Public Open Space and Walking

Abstract

This study examined how proximity and attractiveness of public open spaces (POSs), perceptions of the surrounding built environment, and street configuration were associated with walking to and within POSs. Residents from three neighborhoods in Melbourne (N = 335) completed a questionnaire about walking and perceptions of their neighborhood, and geographic information systems and space syntax measures were used to assess proximity of POSs and street configuration. Proximity and attractiveness of POSs were not associated with POS-related walking. However, several perceptual qualities of the built environment, including safety from crime and traffic and aesthetics, were associated with greater walking. As well, persons living in areas with the most integrated street configurations reported less POS-related walking. Neighborhood perceptions and street configuration are key urban design issues to consider in promoting residents’ use of POS for walking.

Keywords

public open space walking proximity perceptual qualities street configuration built environment

Introduction

During the last decade, a burgeoning body of research, particularly in the fields of public health, transportation, and urban design, has examined the influence of the built environment on physical activity (PA). These studies have adopted an ecological approach (Sallis et al., 2008) and have documented associations between multiple types of PA and various aspects of the built environment, including residential density (Frank, Kerr, Chapman, & Sallis, 2007; Wilson et al., 2011), street connectivity (Boone-Heinonen, Popkin, Song, & Gordon-Larsen, 2010; Moudon et al., 2006), land-use mix (McConville, Rodríguez, Clifton, Cho, & Fleischhacker, 2011; Troped, Wilson, Matthews, Cromley, & Melly, 2010), neighborhood aesthetics (Inoue et al., 2010; Kaczynski, 2010), and presence of sidewalks (Alfonzo, Boarnet, Day, McMillan, & Anderson, 2008; Inoue et al., 2009).

It has also been shown that public open spaces (POSs) such as parks can provide a variety of physical and social benefits to individuals and communities (Bedimo-Rung, Mowen, & Cohen, 2005; Kaczynski & Henderson, 2008). POSs such as parks and playgrounds not only provide places where people can engage in PA such as walking, but they also can serve as interesting destinations that can persuade people to walk to reach them (Bedimo-Rung et al., 2005; Sugiyama, Francis, Middleton, Owen, & Giles-Corti, 2010). Indeed, a rapidly growing body of literature has examined different aspects of POSs in relation to PA (Aspinall et al., 2010; Giles-Corti, Broomhall, et al., 2005; Hino, Reis, Parra, Brownson, & Fermino, 2010; Kaczynski, Potwarka, & Saelens, 2008; Kaczynski, Potwarka, Smale, & Havitz, 2009; Sugiyama et al., 2010; Sugiyama & Ward Thompson, 2008; Timperio et al., 2008). However, this area of research has been limited in several important ways. First, most of the previous studies have considered only the impact of a single closest POS (e.g., Veitch et al., 2011; Witten, Hiscock, Pearce, & Blakely, 2008) and many have used subjective indicators for defining “neighborhoods” or “walking distance” to analyze the availability of POSs (e.g., Ries et al., 2009). These issues mean that the influence of having multiple POSs with different attributes near participants’ homes is ignored and also that differing interpretations of the study scale may produce some inaccuracy in analyzing the availability and influence of nearby POSs.

Second, most of the studies examining the relationship between POSs and PA have used a generalized PA measure such as total PA or total walking measured via electronic monitoring (e.g., accelerometer) or self-report (for an exception, see Kaczynski et al., 2009). This is problematic because it ignores the location of participants’ PA, and it is likely that a substantial amount of the total PA considered is unrelated to the POS exposure variables. Thus, when a “context-free behavioral outcome measure” of overall PA is used, relationships between proximity to or attributes of POS and the amount of PA participants engage in could be biased (Giles-Corti, Timperio, Bull, & Pikora, 2005).

Third, the role of self-selection in analyzing the impacts of POSs on PA has not been addressed in the majority of past research. Self-selection in this area of research refers to “the tendency of people to choose locations based on their travel abilities, needs, and preferences” (Litman, 2011, p. 8). For example, it is likely that people who prefer to walk choose to live in neighborhoods that are more convenient for walking. In such cases, demonstrating a causal relationship between the built environment and walking can be problematic. In a recent study, Kaczynski and Mowen (2011) reported that self-selection did not solely account for the observed associations between POS availability and PA. Nevertheless, if self-selection potentially exists in a study, but is not controlled for, estimates of associations between the built environment and PA could be biased.

Fourth, in most of the previous studies, participants were chosen from areas with similar built environment characteristics (e.g., Sugiyama et al., 2010) or they were randomly selected from the entire population without fully considering the physical characteristics of their locations (e.g., Giles-Corti, Broomhall, et al., 2005; Hillsdon, Panter, Foster, & Jones, 2006; Timperio et al., 2008). To assure sufficient variation in built environment characteristics, participants should be selected from heterogeneous environments (Giles-Corti, Timperio, et al., 2005). In parallel with this variety in the built environment, participants should be from homogeneous socioeconomic environments (unless socioeconomic status [SES] is a focus of the study). In other words, the sample must “maximize variability in contexts while minimizing variability in residents’ background characteristics” (Oakes, Forsyth, & Schmitz, 2007, p. 2).

