Where Do Children Travel to and What Local Opportunities Are Available? The Relationship Between Neighborhood Destinations and Children’s Independent Mobility

Abstract

Associations between access to local destinations and children’s independent mobility (IM) were examined. In 2007, 10- to 12-year-olds (n = 1,480) and their parents (n = 1,314) completed a survey. Children marked on a map the destinations they walked or cycled to (n = 1,132), and the availability of local destinations was assessed using Geographic Information Systems. More independently mobile children traveled to local destinations than other children. The odds of IM more than halved in both boys and girls whose parents reported living on a busy road (boys, OR = 0.48; girls, OR = 0.36) and in boys who lived near shopping centers (OR = 0.18) or community services (OR = 0.25). Conversely, the odds of IM more than doubled in girls living in neighborhoods with well-connected low-traffic streets (OR = 2.32) and increased in boys with access to local recreational (OR = 1.67) and retail (OR = 1.42) destinations. Creating safe and accessible places and routes may facilitate children’s IM, partly by shaping parent’s and children’s feelings of safety while enhancing their confidence in the child’s ability to use active modes without an adult.

Keywords

children independent mobility destinations neighborhood

Introduction

Despite declines in recent decades (Harten & Olds, 2004; McDonald, 2007; McMillan, 2007), participating in active transport (i.e., walking or cycling for transport to destinations) contributes to children’s overall physical activity levels (Alexander et al., 2005; Cooper et al., 2006; Landsberg et al., 2008; Saksvig et al., 2007; Sirard, Riner, McIver, & Pate, 2005). Declines in active transport have been paralleled by a decrease in children’s independent mobility (IM), that is, active transport undertaken without adult supervision (Hillman, Adams, & Whitelegg, 1990; Holt et al., 2009; Pooley, Turnbull, & Adams, 2005). Restricting IM not only reduces children’s physical activity levels (Page, Cooper, Griew, & Jago, 2010; Wen, Kite, Merom, & Rissel, 2009) but also has the potential to influence their mental and social development. The evidence suggests that reduced IM in children may lower self-esteem (Sissons Joshi, MacClean, & Carter, 1999), impact spatial skills (e.g., distance estimation, locating north, spatial referencing skills), and decrease children’s opportunities to learn about their neighborhood (Rissotto & Tonucci, 2002). Furthermore, restricting children’s IM contributes to other negative environmental and health impacts by increasing parent’s motor vehicle use (Mackett, 2002) as they chauffeur their children to destinations (Ker & Tranter, 1997).

Children’s levels of IM are positively associated with active transport to and from school (Page et al., 2010). Although there is widespread research on journeys to school (Bringolf-Isler et al., 2008; Landsberg et al., 2008; van der Ploeg, Merom, Corpuz, & Bauman, 2008), fewer studies have explored journeys to other destinations (Fyhri & Hjorthol, 2009; Mackett, Brown, Gong, Kitazawa, & Paskins, 2007). This is surprising, given evidence suggesting that shops, friends’ houses, recreation areas, and sporting venues are popular destinations to which children travel (Hume, Salmon, & Ball, 2005; Mackett et al., 2007; Martin et al., 2009). Nevertheless, some suggest that there are few places that children are now permitted to travel to alone, or play (Jago et al., 2009; Veitch, Bagley, Ball, & Salmon, 2006), due to parental concerns about traffic safety (Giles-Corti, Kelty, Zubrick, & Villanueva, 2009; Timperio et al., 2006), strangers (Joshi, MacLean, & Carter, 1999; O’Brien, Jones, Sloan, & Rustin, 2000; Prezza & Pacilli, 2007; Valentine, 1997), and insufficient safe local places available for children (Veitch, Salmon, & Ball, 2008). Thus, there appears to be a need for studies exploring the range of factors influencing children’s IM to destinations other than school (Hillman et al., 1990; Jago et al., 2009; Johansson, 2006; Page, Cooper, Griew, Davis, & Hillsdon, 2009). Identifying the barriers and facilitators to children’s IM will assist in developing interventions aimed at encouraging children’s movement across their neighborhood.

