and length of path/cycleway (meters/km2). The year of establishment, city region and street pattern of the neighborhood were also available.

2.6.1. Statistical analysis Cluster-derived neighborhood types: Built environment variables including: walkshed area, number of business/km2; number of bus stops/km2; mix of park types/km2; mix of recreational facilities/km2; sidewalk (meters)/km2; total population/km2; percentage of neighborhood green space, and; path/cycleway (meters)/km2 underwent a two-staged cluster analysis to identify neighborhoods with homogeneous built environment characteristics. Similar approaches have been used elsewhere for

1081

identifying natural groupings of neighborhoods based on their physical attributes (Cerin et al., 2007). The Calgary metropolitan area consists of three broad descriptive neighborhood types that have been identified based on expert judgment and consensus (Sandalack and Nicolai, 2006). Chronologically, the first type of neighborhood was built prior to World War II and includes a grid street pattern that offers high levels of pedestrian connectivity, permeability and route choice, consists of a mix of land uses, and often includes treed boulevards with sidewalks on both sides of the street (i.e., high walkability). The second type of neighborhood was built immediately after World War II and includes a warped-grid street pattern with crescents and curved roads resulting in less pedestrian connectivity compared with the traditional grid pattern, fewer treed boulevards with sidewalks directly adjacent to roads, mostly residential land usually surrounding a centrally located elementary school and community center, and commercial developments at the edges (i.e., medium walkability). The third type of neighborhood has been built during the past three decades and includes high-volume collector roads, a curvilinear ‘loops and lollipops’ road pattern often with a strip of auto-oriented commercial land (i.e., convenience stores and services), low levels of pedestrian connectivity, and sidewalks often missing from one or both sides of the street (i.e., low walkability). Based on this knowledge we initially restricted the cluster selection to three neighborhood types but further explored other cluster configurations to identify the best fitting model. Analysis of Variance (ANOVA) was used to test for differences across variables used to identify the neighborhoods. The coefficient of variation (CV) provided a measure of variation relative to the mean. The estimate was used as a descriptor of the intraneighborhood variation of the built environment variables. A built environment variable with a low CV suggests that it is a common attribute or that it is present to a similar extent among neighborhoods of a given cluster. Chi-square was used to assess concurrent validity of the neighborhood profiles with external data (i.e., not used to identify the neighborhoods). The external data included: (1) street pattern (i.e., grid, warped-grid, curvilinear, or other) based on the previous description of Calgary neighborhoods (Sandalack and Nicolai, 2006); (2) year the neighborhood was established (i.e., pre-1950, 1950–1980, or post-1980) obtained from the City of Calgary, and; (3), urban geographical location (inner city or suburb) also obtained from the City of Calgary. These external data were available at the Calgary administrative neighborhood boundary level. Propensity score estimation: Residential location choice is based on many factors such as socio-demographic characteristics, economic circumstances, preferences for and attitudes towards particular neighborhood characteristics, travel behaviors, and physical activity behaviors. Comparing levels of walking behavior between different neighborhood types might produce biased estimates of the neighborhood’s effect on walking if factors related to neighborhood self-selection are not taken into account. We used propensity score analysis (Rosenbaum and Rubin, 1983) to address the non-random or self-selection of individuals into neighborhoods. Propensity score analysis was initially designed for two group comparisons (i.e., treatment and control). However, the method has been extended to include multiple groups. Our study compared three neighborhoods using a multiple propensity score analysis (Spreeuwenberg et al., 2010) to estimate the conditional probabilities of a respondent residing in each of the neighborhood clusters given their observed characteristics (i.e., neighborhood self-selection, length of neighborhood tenure, attitude towards walking, socio-demographics characteristics, and season the telephone survey was completed). Multinomial logistic regression was used to examine the association between the

1082

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

Calgary neighborhood administrative area. Cluster analysis restricted to three cluster selection provided reasonable fit to the data (average silhouette¼0.4 or fair). We also conducted a cluster analysis with automated cluster selection for comparative purposes and found an identical three cluster structure based on the Schwarz’s Bayesian Information Criterion and log-likelihood distance measure. The first identified neighborhood type (n¼2064) was labeled the ‘‘Low walkable’’ neighborhood (LW). Low walkable neighborhoods on average had: less street connectivity (i.e., small walkshed area), population density, sidewalk availability, mix of recreational destinations, available business destinations and bus stops, but a slightly higher amount of open space, a greater mix of park types and more path/cycleway available. Moreover, there was high LW intra-neighborhood variation for the number of businesses (CV¼0.96) and the mix of recreational destinations (CV¼1.14) (Table 1). The second identified neighborhood type (n¼1330) was labeled the ‘‘Moderate walkable’’ neighborhood (MW). Compared with LW neighborhoods, on average MW neighborhoods had: higher connectivity, sidewalk availability, mix of recreational destination and mix of park types; more business destinations, bus stops; but a lower mix of park types, and slightly less green space and path/cycleway availability (Table 1). Population densities were similar. The third neighborhood type (n¼263) was labeled the ‘‘High walkable’’ neighborhood (HW). Compared with the other neighborhood types, on average HW neighborhoods had considerably higher: connectivity, population densities, mix of recreational destinations, numbers of business destinations and bus stops and kilometers of paths/cycleways available, mix of park types, and path/cycleway availability; and more business destinations and bus stops. HW neighborhoods had similar sidewalk availability and proportion of green space compared with MW neighborhoods, but a lower mix of park types compared with LW neighborhoods. Moreover, high HW intra-neighborhood variation was observed in the number of bus stops (CV¼1.12), area of green space (CV¼0.90), and path/cycleway availability (CV¼0.91) (Table 1). Evidence for concurrent validity for the three neighborhood types was found. A high proportion of LW neighborhoods were built from 1980 to present (69.8%), consisting mainly of curvilinear street patterns (73.4%), and were more likely to be located in the suburbs (94.9%). MW neighborhoods were generally built from 1950 to 1980 (79.5%), had a high proportion of warped grid street patterns (54.4%), and were mostly located in the suburbs (65.0%). In contrast, a high proportion of HW neighborhoods were built prior to 1950 (65.0%), included mostly grid street patterns (69.6%), and were most likely to be situated in the inner city (65.0%) (Table 2). The identified neighborhood clusters reflected