Fifth, most research on parks and PA has largely focused on three factors related to the POS—proximity, attractiveness, and size—with less attention paid to perceptual characteristics of the surrounding built environment that encompasses the POSs and through which people must move to reach the POS. These characteristics not only moderate the influence of the three aforementioned POS factors on PA, they may also have a direct influence themselves on residents’ engagement in neighborhood PA. For example, many studies have reported that perceptions of environmental attributes such as aesthetics, safety from crime, traffic, and the availability of facilities for walking (e.g., sidewalks, trails) can affect the frequency and duration of walking (Duncan, Spence, & Mummery, 2005; Kaczynski, 2010; Owen, Humpel, Leslie, Bauman, & Sallis, 2004; Parra et al., 2011; Shigematsu et al., 2009; Wallmann, Bucksch, & Froboese, 2011).

Finally, and a key point related to the present study, the role of street design around POSs has not yet been examined in relation to residents’ walking to POS. Several studies in the broader built environment literature have explored the association between street configuration and different types of walking (Berrigan, Pickle, & Dill, 2010; Mecredy, Pickett, & Janssen, 2011; Oakes et al., 2007). However, street configuration has been mainly examined as street connectivity, which is defined as “the directness and availability of alternative routes from one point to another within a street network” (Transportation Research Board & Institute of Medicine, 2005, p. 104). Although valuable for some purposes, this idea is not completely representative of the street configuration concept.

Spatial configuration refers to the topology and relations among spaces, which take into account all other relations in a system (Marshall, 2005; Vaughan, 2007). Thus, street configuration is “a more complex idea than spatial relation, which invokes no more than a pair of related spaces” (Hillier, Hanson, & Graham, 1987, p. 363). Many measures have been applied to calculate the connectivity of streets in an area, including intersection density (Cerin et al., 2011), average block length (Mecredy et al., 2011), median block size of an area (Oakes et al., 2007), or link-node ratio (Gómez et al., 2010); for a full list of different measures, see Dill (2004) and Berrigan et al., (2010). However, these measures are limited to the “local and discrete” characteristics of streets and can be calculated without regard for how the streets in an area are connected together (Baran, Rodríguez, & Khattak, 2008). For example, two neighborhoods may have similar levels of street connectivity (e.g., intersection density, block size, etc.) but may have entirely different street patterns. In contrast, space syntax takes into account the topological dimension of the streets in an area and how they form a system that pedestrians and motorists must traverse in moving between destinations.

Addressing this limitation, a few recent studies have started to use space syntax theory to examine the influence of street configuration on specific context-based walking (Baran et al., 2008; Ozbil, Peponis, & Stone, 2011). Space syntax was introduced mainly in architecture studies about three decades ago by the work of Hillier and Hanson (1984) and is defined as “a set of techniques for the representation, quantification, and interpretation of spatial configuration in buildings and settlements” (Hillier et al., 1987, p. 363; for a review of this theory, see Bafna, 2003; Peponis & Wineman, 2002; Vaughan, 2007). Pedestrian movement was the initial focus of space syntax theory (Ratti, 2004), but it has been applied in a wide range of spatial aspects, including space and crime (Hillier, 2004), environmental cognition (Kim & Penn, 2004; Penn, 2003), and spatial segregation (Lima, 2001; Vaughan, 2007). Space syntax theory states that spatial configuration influences the distribution of movement within a network system and that when spaces are more directly connected to other spaces, this is likely to attract more movement (Peponis & Wineman, 2002). By taking into account spatial topological relations, using space syntax has great potential to explore how the street configuration of people’s locations can affect their specific-context walking.

In summary, the current study aims to build on the limitations of past research that were described above. Specifically, its purpose was to examine the influence of proximity to and attractiveness of POSs, perceptual qualities of the built environment surrounding POSs, and street configuration on walking to and within POS. Better understanding of how multiple contextual factors of park and neighborhood environments influence walking in a specific context can provide academics and professionals in urban design and parks and recreation with sound evidence on which to base future research and planning aimed at improving residents’ PA and health.

Method

Study Area

This study occurred in three diverse neighborhoods of Melbourne, Australia, in 2011. To ensure variety in the street configurations within participants’ neighborhoods, a street configuration typology developed by Marshall (2005) was used to select the study areas. Specifically, Marshall identified four types of street patterns, labeled A, B, C, and D, ranging from the “core of a settlement” to the “periphery.” Type A refers to the “core area of old cities,” Type B is a grid pattern, Type C is a “mixture of regularity and irregularity” and “streets typically curved or rectangular,” and Type D is a “hierarchical layout” associated with many cul-de-sacs. In this study, as shown in Figure 1, only the latter three patterns were included as Type A is absent in Melbourne because it was originally a “planned settlement” with a grid layout core. Each of the three neighborhoods included several Census Collection Districts, with the Type B neighborhood covering 35 hectares, the Type C neighborhood 23 hectares, and the Type D neighborhood 39 hectares.