Previous studies suggest that correlates associated with IM include age, proximity to destinations, support from peers, parents’ attitude toward IM, and parent and child fears about traffic, crime, and strangers, with some gender differences observed (Hillman, 1994; Jones, Davis, & Eyers, 2000; Kyttä, 1997; Prezza et al., 2001). For example, a number of studies have found that more boys than girls are independently mobile (Johansson, 2006; Page et al., 2009), with studies now exploring gender differences in correlates (Brown, Mackett, Gong, Kitazawa, & Paskins, 2008).

Although previous studies have explored some aspects of the built environment (e.g., proximity to destinations visited by children), few studies have considered whether the availability of local neighborhood destinations is associated with children’s IM (Kyttä, 2002). Findings of the relationship between the number and mix of local destinations, and physical activity, active transport, and IM are inconsistent (Alton, Adab, & Roberts, 2007; Carver et al., 2005; Mota et al., 2007; Zhu & Lee, 2009). U.S. studies using self-reported data suggest that children and adolescents with greater access (Evenson et al., 2006) and a mix of (Kerr, Frank, Sallis, & Chapman, 2007) local destinations are more likely to walk. In the United Kingdom and Italy, living near a park has been shown to be positively associated with IM (Mackett et al., 2007; Prezza et al., 2001). Conversely, 10- to 12-year-old Australian children who reported poor access to local parks, few sporting venues available, and limited public transport were less likely to walk or cycle to destinations at least three times weekly (Timperio, Crawford, Telford, & Salmon, 2004) than those who reported better access to facilities. However, other Australian (Carver et al., 2005), United States (Zhu & Lee, 2009), United Kingdom (Alton et al., 2007), and Portuguese (Mota et al., 2007) studies have found no such relationship. Studies to date have relied on child or parental report of the presence of destinations en route to school or near home (Zhu & Lee, 2009). No previous studies have examined associations between children’s IM and objectively measured availability of local destinations. Therefore, this study explores (a) the types of local destinations children travel to and (b) the correlates of boys’ and girls’ IM behavior, including access to objectively measured local destinations.

Method

This study was part of the TRavel Environment and Kids (TREK) project, a cross-sectional study examining the impact of the built environment on children’s active transport to and from school. Students and parents were sampled from schools in low and high walkable school areas across metropolitan Perth, Western Australia. A school-specific walkability index (SWI) was developed using Geographic Information Systems (GIS) software and was used to select schools. This study examined two types of travel: (a) visits to destinations by walking and cycling regardless of accompaniment status (herein referred to as “active transport”), which was assessed by a mapping activity, and (b) IM, which involved the child walking or cycling to a destination without an adult (measured by an index of child and parent reports of neighborhood activities/destinations visited independently). This study design is explained fully elsewhere (Giles-Corti et al., 2011) but explained briefly below. The University of Western Australia’s Human Ethics Committee provided ethics approval (RA/4/1/1394).

Recruitment of Schools and Participants

A SWI was developed and applied to all public primary schools across metropolitan Perth, (n = 238) using GIS software (Environmental Systems Resource Institute [ESRI], 2007). The index summed two measures: (a) network connectivity assessed by a pedshed measure (i.e., the walkable service area based on the formal pedestrian network up to 2 km in any direction from the school, divided by the area within a 2 km radius of the school [Chin, Van Niel, Giles-Corti, & Knuiman, 2008]); and (b) road volume exposure (i.e., road function hierarchy detailing average vehicles/day [Main Roads Western Australia, 2007], within 2 km of each school using the road and formal pedestrian networks [Giles-Corti et al., 2011]). The pedestrian network used in SWI analyses included formal (i.e., visible pathways) sidewalks and cut-throughs (Giles-Corti et al., 2011). Schools were ranked in terms of their walkability, with the most and the least walkable schools from within three area-level socioeconomic strata (i.e., low, medium, high; [Wood et al., 2010]) selected and invited to participate in a cross-sectional survey (n = 36). For each participating school (n = 25; 69.4% response rate), one class from each 5-, 6-, and 7-year group in each school was randomly selected for participation until a minimum of 30 children were recruited from each year (n = 2,617).