neighborhood cluster and all neighborhood self-selection, length of neighborhood tenure, attitude towards walking, socio-demographic, and season variables. Balancing observed characteristics of respondents across the neighborhood clusters reduces confounding and bias in estimates of the neighborhood built environment effect on walking. To assess this (i.e., a balance-check), we used ANCOVA for continuous variables and multinomial logistic regression for categorical variables to determine whether or not statistically significant differences in respondent characteristics between neighborhood clusters remained after propensity score adjustment. The covariates are considered balanced when no significant differences are observed. Walking outcome regression models: Two of the three estimated propensity scores were included as covariates in all Generalized Linear Models (GZLM) that estimated the differences in the walking behaviors across the neighborhood types. A GZLM (binary distribution and a logit link function) was used to estimate odds ratios (OR) and 95% confidence intervals (CIs) for the likelihood of undertaking: (1) any WT; (2) any WR; (3) sufficient WT (i.e., Z150 min/wk), and; (4) sufficient WR (i.e., Z150 min/wk) associated with residing in each of the neighborhoods. A GZLM (gamma distribution and identity link function) was used to estimate beta coefficients (b) and 95% confidence intervals representing the difference in minutes of neighborhood-based transportation and recreational walking among those reporting some participation in these activities (i.e., 410 min/week). The GZLMs were adjusted for respondent clustering within the neighborhood administrative areas. SPSS (version 19) was used for all analyses.

3. Results 3.1. Characteristics of neighborhood subgroups There were n¼3684 postal codes for the n¼4422 respondents that completed the telephone-interviews. The 3684 postal codes were linked to built environment data available at the walkshed and administrative neighborhood boundary levels. The built environment data were included in the cluster analysis and neighborhood types with homogeneous built environment attributes were identified. Respondents with incomplete socio-demographic, neighborhood self-selection, walking attitudes, and walking behavior data were then removed from the dataset resulting in a final sample of n¼4034. On average, there were 1.1970.50 respondents from the same postal code (i.e., walkshed) and 21.46715.45 respondents from the same

Table 1 Comparison of walkshed and neighborhood administrative boundary environmental attributes among the neighborhood built environment profiles. Neighborhood built environment profile Low walkable N(neighborhoods)¼ 2064

Moderate walkable N(neighborhoods) ¼ 1330 CV

Mean 7SD

CV

Environmental attribute based on area within 1.6 km of respondents home Walkshed area (km2) 2.15 7 0.69 0.32 3.36 70.85 # of businesses/km2 11.95 7 11.49 0.96 32.65 722.16 # of bus stops/km2 11.06 7 4.38 0.40 14.30 74.10 Mix of park types/ km2 0.57 7 0.56 0.99 0.29 70.31 Mix of recreational destinations/km2 0.24 7 0.27 1.14 0.64 70.41 2 Sidewalk m/km 13958.027 24440.46 0.17 19565.06 72513.46

0.25 0.68 0.29 1.09 0.64 0.13

3.70 71.07 142.877119.12 29.87 733.42 0.34 70.46 0.48 70.32 17255.24 73601.15

0.29 0.83 1.12 1.33 0.67 0.21

Environmental attribute based on administrative boundary in which respondents home was located 2826.077 920.33 0.33 2680.187925.01 Total population/km2 % of green space area 19.00 7 9.00 0.46 17.00 711.00 Paths/cycleway m/km2 2742.63 7 1167.85 0.43 1845.47 710005.27

0.35 0.66 0.54

7451.57 72364.48 15.00 714.00 3507.5773185.74

0.32 0.90 0.91

Environmental attribute

Mean7 SD

CV

Mean 7 SD

High walkable N(neighborhoods) ¼263

SD: Standard deviation. CV: Coefficient of variation. Differences among neighborhood profiles all statistically significant (p o0.05).