Figure 1.

Areas from where participants were selected.

It has also been well-demonstrated that SES at the individual level (Burton & Turrell, 2000; Crespo, Ainsworth, Keteyian, Heath, & Smit, 1999; Janssen, Boyce, Simpson, & Pickett, 2006) and the area level (Kavanagh et al., 2005; Ross & Mirowsky, 2008; Rundle et al., 2008; Shishehbor, Gordon-Larsen, Kiefe, & Litaker, 2008; Yen & Kaplan, 1998) can influence PA such as walking. Therefore, to control the area-level SES in selecting the study areas, the socioeconomic index for area (SEIFA) index of advantages/disadvantages (Australian Bureau of Statistics, 2008) was applied. The selected areas with different street layouts were all selected from the lowest four deciles of the index. As well, all study areas were examined and excluded if they contained a physical barrier such as a freeway that could considerably affect participants’ travel behavior.

Data Collection

The geocoded addresses for all parcels in these three areas were obtained using the VICMAP address dataset (Department of Sustainability and Environment, 2009). The type of land use for individual parcels was checked using interactive maps provided online by the Victorian Department of Sustainability and Environment. Addresses of all nonresidential parcels were removed from the database and 330 households were then selected from each area (which included more than half of the number of households in each area). A self-administered questionnaire (labeled with an address code) and an enclosed postage-paid return envelope were sent to all 990 addresses in May 2011. Two weeks later, a reminder letter was sent to households that had yet to respond. In addition, an online version of the questionnaire was created and described within the reminder letter as an option for participating in the study. Finally, 1 month after the first questionnaire was sent, households that still had not yet responded were visited in person asking them to complete the questionnaire.

Measures

Walking to and within POS

The outcome measure in this study was a specific context-based type of walking—walking to and within POSs. The questions were modeled around those used in the International Physical Activity Questionnaire (Craig et al., 2003) and the Neighborhood Physical Activity Questionnaire (Giles-Corti et al., 2006) and asked about the frequency and duration of walking to and within POSs. Specifically, participants first reported the number of days within the past 7 days that they had walked to a POS like a park or playground within their neighborhood. Those who reported some walking to POSs were then asked the average amount of time spent doing so per day. The same two frequency and duration questions were then asked about walking within neighborhood POSs during the past 7 days. Neighborhood was defined for participants as the area within a 10-15 min walk from home.

POS attractiveness and proximity

In this study, the Open Space 2002 dataset (Australian Research Centre for Urban Ecology, 2003), which includes 14 types of POSs, including parks and playgrounds classified by the level of access (no public access, restricted public access, and full public access), was used to identify POSs. Across the three study areas, the predominant types of POS were parks or playgrounds as the other POSs like some small gardens or reserved lands were only less than 5% of all POSs.

Two types of factors related to the POS were examined—attractiveness and proximity. Attractiveness was deemed important to consider because several studies have shown that POS features such as landscaping, trees, water, and maintenance can influence POS usage (Giles-Corti, Broomhall, et al., 2005; Sugiyama et al., 2010; Timperio et al., 2008). Attractiveness can be measured objectively via on-site observation (e.g., Giles-Corti, Broomhall, et al., 2005), but such audits are time-consuming and expensive and do not capture study participants’ perceptions of nearby POSs. Alternatively, attractiveness can be assessed subjectively using residents’ evaluation of the quality of neighborhood POS. In this study, the latter method was used and participants were asked to rate their level of agreement with a single statement: ‘There are attractive POSs like parks and playgrounds in my neighborhood’ (1 = strongly disagree, 4 = strongly agree). Again, neighborhood was defined as a 10 to 15 min walk from home.

Three factors related to proximity to POSs were measured objectively through geographic information systems (GIS). Network distance, which is more appropriate than Euclidean distance (Apparicio, Abdelmajid, Riva, & Shearmur, 2008; Comber, Brunsdon, & Green, 2008; Nicholls, 2001; Witten, Exeter, & Field, 2003), was used to calculate the distance between each participant’s home and the nearest POS. In addition, a 1-km network buffer around each participant’s home was created to determine the number of POS and the total area of POS that fell into this catchment area. The 1-km buffer is similar to that used in several past studies of PA and parks or the built environment (Frank, Schmid, Sallis, Chapman, & Saelens, 2005; Kaczynski et al., 2009; Lee & Moudon, 2008; Lovasi et al., 2008). In addition, 1 km is likely to be a reasonable walkable distance and the area within 1 km from home usually is perceived by residents as a part of their walkable neighborhood (Lee, 2004; Moudon et al., 2006).