Data Collection

Between July and December, 2007, children completed a mapping activity and questionnaire during a 75-min class (n = 1,480; 56.5% response rate). Parents also completed questionnaire items (n = 1,314; 89.6% response rate). The questionnaires were assessed for test–retest reliability (1 week) among 160 10- to 12-year-old children and 101 parents recruited from four schools. Kappa (κ) and intraclass correlations statistics are reported in Table 1. The mapping activity was also piloted and modified where necessary to facilitate ease of use and readability.

Table 1.

Questionnaire-Derived Factors Included in Analyses.

Factor/subscale	Questionnaire items included in subscales	Reliability (κ or intraclass correlations)	Cronbach’s alpha
Perceived environment factors
Child can actively travel to the local shops without an adult^a,b	“I am sure that I could walk to the shop closest to my home without an adult present”; “I am sure that I could ride a bike to the shop closest to my home without an adult present”	0.74	0.86
Child perceives park closest to their house has fun or interesting things for them to do^b	—	0.56	—
Child perceives that it is safe to play at the park closest to their house without an adult^b	—	0.70	—
Parent perceives that home is located on a busy road^a,c,d	Which of the following best describes the location of your home? Responses = “On a highway”; “busy road”; “minor road (50 km/hr speed limit)”; “in a cul-de-sac”/”within a school zone (40 km/hr speed limit in school hours).”^eResponses were dichotomized (see table footnotes)	0.70	—
Parent perceives that their neighborhood is friendly^a,b	I often see adults walking in our neighborhood; “I often see children walking in our neighborhood”; “Our neighborhood is friendly”; “Our neighborhood is a nice place to walk around”	0.54	0.75
Parent perceives that their neighborhood road crossings are safe^a,b	There are no safe crossings for my child to use if he/she walked or cycled to the local shop’; “There are no safe crossings for my child to use if he/she walked or cycled to the closest park”	0.54	0.76
Social factors
Parent is confident in child’s ability to walk to the closest shop without an adult^b	—	0.64	—
Parent is not fearful of child’s personal safety in neighborhood^a,e	How fearful are you that if your child walked or rode a bike in your neighborhood without an adult he or she may; “be approached by a stranger”; “be taken by a stranger”; “be hurt by a stranger”; “be bullied by children the same age”; “be bullied by older children or teenagers”; “be injured in an incident when walking”; “be injured in an incident when riding a bike”; “be bitten by a dog”	0.83	0.92
Individual factors
Child has access to a bike at home to ride^c	—	0.73	—
Child perceives lots of children their own age to hang out within their neighborhood^c	—	0.73	—

Note: Child’s age (10, 11, 12 years), whether or not child was sick in the week prior to the survey (yes, no), maternal education (less than secondary education; secondary education/trade/diploma; bachelor’s degree or higher), socioeconomic status of school attended (low, medium, high), school clustering (n = 25) were adjusted for in analyses.

Subscale.

Likert-type scale: strongly disagree to strongly agree.

Yes, no.

Parent perceived that home is located on a busy road (yes, no): yes = on a highway, busy road; no = minor road, cul-de-sac, school zone.

Likert-type scale: not at all fearful to extremely fearful.

Mapping activity

The mapping activity collected information on the destinations children actively traveled to with, or without an adult. Children who reported walking or cycling to at least one local destination completed the mapping activity (n = 1,132, 76.5%), although only children who lived within 2 km of their school were used in analyses (n = 977).

Mapping has been shown to be an effective method to obtain this type of information (Hume et al., 2005; Rissotto & Tonucci, 2002; Veitch et al., 2006), with visual examples used successfully to assist children with mapping exercises (Veitch et al., 2006). One map was created for each school (n = 25) using GIS. Each map contained the school location, the local street network, and destinations (derived from the 2003 Western Australian Standard Land Use Codes [WASLUC]) within a 2 km radius around the school. Destinations were marked by symbols typically used in the 2007 Perth street directory (Western Australian Land Information Authority, 2006). Each child’s address was obtained from their parents and geocoded using ArcGIS v9.2 (ESRI, 2007).