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

the expert-based descriptions of the three Calgary neighborhood types described elsewhere (Sandalack and Nicolai, 2006). 3.2. Sample characteristics LW neighborhood residents were the youngest (45.9715.1 years) while MW neighborhood residents were the oldest in age (49.6716.1 years). Compared with HW neighborhoods (54.4%), a slightly higher proportion of LW and MW neighborhood residents were women (60.2% and 60%, respectively). HW neighborhood

1083

residents were more likely to have undergraduate or postgraduate university education (48.0%) compared with those from the other neighborhoods. Compared with other neighborhood types, a higher proportion of HW neighborhood residents reported having no dependents o18 years of age living at home (78.6%). Moreover, home ownership was highest in LW neighborhoods (87.0%) followed by the MW (77.4%) and HW neighborhoods (57.1%), respectively. Length of tenancy was 7.9 78.2 years for HW, 14.8713.6 years for MW, and 9.4 79.2 years for LW neighborhoods, respectively (Table 3).

Table 2 Assessment of concurrent validity by comparison of external neighborhood data (established year, street pattern type, city region) among neighborhood built environment profiles. Neighborhood built environment profile Low walkable N(neighborhoods) ¼ 2064 (%)

Moderate walkable N(neighborhoods)¼ 1330 (%)

High walkable N(neighborhoods)¼ 263 %

Year established Before 1950 1950 to 1980 1980 to present Data not available

1.7 27.7 69.8 0.9

18.9 79.5 0.9 0.8

65.0^ 26.2 8.7 0.0

Street patternb Grid Warped grid Curvlinear Other

5.0 19.0 73.4 2.5

33.3 54.4 8.6 3.7

69.6^ 8.0 22.4 0.0

City regiona Inner city Suburbs

3.1 96.9

35.0 65.0

65.0^ 35.0

a

^ a b

p o 0.05. Data from City of Calgary records. Based on previous categorization of street typologies for the Calgary Metropolitan region Sandalack and Nicolei [1].

Table 3 Socio-demographic characteristics of respondents by neighborhood environment profile (n¼ 4034n). Neighborhood built environment profile Low walkable n ¼2322

Moderate walkable n ¼1418

High walkable n ¼ 294

Age in years (mean7 SD)

45.86 7 15.09

49.63 7 16.14

44.51 7 14.83^

Sex (women: %)

60.2

60.0

54.4

Highest education achieved (%) High school diploma or less College or technical college Undergraduate degree Postgraduate degree

30.7 26.7 29.1 13.4

34.9 25.4 25.0 14.7

27.9^ 24.1 33.0 15.0

Number of children (%) None One Two or more

56.2 17.8 26.0

70.0 12.8 17.3

78.6^ 11.2 10.2

Season (%) Summer Autumn Winter Spring

12.0 37.4 25.3 25.3

15.2 35.9 24.4 24.5

13.3 34.0 24.1 28.6

Home ownership (owners: %)

87.0

77.4

57.1^

Socio-demographics

Neighborhood tenure (mean 7 SD)

9.44 7 9.21

Neighborhood-based walking in a usual week (%) Any recreational walking 76.1 Any transportation walking 53.1 ^ n

14.84 7 13.62

73.6 64.8

p o 0.05 (Statistical significance assessed using chi-square for categorical variables and ANOVA for continuous variables). Respondents with complete neighborhood environment, socio-demographic, self-selection, attitude, and walking data.

7.90 7 8.22^

72.1 79.3^

1084

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

Table 4 Residential choice and attitudes towards walking among respondents by neighborhood built environment profile (n¼ 4034n). Neighborhood built environment profile

Reason for moving to the neighborhooda

Low walkable n ¼ 2322

Moderate walkable n ¼ 1418

High walkable n ¼ 294

Mean7 SD

Mean7 SD

Mean7 SD

Affordability Proximity to parks Proximity to job/school Proximity to transit Proximity to stores/services Ease of walking Sense of community Safety from crime Proximity to recreation facilities Access to highways Attractive streets Proximity to family/friends Views of scenery (e.g., mountains) Cleanliness of streets Proximity to downtown Proximity to trails Places to be physically active Places to walk/cycle to Ease of driving

2.43 7 0.66 2.17 7 0.73 2.13 7 0.83 1.84 7 0.80 2.11 7 0.68 2.19 7 0.75 2.17 7 0.70 2.67 7 0.60 1.98 7 0.69 1.93 7 0.74 2.28 7 0.66 1.94 7 0.80 1.97 7 0.75 2.507 0.62 1.61 7 0.70 1.86 7 0.71 2.16 7 0.67 2.22 7 0.70 2.307 0.68

2.44 7 0.68 2.11 7 0.73 2.36 7 0.80 2.007 0.83 2.23 7 0.67 2.28 7 0.73 2.16 7 0.71 2.50 7 0.68 2.007 0.70 1.80 7 0.73 2.25 7 0.68 1.91 7 0.80 1.74 7 0.72 2.38 7 0.65 2.04 7 0.79 1.75 7 0.72 2.10 7 0.68 2.17 7 0.73 2.24 7 0.70

2.34 7 0.70 2.017 0.73^ 2.33 7 0.83^ 2.13 7 0.85^ 2.37 7 0.64^ 2.57 7 0.65^ 1.89 7 0.69^ 2.28 7 0.76^ 1.87 7 0.75^ 1.52 7 0.69^ 2.027 0.70^ 1.79 7 0.74^ 1.81 7 0.75^ 2.19 7 0.69^ 2.37 7 0.80^ 1.83 7 0.76^ 2.11 7 0.67^ 2.307 0.71^ 1.89 7 0.76^