Characteristics of the neighborhood environment

Perceptions of characteristics of the neighborhood environment that can influence walking, including aesthetics, safety from traffic, safety from crime, and the availability of facilities for walking, were measured using related sections of an Australian version of the Neighborhood Environment Walkability Scale (NEWS-AU). The original NEWS is a self-report instrument that has demonstrated good reliability and validity in several past studies (Adams et al., 2009; Brownson et al., 2004; Cerin, Saelens, Sallis, & Frank, 2006; Saelens, Sallis, Black, & Chen, 2003). The NEWS-AU is a modified version developed by Leslie and colleagues (2005) for the Australian context and its validity has been reported by Cerin, Leslie, Owen, and Bauman (2008).

Two space syntax measures, local integration and control, were applied to analyze street configuration. Integration, shown in Figure 2, refers to “the average depth of a space to all other spaces in the system” (Klarqvist, 1993, p. 11). It is “a function of the mean depth (number of connections that must be traversed) if one were to move from every space . . . to every other space” on a network (Peponis & Wineman, 2002, P. 273). In other words, a traveler requires fewer turns to reach a highly integrated street, whereas less integrated streets (typically like cul-de-sacs) require more changes in directions to arrive at one’s destination (Kostakos, 2010). Integration can be calculated for each space; one calculation considers all the spaces in the system (global integration) and another is limited to a certain number of (usually three) neighboring spaces (local integration). The latter was considered in this study, as pedestrian movement is influenced more by the integration at a local scale (Ratti, 2004). As depicted in Figure 3, control refers to “the degree to which a space controls access to its immediate neighbours taking into account the number of alternative connections that each of these neighbours has” (Klarqvist, 1993, p. 12). For example, a street connected to several cul-de-sacs has to be moved through to reach those cul-de-sacs so it has high control (Kostakos, 2010). These measures are explained in detail elsewhere (Hillier & Hanson, 1984; Penn, Hillier, Banister, & Xu, 1998).

Figure 2.

Local integration measure of selected areas.

Figure 3.

Control measure of selected areas.

To measure integration and control corresponding to the addresses of the participants, axial maps of selected areas that accounted for all the spaces—vehicular and pedestrian—between buildings were constructed using UCL DepthMap software version 10 (developed by Alasdair Turner at University College in London) and then refined by hand. In this study, axial maps for the pedestrian network were developed, rather than for the vehicular network, as studies have shown that using a pedestrian network could increase the overall connectivity of a place, especially in newer, less-connected (e.g., cul-de-sac) neighborhoods (Chin, Van Niel, Giles-Corti, & Knuiman, 2008). The two space syntax measures were calculated for all axial lines in the three neighborhoods using Axwoman 4 (Jiang, Claramunt, & Klarqvist, 2000) as an extension in ArcGIS 9.2. Then, using a spatial join in ArcGIS 9.2, specific values for integration and control were assigned to each participant according to the street on which their home was located.

Sociodemographic variables

The questionnaire also captured several sociodemographic variables, including the participant’s age, gender, employment status, annual household income, and education level. In addition, two other items—the presence of children less than 12 years in the household and having a dog—were included as previous studies have demonstrated that these factors can influence one’s level of walking (Cutt, Knuiman, & Giles-Corti, 2008; Hull et al., 2010; Sjogren, Hansson, & Stjernberg, 2011).

Finally, to partially control for issues related to self-selection (i.e., a preference for living near POSs), an attitudinal measure was included in the questionnaire. Using a scale developed by Frank, Saelens, Powell, and Chapman (2007), participants were asked to rate the importance of several factors in their decision to live in their current neighborhood. This analysis used the variable ‘closeness to POSs like parks and playgrounds’ which was rated on a 5-point scale ranging from ‘not at all important’ (1) to ‘very important’ (5).

Analyses

Two types of regression models were used to analyze the data. First, the outcome variable, walking to or within POS, was dichotomized into some walking versus no walking and all participants were included in a binary logistic regression analysis to examine the factors associated with higher odds of any walking to or within POS. In addition, to identify factors associated with the amount of walking to and within POS, those participants who reported any walking were included in a linear regression model with the aggregate amount of walking to and within POS (minutes per week) used as the outcome variable. To moderate the effects of skewness, the total minutes values were normalized using a log10 transformation. All models were adjusted for several control variables, including all sociodemographic variables and responses to the self-selection measure. All analyses were conducted using SPSS 17.0 for Windows (SPSS Inc., Chicago, IL).

Results

From a total of 990 questionnaires that were mailed, 40 were undeliverable and 335 were returned (response rate = 35.3%). Of these, 257 (77%) were returned via mail, 36 (11%) were completed online, and 42 (12%) were garnered from in-person visits. In addition, 110 respondents (response rate = 35.6%) lived in the Type B neighborhood, 121 respondents (response rate = 37.7%) lived in the Type C neighborhood, and 104 respondents (response rate = 32.5%) lived in the Type D neighborhood. Of the 320 participants who provided usable data, 56% were female, the mean age was 44 years (SD = 15), 66% had completed a tertiary degree, 64% were employed, 39% had an annual income more than AU$80,000, 25% had a dog, and 26% had children under 12 years in the household. A total of 181 participants (57%) reported some weekly walking to or within POS. Characteristics of the sample according to those who engaged in some weekly walking to and within POS and those who did not are shown in Table 1. Among people who reported at least some walking to and within POS, the average walking time was 144 min per week (SD = 117 min), with a median of 120 min per week.