The facilitator instructed children how to mark the maps, aided by the use of a laminated A2-sized map at the front of the class. Children marked an A3-sized (29 cm × 42 cm) black and white map and answered questions related to active transport to school (not reported here) and other local destinations. They used colored markers to indicate the location of their home, the route/s they walked/cycled to and/or from school, and destinations to which they walked or cycled. Destinations were coded on questionnaires and maps as P (parks/green space), S (shops), F (friends’/relatives’ homes), and T (other places; see Figure 1).

Figure 1.

Example of a typical school map.

Data Entry

Mapping activity data were digitized using GIS. To automate the digitization of destinations from paper maps to digital format, a customized TREK GIS Destinations Application was developed using ArcObjects. The application provided an interface displaying a list of pregeocoded destinations derived from three Western Australian destination data sources: (a) Sensis Pty Ltd. (i.e., the commercial Yellow Pages listings), (b) WASLUC (i.e., local land uses), and (3) the Centre’s Residential Environments Study Parks Database (Giles-Corti et al., 2008). All destinations within 4 km of the child’s home could be selected. Destinations marked on each child’s map were selected from the application database, but if not available (i.e., not pregeocoded), were digitized manually and added to the database. Once all destinations from a map were geocoded and selected, a geographic data set containing a child’s visited destinations was exported into a shapefile (i.e., stores shapes of nontopological geometry and attributes of spatial features as a set of vector coordinates in a data set). Shapefiles were merged to create a single destination layer containing all the destinations visited by children. The count and shortest distance to each visited destination and the count of available local destinations within 800 m of children’s homes were calculated using ArcGIS v9.3. These measures are described in more detail below.

Variables

A range of GIS-derived variables (i.e., objective environmental factors) and questionnaire-derived variables (i.e., perceived environmental, social, and individual factors) were used.

GIS-Derived Variables

Count of, and shortest distance to, visited destinations using active travel

It was felt that the pedestrian network would more likely reflect pedestrian movement (Chin et al., 2008) and was particularly appropriate for use in children’s studies as their mobility is more likely than adults to be affected by the design of their neighborhood. Thus, in addition to the formal pedestrian network used in SWI calculations (i.e., visible sidewalks and pedestrian access ways at the end of cul-de-sacs), the informal pedestrian network (i.e., paths through green space) was manually digitized and used to calculate count and proximity GIS measures. To facilitate digitization of the informal paths, the 2007 street network was used as the foundation, in conjunction with high-resolution aerial photographs (25 centimeter) (Western Australian Land Information Authority, 2007). Based on the formal and informal pedestrian networks, an origin–destination cost matrix was developed using the ArcGIS Network Analyst extension, to obtain the shortest distance from the child’s home (origin) to visited destinations (i.e., green space/parks, friends’/relatives’ houses, shops, other places marked on their maps). The number of visited destinations was also counted.

Count of available destinations within 800 m of a child’s home

“Available” destinations refer to local destinations available within 800 m of the child’s home (i.e., not only those visited). For each child, the following variables were generated for available child-specific destinations (i.e., destinations that a child could use) within an 800-m circular buffer of their home: (a) counts within specific destination categories (e.g., green space, retail shops, shopping centers etc.); and (b) shortest distance to the closest available green space/park (herein called green space), regardless of use. An 800-m circular buffer was used because parents have previously reported that a round trip of 1.6 km (i.e., 800 m one way) is a “walkable distance” for their child (Timperio et al., 2006). Using the Sensis destinations data, the count of utilitarian destinations (e.g., shopping centers, retail shops, food stores etc.) was generated using the ArcGIS Generate Near Table function in ArcToolbox. Green spaces within 800 m of each child’s home were manually digitized by drawing a polygon around the green space area using the 2007 Perth street directory and aerial imagery as guides. Points were generated at 25 m intervals around each polygon to represent its “access points.” The access point closest to the child’s house was identified and used to calculate the count to available green space, and shortest distance. All GIS-derived variables (n = 18 items) were imported into SPSS v17 to explore associations.