Attitudes towards walking in the near futureb Useful Unfoolish Beneficial Enjoyable Interesting Relaxing

4.407 0.76 4.44 7 0.73 4.54 7 0.61 4.307 0.72 3.96 7 0.84 4.15 7 0.78

4.40 7 0.78 4.39 7 0.79 4.49 7 0.65 4.27 7 0.73 3.96 7 0.86 4.11 7 0.81

4.53 7 0.72^ 4.49 7 0.70^ 4.58 7 0.60^ 4.31 7 0.76 4.027 0.92 4.207 0.82

^ n

a b

p o0.05 (Statistical significance assessed using ANOVA). Included respondents with complete neighborhood environment, socio-demographic, walking, self-selection, and attitude data. Response categories: 1 ¼not important; 2¼ somewhat important; 3 ¼very important. Response categories: 1¼ Strongly disagree to 5 strongly agree.

All but one residential location choice variable differed significantly (po0.05) by neighborhood type—i.e., the importance of housing affordability (Table 4). Among those residing in HW neighborhoods ‘‘ease of walking’’ was the most important reason for residential location choice while for those in LW and MW neighborhoods ‘‘safety from crime’’ was the most important reason. Among HW neighborhood residents ‘‘access to highways’’ was the least important reason reported for residential location choice, while for those residing in MW and LW neighborhoods, ‘‘views of attractive scenery’’ and ‘‘proximity to downtown’’, respectively, were of least importance (Table 4). Among the six items capturing respondent attitude toward walking in the near future, only agreeing that doing so would be useful, beneficial and not foolish significantly (po0.05) differed between by neighborhood (Table 4). Neighborhood type was regressed on all observed covariates. Together the covariates were important contributors to neighborhood assignment (Naglkerke R2 ¼0.34). After adjustment for the propensity scores, there were no significant differences between the three neighborhoods for any socio-demographic, neighborhood self-selection, or attitude variable. This finding suggests that the propensity score estimates successfully balanced all the observed covariates across the three neighborhoods. Any differences in walking behavior observed between the three neighborhoods were therefore the result of the built neighborhood characteristics or potentially unmeasured covariates.

(79.3%) followed by MW (64.8%) and LW neighborhood residents (53.1%) (po0.05) (Table 3). After adjusting for the propensity scores and neighborhood clustering, MW and HW neighborhood residents were more likely than LW neighborhood residents to undertake some neighborhood-based transportation walking (OR 1.42, 95%CI 1.21, 1.67 and; OR 1.38, 95%CI 0.89, 2.14, respectively), although the result for HW neighborhoods did not reach statistical significance (Table 5). On average, HW neighborhoods residents who engaged in neighborhood-based transportation walking did so on average 209.47195.2 min/wk which was higher than both MW (119.97 140.7 min/wk) and LW (105.67132.8 min/wk) neighborhood residents (po0.05). After adjusting for the propensity scores and neighborhood clustering, the usual minutes of neighborhood-based transportation walking in HW residents was significantly higher than both LW (difference ¼31.9 min/wk) and MW (difference¼30.4 min/wk) neighborhood residents (Table 5). Residents who were able to achieve sufficient levels of walking through neighborhood-based transportation walking were those in HW neighborhoods (45.9%) followed by MW (22.6%) and LW (17.8%) neighborhoods (po0.05). After adjusting for the propensity scores and neighborhood clustering, HW neighborhood residents were significantly more likely to achieve Z150 min of neighborhoodbased transportation walking in a usual week compared with LW neighborhood residents (OR 1.60, 95%CI 1.08, 2.37) (Table 5).

3.3. Association between neighborhood type and walking for transportation

3.4. Association between neighborhood type and walking for recreation

Participation in neighborhood-based transportation walking in a usual week was highest among HW neighborhoods residents

Participation in neighborhood-based recreational walking in a usual week was slightly higher among LW neighborhoods

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

1085

Table 5 Associations between neighborhood built environment profile and transportation and recreational walking behavior undertaken inside the neighborhood. Transportation walking inside the neighborhood in a usual week Neighborhood profile

Participation OR (95%CI)a n ¼ 4034

Minutes B (95%CI)b,c n ¼2385

Z150 min OR (95%CI)a,c n ¼ 2385

Low walkable (reference category) Moderate walkable High walkable

1.00 1.42 (1.21, 1.67)^ 1.38 (0.89, 2.14)

0.00 1.46 (  10.66, 13.57) 31.91 (1.33, 62.49)^

1.00 1.14 (0.91, 1.42) 1.60 (1.08, 2.37)^

Recreation walking inside the neighborhood in a usual week

Low walkable (reference category) Moderate walkable High walkable

Participation OR (95%CI)a n ¼ 4034

Minutes B (95%CI)b,c n ¼3022

Z150 min OR (95%CI)a,c n ¼ 3022

1.00 1.00 (0.83, 1.19) 1.03 (0.72, 1.47)

0.00 14.16 (  1.25, 29.57) 13.66 (  16.82, 44.13)