Table 1.

Characteristics of Study Participants.

Participant characteristics	Total number (%)	Some walking to or within POS (%)	No walking to or within POS (%)
Total sample	320	181	139
Gender
Female	180 (56)	97 (54)	83 (60)
Male	131 (41)	78 (43)	53 (38)
Age
18-24	21 (7)	12 (7)	9 (6)
25-34	81 (25)	34 (19)	47 (34)
35-44	75 (23)	48 (26)	27 (19)
45-54	60 (18)	36 (20)	24 (17)
55-64	43 (13)	27 (15)	16 (11)
65-74	21 (7)	13 (7)	8 (6)
>75	14 (4)	7 (4)	7 (5)
Education level
Did not complete year 12	21 (7)	12 (7)	9 (6)
Completed VCE	34 (10)	20 (11)	14 (10)
Completed trade or TAFE	49 (15)	33 (18)	16 (11)
Completed tertiary degree	211 (66)	113 (62)	98 (70)
Employment status
Employed	206 (64)	121 (67)	85 (61)
Unemployed	4 (1)	2 (1)	2 (1)
Retired	48 (15)	27 (15)	21 (15)
Student	33 (10)	16 (9)	17 (12)
Other	28 (9)	14 (8)	14 (10)
Annual income
<AU$20,000	13 (4)	8 (4)	5 (4)
AU$20,000-AU$40,000	29 (9)	15 (8)	14 (10)
AU$40,000-AU$60,000	47 (15)	25 (14)	22 (16)
AU$60,000-AU$80,000	56 (18)	32 (18)	24 (17)
>AU$80,000	124 (39)	69 (38)	55 (40)
Dog ownership
Yes	80 (25)	56 (31)	24 (17)
No	240 (75)	125 (69)	115 (83)
Child <12 years in household
Yes	84 (26)	62 (34)	22 (16)
No	232 (73)	117 (65)	115 (83)

Note: VCE = Victorian Certificate of Education; TAFE = Technical and Further Education.

Table 2 shows the results of the logistic regression analysis exploring factors related to engaging in at least some walking to or within POS (vs. no walking). None of the four POS factors—nearest POS (odds ratio [OR] = 1.00, 95% confidence interval [CI] = [0.99, 1.00]), number of POS within 1 km (OR = 1.01, 95% CI = [0.99, 1.03]), total area of POS within 1 km (OR = 1.00, 95% CI = [0.99-1.01]), or attractiveness of POS (OR = 0.92, 95% CI = [0.65, 1.30])—were significantly associated with an increased likelihood of walking to or within POS. Likewise, the self-selection variable measuring preferences for living near POS was not significant either (OR = 0.65, 95% CI = [0.33, 1.28]). With respect to perceptions of the surrounding built environment, safety from traffic (OR = 3.28, 95% CI = [1.43, 7.55]) was the strongest predictor of walking to or within POS (Table 2). In addition, safety from crime (OR = 2.17, 95% CI = [1.02, 4.61]) and aesthetics (OR = 2.17, 95% CI = [1.04, 4.52]) were both significantly associated with walking to or within POS, but neighborhood facilities for walking (OR = 0.65, 95% CI = [0.29, 1.44]) were not. Somewhat surprisingly, both street configuration measures—local integration (OR = 0.66, 95% CI = [0.46, 0.95]) and control (OR = 0.72, 95% CI = [0.56, 0.93])—demonstrated a significant negative association with engaging in at least some walking to or within POS (Table 2). Furthermore, among the sociodemographic variables, people living in a household with a child less than 12 years (OR = 4.62, 95% CI = [2, 10.66]) or who owned a dog (OR = 2.47, 95% CI = [1.10, 5.57]) were significantly more likely to walk to or within POS (data not shown).

Table 2.

Association of POS and Neighborhood Variables With Some Walking To or Within POS.

	Some walking to/within POS
	OR	95% CI
Self-selection
Closeness to POS	0.65	[0.33, 1.28]
Factors related to POS
Nearest POS	1.00	[0.99, 1.00]
Number of POS within 1 km	1.01	[0.99, 1.03]
Total area of POS within 1 km	1.00	[0.99, 1.01]
Attractiveness of POS	0.92	[0.65, 1.30]
Perceptual qualities of the surrounding built environment
Facilities for walking	0.65	[0.29, 1.44]
Aesthetics	2.17*	[1.04, 4.52]
Safety from traffic	3.28**	[1.43, 7.55]
Safety from crime	2.17*	[1.02, 4.61]
Street configuration
Local integration	0.66*	[0.46, 0.95]
Control	0.72*	[0.56, 0.93]

Note: OR = odds ratio; CI = confidence interval. Analysis controlled for sociodemographic variables (age, gender, employment status, income, education level, dog ownership, and children in household).

p < .05. **p < .01.