Questionnaire-Derived Variables

Independent mobility

To determine children’s IM behavior, an IM index was computed using questions from both the parent and child questionnaires. This index has been described elsewhere (Villanueva et al., 2012). In brief, children were asked whether they actively traveled to 15 local activities or destinations (excluding trips to school) in the week prior to the survey (no, sometimes, yes; “sometimes” indicates active transport to activities/destinations on some trips only, but not all trips). The activities (n = 6) included playing a team sport, swimming, going to a club or youth group, watching sport, music lessons, and catching a bus. The destinations (n = 9) included visiting a park, playground, or playing field; own friend’s house; family/family friend’s house; local shop; other shops; postbox; local library (not school library); movie cinema; Sunday school/church. Parents were asked about their child’s independent mobility to these 15 activities/destinations (yes, no). A score was computed by summing the activities/destinations that children traveled to and were allowed to visit independently. Possible scores could range from 0 (i.e., no independent mobility) to 15.

Perceived environmental, social, and individual factors were obtained from the student and parent questionnaires (n = 29 items). To reduce questionnaire items into common components, principal components analysis (PCA) with varimax or oblimin rotation (depending on how correlated the items were, i.e., component correlation matrix ≤0.3 = varimax, >0.3 = oblimin; Pallant, 2007) was performed on a total of 16 items (10 of which were reverse coded so that a higher score represented positive IM). The number of components were determined based on Eigenvalues >1, factor loadings >0.40 and on a single factor, and scree plots. Subscales derived from PCA analyses included (a) parent perception of positive neighborhood friendliness (4 items), (b) parent perception of safe neighborhood crossings (2 items), (c) parents’ lack of fear in their child’s personal safety in their neighborhood (8 items), and (d) child perception that they were confident in their ability to travel independently (2 items). Table 1 shows questionnaire-derived variables, including their test–retest reliability and Cronbach’s alpha scores where applicable.

Statistical Analyses

ArcGIS v9.3, SPSS v17, and Stata/IC 11.0 for Windows were used for analyses. Effect sizes (i.e., phi) and degrees of freedom (i.e., df) are reported for sample characteristics (Table 2). For descriptive analyses, total counts of and shortest and average distances from home to visited destinations were calculated (refer to Table 3). Bivariate comparisons between visited destinations and boys’ and girls’ IM status were examined using t tests and cross-tabulations. Multivariate logistic regression was used to calculate odds ratios associated with having some IM (outcome variable, Table 4). All models were adjusted for highest level of maternal education, the child’s school year, and whether or not the child was sick in the week prior to survey data collection. Standard errors were adjusted for clustering within schools using a robust variance estimation procedure. Given that others have documented gender differences in independent mobility (Mackett et al., 2007; O’Brien et al., 2000), analyses were stratified by gender. No multivariate analyses were conducted on friends’ houses based on the premise that a count of available friends’ houses for each child was not collected and cannot be deduced from available data.

Table 2.

Sample Characteristics.

	Gender
Sample characteristic (%)	Boys (n = 478)	Girls (n = 499)
Child’s age
10	31.4	28.9
11	36.6	38.9
12	32.0	32.3
SES of school
Low	27.0	29.9
Medium	34.9	32.9
High	38.1	37.3
School neighborhood walkability
Low	53.3	50.3
High	46.7	49.7
Sick in the last week?*
No	70.8	79.7
Yes	29.2	20.3
Maternal education
Less than secondary education	26.4	28.9
Secondary education/trade/diploma	57.9	55.3
Bachelor degree or higher	15.6	15.7
Independent mobility
No	17.9	22.8
Some	82.1	77.2

Note: SES = socioeconomic status.

p < .05.

Table 3.

Average Distances (m) to Visited Destinations According to Independent Mobility.