1.00 0.94 (0.80, 1.11) 1.00 (0.70, 1.42)

p o 0.05. Odds ratios (OR) Estimated using a Generalized Linear Model with a binomial distribution and logit link functions. b Beta coefficients (B) estimated using a Generalized Linear Model with a gamma distribution and identity link functions. c Model includes only those participants reporting participation in activity. All models adjusted for neighborhood self-selection, attitudes towards walking, demographic characteristics, season, and neighborhood tenure using multiple propensity scores and respondent clustering within the neighborhood administrative boundaries. ^ a

residents (76.1%) compared with those from MW (73.6%), and HW (72.1%) neighborhoods, although the difference was not statistically significant (Table 3). After adjusting for the propensity scores and neighborhood clustering, there were no significant neighborhood differences in the likelihood of participating in neighborhood-based recreational walking (Table 5). HW neighborhood residents who participated in neighborhoodbased recreational walking did so, on average, 198.17186.1 min/ wk. MW (189.67189.1 min/wk) and LW (182.87169.4 min/wk) neighborhood residents spent less time in neighborhood-based recreational walking however, the difference was not significant (p40.05). After adjusting for the propensity scores and neighborhood clustering, MW neighborhood residents spent, on average, 14.2 min/wk more walking for recreation inside the neighborhood than LW neighborhood residents although this difference only approach statistical significance (p¼0.07). Compared with LW residents, HW residents also spent more time per week walking for recreation inside the neighborhood (13.7 min/wk) however, this difference was not statistically significant (Table 5). Among respondents who participated in neighborhood-based recreational walking, achieving sufficient levels in a usual week was highest among HW neighborhood residents (46.2%) and lowest among LW (42.5%) and MW (41.1%) neighborhood residents, although the difference was not significant before or after propensity score and cluster adjustment (Table 5).

4. Discussion Residing in a high walkable neighborhood encouraged more neighborhood-based transportation walking with respect to any participation in walking, total duration, and achievement of sufficient levels. Our results lend further support for the typology of the urban landscape found in Calgary which is quite comparable to other North America cities developed over this same time period, suggesting the existence of three general neighborhood types that offer different levels of pedestrian-friendliness (Sandalack and Nicolai, 2006). HW neighborhood residents undertook more neighborhoodbased transportation walking (participation and duration) than those from LW neighborhoods. These results are promising as our estimates are adjusted for neighborhood self-selection and our walking measures are context-specific. Our findings corroborate the few previous studies that have found associations between

the built environment and neighborhood-based walking that were adjusted for neighborhood self-selection (Cao et al., 2009; Pinjari et al., 2009; Handy et al., 2006; Shay et al., 2006). We found that HW neighborhood residents were 34% more likely to participate in neighborhood-based transportation walking and  58% more likely to achieve at least 150 min/wk of neighborhood-based transportation walking compared with LW neighborhood residents. Moreover, on average HW neighborhood residents undertook neighborhood-based transportation walking for 30 min more per week than those from LW neighborhoods. Despite the results being derived from cross-sectional data, our findings might suggest that designing and creating high walkable neighborhoods has the potential to support walking behavior. Using cluster analysis allowed differences in the measured built characteristics between an average LW, MW, and HW neighborhood to be estimated. If replicated across other studies, this information could be used to develop thresholds that inform urban planners and designers about the specific levels of neighborhood attributes required to create a more walkable neighborhood within a given context—i.e., in a Canadian urban setting. Our findings suggest that retrofitting the average LW neighborhood to be more like the average HW neighborhood would require substantial environmental modification. This level of change might be unrealistic for most LW neighborhoods, especially in the short-term. Moreover, whether or not the cost of retrofitting entire neighborhoods where less substantial changes are necessary, would be off-set by health and sustainability gains is yet to be determined. There is evidence suggesting that modifying some characteristics of the built environment, such as building trails, might be a cost-effective intervention for increasing physical activity and improving health (Wang et al., 2004). For established neighborhoods, interventions that modify micro-scale or street-level (e.g., installing sidewalks or street furniture, planting trees, improving pedestrian safety) characteristics might be a more realistic option for improving walkability. More research is needed to determine whether modifying one or few attributes is sufficient or whether a complete redesign of entire neighborhoods is required to optimize physical activity and health outcomes. As demonstrated in this study, neighborhoods differed on many attributes that might support walking. Neighborhoods with a low mix of commercial and retail destinations, connectivity, and population density also had a low mix of recreational destinations, and fewer sidewalks and bus stops (i.e., LW neighborhoods).