The multiple regression analysis examining only those participants who engaged in at least some walking to or within POS is presented in Table 3. Again, none of the four POS factors—nearest POS, number of POS within 1 km, total area of POS within 1 km, or POS attractiveness—or the self-selection variable were significantly associated with the total amount of walking to or within POS. However, there were significant, positive associations for two perceptual qualities of the surrounding built environment: facilities for walking (β = .20) and safety from crime (β = .23). Finally, control was the only street configuration variable significantly related to the amount of walking and in a negative direction (β = –.21). None of the sociodemographic variables were significantly associated with the total amount of walking to or within POS.

Table 3.

Association of POS and Neighborhood Variables With Amount of Walking To or Within POS.

	Amount of total walking to/within POS
	Standardized β	t value
Self-selection
Closeness to POS	−0.03	−0.33
Factors related to POS
Nearest POS	−0.08	−0.89
Number of POS within 1 km	0.12	1.54
Total area of POS within 1 km	−0.01	0.90
Attractiveness of POS	0.01	1.28
Perceptual qualities of the surrounding built environment
Facilities for walking	0.20*	2.16
Aesthetics	−0.16	−1.68
Safety from traffic	−0.17	−1.68
Safety from crime	0.23*	2.23
Street configuration
Local integration	−0.13	−1.26
Control	−0.21*	−2.12

Note: Analysis controlled for sociodemographic variables (age, gender, employment status, income, education level, dog ownership, and children in household).

p < .05.

Discussion

Parks are important settings for attracting active travel and for encouraging PA therein (Bedimo-Rung et al., 2005; Moody et al., 2004; Stanis, Schneider, & Pereira, 2010). However, research to date is limited on how characteristics of parks and the neighborhoods around them influence walking. Therefore, this study examined how multiple factors related to POSs, perceptual qualities of participants’ neighborhood environments, and street configuration were associated with participants’ levels of walking specifically to and within POS.

Factors Related to POS

The study showed three park proximity variables—nearest POS, number of POS within 1 km, and total area of POS within 1 km—predicted neither any walking nor the amount of walking to and within POS. These findings are consistent with some previous studies that reported proximity or distance to POS was unrelated to residents’ PA levels (Jilcott, Evenson, Laraia, & Ammerman, 2007; McGinn, Evenson, Herring, Huston, & Rodriguez, 2007; Witten et al., 2008). In contrast, many other studies have demonstrated an association between various measures of POS proximity and PA (Cohen et al., 2007; Frank, Kerr, et al., 2007; Giles-Corti, Broomhall, et al., 2005; Li, Fisher, Brownson, & Bosworth, 2005; Roemmich et al., 2006; Roux et al., 2007; Sugiyama et al., 2010). As discussed by Kaczynski et al. (2009), distance might influence behavior, but not in a linear function. For example, the effects of proximity to POS on a behavior like walking could start after a threshold distance. In our study, the maximum average distance to POS for participants was 651 meters and this relatively limited range may be one reason for observing a lack of a relationship between proximity to POS and PA. In addition, past studies have employed a variety of objective and subjective approaches in measuring proximity, which may contribute to inconsistencies across study findings. The current study applied objective measures in calculating proximity, and it is possible that disparate results would be observed if subjective measures were used. Indeed, it has been shown that the agreement between objective and subjective measures of the built environment is low (Arvidsson, Kawakami, Ohlsson, & Sundquist, 2012; Ball et al., 2008; Boehmer, Hoehner, Wyrwich, Brennan Ramirez, & Brownson, 2006; McCormack, Cerin, Leslie, Du Toit, & Owen, 2008) and they have different impacts on PA (Lin & Moudon, 2010; McCormack et al., 2008). Future researchers may wish to combine objective and subjective measures of proximity to POS to obtain more comprehensive results.

Attractiveness of POS also made no contribution in influencing walking to and within POS. This result is in contrast with previous studies that showed a significant association between the attractiveness or features of POS and walking or other PA behavior (Giles-Corti, Broomhall, et al., 2005; Kaczynski et al., 2008; Sugiyama et al., 2010). However, in the present study, residents’ perceptions were used to gauge the attractiveness of their neighborhood parks, rather than the research team collecting such information through observational audits. We chose to use a subjective measure to provide a novel perspective on how residents’ ratings of park attractiveness influence their walking to and within POS. As well, past research has shown that people have only limited knowledge of the proximity or attributes of local parks (Lackey & Kaczynski, 2009; Macintyre, Macdonald, & Ellaway, 2008; Spotts & Stynes, 1984). Therefore, simply adding physical features (e.g., courts, restrooms, landscaping) into existing POSs to increase attractiveness might not be helpful in improving people’s perceptions of those POSs and subsequently encouraging them to walk to and within these destinations. Future studies should examine how specific POS’s physical features influence residents’ knowledge of POS and their perceptions of POS attractiveness and how, in turn, this translates into increased walking to and within POS. Such research can contribute to the development of more effective policies and investment in equipping POSs with physical features that promote pride in local parks and use of them for PA.