1086

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

Despite having more green space and a mix of parks compared with the other neighborhoods, notably LW neighborhoods did not encourage recreational walking. Weekly minutes of neighborhood-based recreational walking were lower (  14-min/wk) in LW neighborhoods than in MW, and HW neighborhoods. It is possible that for recreational walking the total amount of open space will be less important than the quality, attractiveness and safety of the open space (Giles-Corti et al., 2005; Sugiyama and Thompson, 2008; McCormack et al., 2010). Sugiyama et al. (2010) found that the presence of attractive open space may encourage some walking, but having access to both large and attractive open spaces in the neighborhood encouraged walking at recommended levels. Moreover, environmental attributes not measured here, such as the park esthetics and safety (traffic and personal) might support walking behavior (Foster et al., 2004; Humpel et al., 2004). Similar to those elsewhere (Sallis et al., 2009a; Frank et al., 2007; Owen et al., 2007; Learnihan et al., 2011; Cerin et al., 2007) we found neighborhood composition to be more strongly associated with walking for transportation than walking for other purposes (exercise, leisure, and recreation). Nevertheless, Christian et al. (2011) recently found that using a land use mix variable that incorporated open space and vacant land, increased the predictive capacity of a traditional transport-walkability index to predict local recreational walking. Given that recreational walking is volitional rather than utilitarian, it may be that a supportive neighborhood built environment is necessary, but insufficient to increase recreational walking alone (Giles-Corti and Donovan, 2003). Population-wide interventions focused on cognitions and a supportive social environment might be needed to encourage people in all neighborhood types to undertake more recreational walking. One of the strengths of our study was the use of cluster analysis to determine neighborhood profiles. We used objective and transparent steps in identifying neighborhood clusters. For example, content knowledge of the Calgary urban form (Sandalack and Nicolai, 2006) was used to decide a priori the number of neighborhoods to be initially selected and then we confirmed the this structure by repeating the process using automated cluster selection. Previous descriptions of the Calgary urban form developed by Sandalack and Nicolai (2006) were based on expert knowledge and consensus and described the relative differences, rather than quantified differences, in general environmental attributes between neighborhood types. Our approach differs in that we used quantified measures of environmental attributes to identify homogeneous neighborhood clusters. The three neighborhoods differed on all internal variables used to derive the neighborhood clusters and external variables not used in cluster identification (i.e., inner vs. suburbs; grid, warped-grid, vs. curvilinear street pattern, and; year established). Furthermore, the observed associations between the identified neighborhood types and walking were consistent with previous knowledge regarding associations between the built environment and physical activity. Several limitations however, should be considered when interpreting these findings. The study’s cross-sectional design limits assessment of temporal causality. Despite adjusting for neighborhood self-selection, attitude towards walking, length of neighborhood tenure and socio-demographic characteristics the influence of unmeasured covariates on our results cannot be ruled out. The generalizability of our results was also influenced by the low participation rate and bias in the sample self-selection, although we have found that our sample was comparable to the Calgary population on several important demographic characteristics (McCormack et al., 2009b). As a result of our sample design (i.e., random cross-section of the Calgary metropolitan area) the uneven sample sizes across neighborhood groups reflected the geographical distribution of the Calgary population, with more

people residing in middle and outer neighborhoods (i.e., generally less walkable) than in inner-city neighborhoods (i.e., generally more walkable). The lower sample size in the high walkable neighborhood group might have reduced the precision of regression estimates compared with estimates for the moderate and low walkable neighborhood groups. Thus the reason for some modest associations between the high walkable neighborhood group and walking (i.e., participation in transportation walking, and minutes of recreational walking) were not found to be significantly significant. The self-reported physical activity measures are also subject to measurement error however, we did use reliable context-specific walking measures to reduce this source of error in our data. Our definition of sufficient walking (for transportation and recreation) does not reflect patterns of walking undertaken outside the neighborhood or participation in other types of physical activity. Thus among our respondents the achievement of sufficient levels of physical activity via walking outside the neighborhood or via other types of physical activity is possible. Moreover, despite our approach to the cluster analysis, the possible existence of other environmental attributes not included in the analysis could lead to the identification of different cluster structures. Consistent with other studies we defined respondents’ neighborhoods as walksheds with a 1.6 km street network radius, but environmental data not available at this level was supplemented with data from a more aggregate source (i.e., the neighborhood administrative boundary). However, we acknowledge that other neighborhood definitions exist often representing attributes measured at different neighborhood scales (e.g., administrative boundaries, census tracts, walksheds) and for different purposes (e.g., transportation, economic, and city planning) and that our definition may not reflect respondent’s own notions of neighborhood size. Our regression models estimating the association between neighborhood groups and walking behavior were adjusted for respondent clustering within neighborhood administrative areas. However, it should be noted that there were negligible differences between the coefficients and standard errors estimated from the cluster-adjusted models versus models that did not adjust for clustering (results not shown here).

5. Conclusion In conclusion, we found that neighborhoods with a highly connected pedestrian network, a large mix of businesses, high population density, high access to sidewalks and pathways, and high number of bus stops within walking distance supported participation in and time spent walking for transportation in the neighborhood, but not recreational walking. On average, residents of high walkable neighborhoods who reported some walking did around 30-min more of transportation walking per week, compared with their counterparts in low walkable neighborhoods. The extent to which the mix and size of public open space contribute to recreational walking was unclear in this study. Creating neighborhood built environments that are conducive to physical activity has the potential to improve population health. However, more research is needed to identify the combination and thresholds for environmental attributes necessary to increase walking.