Finally, issues related to self-selection did not appear to be a factor in our sample as participants’ preferences for living close to POSs were not associated with any walking or the amount of total walking to and within POS. Using a similar measure to ours, Kaczynski and Mowen (2011) reported that self-selection did not fully account for the relationship between park availability and PA, but future studies should more comprehensively examine such issues as they relate to POS and walking to or within POS specifically (for a comprehensive review of different methods of addressing self-selection, see Cao, Mokhtarian, & Handy, 2009; Mokhtarian & Cao, 2008).

Perceptual Qualities of the Surrounding Built Environment

Participants’ perceptions of several qualities of the surrounding built environment were significantly associated with any walking and with the amount of walking to and within POS. Consistent with previous evidence emphasizing the role of safety from crime in facilitating PA (Ellaway, Macintyre, & Bonnefoy, 2005; Harrison, Gemmell, & Heller, 2007), our study found this to be an important factor influencing the amount of residents’ POS-related walking. Perceived or objective danger from crime could prevent people from walking in public spaces such as streets or parks (Sallis & Kerr, 2006), and therefore, developing policies to improve safety from crime in POSs could positively influence people’s walking.

However, the contribution of other qualities was different in predicting some walking versus none and in increasing the amount of walking among those who did walk to or within POS. For example, facilities for walking had no effect on predicting some walking to and within POS, but did demonstrate a significant positive association with the total amount of walking. Carnegie et al. (2002) found that perceptions of practical features of the built environment are significantly related to levels of PA. For example, people who walk may have better awareness of their surrounding environment in comparison with those who do not walk within their neighborhood; the former group may, therefore, be more familiar with facilities and barriers for walking available within the built environment (McCormack et al., 2008). Consequently, it is likely that the importance of environmental factors influencing walking might differ for people who walk and those who do not, and thus new policies in encouraging walking should be developed to target both groups (e.g., physical improvements for people who are already somewhat active and promotion of available infrastructure for those who are not).

In addition, perceptions of safety from traffic and aesthetics were significantly related to engaging in some walking to and within POS, but not to the amount of total walking (among those who engaged in at least some). Jacobsen, Racioppi, and Rutter (2009), in their review of the literature of several fields (including public health, planning, and transportation), concluded that evidence shows real and perceived danger from traffic can negatively affect walking and bicycling. Likewise, Lee and Moudon (2008) found traffic volume was reported by their participants as the most significant barrier for walking and cycling. Other studies have shown aesthetics to be a key variable influencing walking and other PA in one’s neighborhood (Carnegie et al., 2002; Inoue et al., 2010; Kaczynski, 2010). Another recent study reported similar results to ours in that some key neighborhood perceptions, including aesthetics and safety, were associated with engaging in any recreational PA within the neighborhood, but not with the total amount of recreational neighborhood-based PA (Kaczynski, 2010). Consistent with that article, we might conclude that a positive perception of one’s neighborhood acts as a trigger to stimulate at least some activity among residents, but may not differentiate between those who are somewhat active and those who are very active. Nevertheless, more inviting neighborhoods—including those that are safe and aesthetically-pleasing—may facilitate at least some walking to and within POS among the large mass of sedentary population.

Street Configurations

Our results related to street configuration showed, interestingly, a significant negative impact of local integration and control on POS-related walking behavior. Specifically, people living on street segments that had high control and high local integration reported less walking behavior related to POS compared with people living on street segments with low control and low local integration. This result concurs partially with the only previous study to date that analyzed space syntax measures associated with specific types of walking (Baran et al., 2008). Those authors reported that local integration negatively influences leisure walking, but that residents on higher control segments showed more leisure walking.

There has been overwhelming evidence in the broader built environment literature supporting the idea that high street connectivity can increase PA such as walking (e.g., Badland, Schofield, & Garrett, 2008; Cleland, Timperio, & Crawford, 2008; Frank et al., 2005; Lee & Moudon, 2008; Pearce & Maddison, 2011; Saelens, Sallis, & Frank, 2003), though other studies have found no such association (Gómez et al., 2010; Mecredy et al., 2011). To analyze the role of street connectivity on walking, it is worthwhile to differentiate between various types of walking, because it has been shown that the significance of built environment factors could be different for the two main types of walking—walking for transport and walking for recreation (Cao, Handy, & Mokhtarian, 2006; Hoehner, Brennan Ramirez, Elliott, Handy, & Brownson, 2005; Humpel, Owen, Iverson, Leslie, & Bauman, 2004; Kaczynski, 2010; Lee & Moudon, 2006; Troped, Saunders, Pate, Reininger, & Addy, 2003). In their comprehensive review, Saelens and Handy (2008) found little or no evidence for an association between street connectivity and recreational walking.

Assuming that walking to and within POS is largely recreational in nature, our street configuration results are consistent with studies showing street connectivity has a negative effect on leisure walking (Oakes et al., 2007). High street connectivity can be helpful in walking for transport as people prefer to reach destinations as directly and quickly as possible, but it is likely that distance and time are not as important for people who want to walk for leisure. In addition, more connected areas inevitably encourage increased vehicular traffic that is dispersed across all streets in a neighborhood and which may have a negative effect on the safety and enjoyment of walking. Indeed, Leck’s (2006) meta-analysis found that more rigid grid patterns increased the probability for using cars.

More connected street patterns, such as the grid layouts of the core areas of many cities, have recaptured their popularity in walking-friendly urban planning. However, the practical evidence is insufficient to draw such a definitive conclusion about the superiority of such designs. Despite much debate admonishing cul-de-sac street patterns (see Southworth & Ben-Joseph, 2003), these patterns do not discourage all types of walking and have been shown to be at least somewhat successful in encouraging leisure or recreational walking. For example, the results of a recent study showed that children who live in a cul-de-sac spend more time playing in their streets (Veitch, Salmon, & Ball, 2010) and also devote less time to using computers or electronic games (Veitch et al., 2011). In addition, Handy, Cao, and Mokhtarian (2008) reported that cul-de-sacs were an important factor in encouraging outdoor play among children aged 6 to 12 years. Among adults, Oakes et al. (2007) reported increased odds of leisure walking in low connectivity areas. More connected layouts might be good for transport walking, but whether they can facilitate recreational PA, especially among adults, is still not clear and requires further study. Greater use of space syntax theory, which takes into account the topology of the street layout, has the potential to more comprehensively examine the relationship between street configuration and different types of walking.

Limitations

This study had several limitations. For example, like other cross-sectional studies that form the majority of the built environment and PA literature, we are unable to draw causal relationships between POSs and neighborhood attributes and behavior. Nevertheless, this study did attempt to partially control for the issue of self-selection. In addition, a self-reported measure of walking to and within POS could introduce some problems related to recall error (Sallis & Saelens, 2000). Another limitation was “reporting bias” [or “source bias” (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009)], which occurs when the same person is asked for both “exposure (built environment) and outcome (PA)” (Weiss, Maantay, & Fahs, 2010). For example, someone who engages in no walking in a neighborhood or POS may describe the neighborhood or POSs characteristics as in poor condition in an exaggerated way.

Furthermore, other measures of spatial configuration beyond integration and control are available to researchers and could form the basis of future analyses using space syntax principles and methods. For example, the location of POSs could be assessed based on the “step depth” distance from a participant’s address. Step depth calculates “the number of turns that it takes to see from one location to another location on the plan and represents how visually connected or isolated individuals are from one another” (Wineman, Kabo, & Davis, 2009, p. 432). A measure such as this would be interesting to incorporate, especially when the POS itself is considered a destination. Finally, although street configuration and POS proximity data were gathered objectively, participants’ subjective perceptions were used to measure POS attractiveness and other neighborhood attributes (e.g., safety, aesthetics) and our measure of attractiveness could be expanded beyond a single item in future research.

Conclusion

The current study adds to the limited, but rapidly expanding, body of research in urban design and public health examining associations between POSs and walking. In addition to factors related to POSs, our findings emphasize the role of perceptual qualities of the surrounding built environments that encompass POSs. Moreover, by considering the relationship between space syntax measures such as local integration and control and walking to and within POS, this study sheds light on the ways different street configurations can affect residents’ context-based walking. Future research should build on the present findings to continue to examine how diverse contextual properties of parks and neighborhoods can positively influence PA and health.

Footnotes

Acknowledgements

The authors would like to thank the Australian Research Centre for Urban Ecology for providing the Open Space 2002 dataset as its owner. In addition, they would like to express their appreciation of the Victorian Department of Sustainability and Environment for supplying GIS datasets under license to University of Melbourne. The authors wish to acknowledge the helpful comments of the editor and two knowledgeable reviewers.

Author’s Notes

This research was based on a part of the first author’s PhD thesis at the Melbourne School of Design, University of Melbourne.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Mohammad Javad Koohsari is a PhD candidate in urban design at the Melbourne School of Design, University of Melbourne. His research interests focus on the influence of urban design on health outcomes, with a specific emphasis on relationships between urban form, walking, and sense of community.

Justyna Anna Karakiewicz, BArch (Hon), AA Dip PhD MSAI RIBA FRSA, is an associate professor in urban design and coordinator of master of urban design at The Faculty of Architecture, Building and Planning, University of Melbourne. Her research interests include high-density development and parametric urbanism. She has published more than 50 papers, books, and book chapters.

Andrew T. Kaczynski is an assistant professor in the Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina. His research focuses on the role of the built environment and parks in facilitating physical activity and health among children and adults.

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