Acknowledgements Canadian Institutes for Health Research (CIHR) funded the study. Career salary support provided by the Alberta Heritage Foundation for Medical Research (AHFMR) and the Canadian

G.R McCormack et al. / Health & Place 18 (2012) 1079–1087

Institute for Health Research (CIHR) to Alan Shiell and by AHFMR to Christine Friedenreich is gratefully acknowledged. Gavin McCormack was supported by a CIHR Bisby Postdoctoral Fellowship. Billie Giles-Corti is supported by an Australian NHMRC Principal Research Fellowship. References Adams, M.A., Sallis, J.F., Kerr, J., Conway, T.L., Saelens, B.E., Frank, L.D., Norman, G.J., Cain, K.L., 2011. Neighborhood environment profiles related to physical activity and weight status: a latent profile analysis. Preventive Medicine 52 (5), 326–331. Besser, L.M., Dannenberg, A.L., 2005. Walking to public transit: steps to help meet physical activity recommendations. American Journal of Preventive Medicine 29 (4), 273–280. Bow, C.J., Waters, N., Faris, P., Seidel, J., Galbraith, P.D., Knudtson, M., Ghali, W., 2004. Accuracy of city postal code coordinates as a proxy for location of residence. International Journal of Health Geographics 3 (1), 5. Cao, X., Mokhtarian, P., Handy, S., 2009. The relationship between the built environment and nonwork travel: a case study of Northern California. Transportation Research A-Policy 43, 548–559. Cerin, E., Leslie, E., du Toit, L., Owen, N., Frank, L., 2007. Destinations that matter: associations with walking for transport. Health and Place 13 (3), 713–724. Christian, H.E., Bull, F.C., Middleton, N.J., Knuiman, M.W., Divitini, M.L., Hooper, P., Amarasinghe, A., Giles-Corti, B., 2011. How important is the land use mix measure in understanding walking behaviour? Results from the RESIDE study. International Journal of Behavioral Nutrition and Physical Activity 8, 55. Coombes, E., Jones, A., Hillsdon, M., 2010. The relationship of physical activity and overweight to objectively measured green space accessibility and use. Social Science & Medicine 70 (6), 816–822. Duncan, M., Mummery, K., 2005. Psychosocial and environmental factors associated with physical activity among city dwellers in regional Queensland. Preventive Medicine 40 (4), 363–372. Foster, C., Hillsdon, M., Thorogood, M., 2004. Environmental perceptions and walking in English adults. Journal of Epidemiology and Community Health 58 (11), 924–928. Frank, L., Andersen, M., Schmid, T., 2004. Obesity relationships with community design, physical activity, and time spent in cars. American Journal of Preventive Medicine 27 (2), 87–96. Frank, L., Saelens, B., Powell, K., Chapman, J., 2007. Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Social Science & Medicine 65 (9), 1898–1914. Frank, L.D., Schmid, T.L., Sallis, J.F., Chapman, J., Saelens, B.E., 2005. Linking objectively measured physical activity with objectively measured urban form—findings from SMARTRAQ. American Journal of Preventive Medicine 28 (2 Suppl 2), 117–125. Giles-Corti, B., Broomhall, M.H., Knuiman, M., Collins, C., Douglas, K., Ng, K., Lange, A., Donovan, R.J., 2005. Increasing walking: how important is distance to, attractiveness, and size of public open space? American Journal of Preventive Medicine 28 (2, Supplement 2), 169–176. Giles-Corti, B., Donovan, R., 2003. Relative influences of individual, social environmental and physical environmental correlates of walking. American Journal of Public Health 93 (9), 1583–1589. Giles-Corti, B., Timperio, A., Cutt, H., Pikora, T.J., Bull, F.C., Knuiman, M., Bulsara, M., van Niel, K., Shilton, T., 2006. Development of a reliable measure of walking within and outside the local neighborhood: RESIDE’s neighborhood physical activity questionnaire. Preventive Medicine 42 (6), 455–459. Greenwald, M., Boarnet, M., 2001. Built environment as determinant of walking behavoir: anaylzing nonwork pedestrian travel in Portland, Oregon. Transporation Research Record, 33–42. Handy, S., Cao, X.Y., Mokhtarian, P.L., 2006. Self-selection in the relationship between the built environment and walking—Empirical evidence from northern California. Journal of the American Planning Association 72 (1), 55–74. Hoehner, C., Brennan Ramirez, L., Elliott, M., Handy, S., Brownson, R., 2005. Perceived and objective environmental measures and physical activity among urban adults. American Journal of Preventive Medicine 28 (2, Suppl 2), 105–116. Humpel, N., Owen, N., Iverson, D., Leslie, E., Bauman, A., 2004. Perceived environment attributes, residential location, and walking for particular purposes. American Journal of Preventive Medicine 26 (2), 119–125. Kaczynski, A., Henderson, K., 2007. Environmental correlates of physical activity: a review of evidence about parks and recreation. Leisure Sciences 29 (4), 315–354.

1087

Learnihan, V., Van Niel, K., Giles-Corti, B., Kniuman, M., 2011. Effect of scale on the links between walking and urban design. Geographical Research 49 (2), 183–191. Lee, C., Moudon, A., 2006. Correlates of walking for transportation and recreation purposes. Journal of Physical Activity & Health 3 (Supp 1), S77–S98. Lovasi, G.S., Moudon, A.V., Pearson, A.L., Hurvitz, P.M., Larson, E.B., Siscovick, D.S., et al., 2008. Using built environment characteristics to predict walking for exercise. International Journal of Health Geographics 7, 10. McCormack, G., Giles-Corti, B., Lange, A., Smith, T., Martin, K., Pikora, T.J., 2004. An update of recent evidence of the relationship between objective and selfreport measures of the physical environment and physical activity behaviours. Journal of Science and Medicine in Sport 7 (1 Suppl), 81–92. McCormack, G.R., Shiell, A., Doyle-Baker, P., Friedenreich, C., Sandalack, B., GilesCorti, B., 2009a. Testing the reliability of neighborhood-specific measures of physical activity among Canadian adults. Journal of Physical Activity and Health 6, 367–373. McCormack, G.R., Friedenreich, C., Shiell, A., Giles-Corti, B., Doyle-Baker, P.K., 2009b. Gender and age-specific seasonal variations in physical activity among adults. Journal of Epidemiology & Community Health 64, 1010–1016. McCormack, G.R., Rock, M., Toohey, A.M., Hignell, D., 2010. Characteristics of urban parks associated with park use and physical activity: a review of qualitative research. Health & Place 16, 712–726. McNally, M., Kulkarni, A., 1997. Assessment of influence of land use–transportation system on travel behavior. Transportation Research Record 1607, 101–115. Oakes, J.M., Forsyth, A., Schmitz, K.H., 2007. The effects of neighborhood density and street connectivity on walking behavior: the Twin Cities walking study. Epidemiologic Perspectives & Innovations 4, 16. Oliver, L.N., Schuurman, N., Hall, A.W., 2007. Comparing circular and network buffers to examine the influence of land use on walking for leisure and errands. International Journal of Health Geographics 6, 41. Owen, N., Cerin, E., Leslie, E., duToit, L., Coffee, N., Frank, L.D., Bauman, A.E., Hugo, G., Saelens, B.E., Sallis, J.F., 2007. Neighborhood walkability and the walking behavior of Australian adults. American Journal of Preventive Medicine 33 (5), 387–395. Pinjari, A., Bhat, C., Hensher, D., 2009. Residential self-selection effects in an activity time-use behavior model. Transportation Research B-Methodological 43, 729–748. Rosenbaum, P., Rubin, D., 1983. The central role of the propensity score in observation studies for causal effects. Biometrika 70 (1), 41–55. Saelens, B., Handy, S., 2008. Built environment correlates of walking: a review. Medicine & Science in Sports & Exercise 40 (7 Suppl), S550–S566. Sallis, J.F., Saelens, B.E., Frank, L.D., Conway, T.L., Slymen, D.J., Cain, K.L., Chapman, J.E., Kerr, J., 2009a. Neighborhood built environment and income: examining multiple health outcomes. Social Science & Medicine 68 (7), 1285–1293. Sallis, J.F., Bowles, H.R., Bauman, A., Ainsworth, B.E., Bull, F.C., Craig, C.L., Sjostrom, M., De Bourdeaudhuij, I., Lefevre, J., Matsudo, V., Matsudo, S., Macfarlane, D.J., Gomez, L.F., Inoue, S., Murase, N., Volbekiene, V., McLean, G., Car., H., Heggebo., L.K., Tomten, H., Bergman, P., 2009b. Neighborhood environments and physical activity among adults in 11 countries. American Journal of Preventive Medicine 36 (6), 484–490. Sandalack, B., Nicolai, A., 2006. The Calgary Project: urban form/urban life, Calgary: University of Calgary Press. Shay, E., Fan, Y., Rodriguez, D., Khattak, A., 2006. Drive or walk? Utilitarian trips within a neotraditional neighborhood. Transportation Research Record, 154–161. Spreeuwenberg, M.D., Bartak, A., Croon, M.A., Hagenaars, J.A., Busschbach, J.J., Andrea, H., Twisk, J., Stijnen, T., 2010. The multiple propensity score as control for bias in the comparison of more than two treatment arms: an introduction from a case study in mental health. Medical Care 48 (2), 166–174. Sugiyama, T., Francis, J., Middleton, N.J., Owen, N., Giles-Corti, B., 2010. Associations between recreational walking and attractiveness, size, and proximity of neighborhood open spaces. American Journal of Public Health 100 (9), 1752–1757. Sugiyama, T., Thompson, C., 2008. Associations between characteristics of neighbourhood open space and older people’s walking. Urban Forestry and Urban Greening 7 (1), 41–51. Tremblay, M.S., Warburton, D.E.R., Janssen, I., Paterson, D.H., Latimer, A.E., Rhodes, R.E., Kho, M.E., Hicks, A., LeBlanc, A.G., Zehr, L., Murumets, K., Duggan, M., 2011. New Canadian physical activity guidelines. Applied Physiology, Nutrition, and Metabolism 36 (1), 36–46. Wang, G., Macera, C.A., Scudder-Soucie, B., Schmid, T., Pratt, M., Buchner, D., Heath, G., 2004. Cost Analysis of the Built Environment: the case of bike and pedestrian trials in Lincoln, Neb. American Journal of Public Health 94 (4), 549–553. Wendel-Vos, W., Droomers, M., Kremers, S., Brug, J., van Lenthe, F., 2007. Potential environmental determinants of physical activity in adults: a systematic review. Obesity Reviews 8 (5), 425–440.