	Boys						Girls
	No IM (n = 72)			Some IM (n = 330)			No IM (n = 97)			Some IM (n = 328)
Destination type	% visited	Minimum distance (m, SD)	Average distance (m, SD)	% visited	Minimum distance (m, SD)	Average distance (m, SD)	% visited	Minimum distance (m, SD)	Average distance (m, SD)	% visited	Minimum distance (m, SD)	Average distance (m, SD)
Green space	80.6*	529.74* (459.22)	636.87 (523.54)	87.1*	401.44* (434.36)	622.06 (529.36)	76.3	452.52 (492.82)	858.83 (526.91)	89.3	385.44 (448.18)	865.73 (503.98)
Shops	72.2	953.30	1077.08	80.6	939.52	1111.13	73.2	1016.75 (464.09)	586.77 (523.49)	85.4	900.89 (553.76)	620.08 (535.45)
Friends houses	55.6*	648.00	879.82	75.5*	639.85	893.78	37.2	690.61 (704.75)	864.61 (691.83)	76.8	597.64 (634.65)	819.36 (648.66)
Other places^a	20.8	1881.61 (799.98)	1884.24 (798.77)	24.5	1505.69 (1107.96)	1637.16 (1120.12)	20.6	1206.11 (877.02)	1246.85 (964.16)	28.0	1609.42 (1065.01)	1688.67 (1032.70)
Any destination^b	100.0	434.87* (389.54)	897.43 (501.23)	99.7	302.52* (326.36)	890.24 (492.04)	95.8	415.38* (455.09)	858.83 (526.91)	99.3	286.92* (365.17)	865.73 (503.98)

Note: IM = independent mobility; m = meters.

Other places: library, BMX skate park, bike track, recreation center, daycare, other schools, river, lake/creek, church, community hall, postbox, beach, cubby house, youth group, graffiti alley, nursing home, caravan park, bush, bowling club, tennis courts, golf course, cemetery, quarry.

Any destination: total destinations (i.e., green space, shops, friends’/relatives’ houses, other places).

p < .05: comparison between IM status within sex; unadjusted results presented.

Table 4.

Association Between Environmental, Social, and Individual Factors and Child’s Independent Mobility in a Multivariate Logistic Regression Model.

		Boys		Girls
		OR	95% CI	OR	95% CI
Objective built environment factors
Walkability of school neighborhood	Low	1.00		1.00
	High	0.77	0.35-1.70	2.32*	1.17-4.59
Count of green space less than two acres	—	1.06	0.84-1.34	0.89	0.74-1.08
Count of green space two acres or more	—	0.93	0.83-1.04	0.94	0.84-1.04
Shortest distance to green space (m)	—	0.99*	0.99-1.00	0.99	0.99-1.00
Count of smaller food stores^a	—	1.19	0.80-1.77	1.11	0.76-1.63
Count of supermarkets	—	1.21	0.68-2.13	1.06	0.80-1.40
Count of shopping centers	—	0.18*	0.04-0.80	0.80	0.18-3.52
Count of fast food outlets	—	1.09	0.81-1.48	0.91	0.75-1.12
Count of churches, synagogues, temples	—	0.69	0.45-1.06	0.90	0.60-1.36
Count of recreation venues^b	—	1.67*	1.05-2.65	0.82	0.61-1.12
Count of government primary schools	—	1.19	0.94-1.50	1.15	0.90-1.47
Count of community services^c	—	0.25*	0.11-0.58	1.18	0.57-2.45
Count of retail shops^d	—	1.42*	1.11-1.83	1.08	0.93-1.27
Perceived environment factors
Our neighborhood road crossings are safe^e,f,g	—	1.33*	1.04-1.69	1.02	0.75-1.39
Our neighborhood is friendly^e,f,g	—	1.81*	1.07-3.04	0.91	0.54-1.54
Home is located on a busy road^e,f,g	No	1.00		1.00
	Yes	0.48*	0.24-0.96	0.36*	0.15-0.87
Not fearful of child’s personal safety in neighborhood^e,f,g	—	0.98	0.64-1.48	1.46	0.95-2.23
Child perceives that it is safe to play at the park closest to their house	No	1.00		1.00
	Neither	1.63	0.66-4.02	1.80	0.65-4.95
	Yes	2.03	1.05-3.65	2.67*	1.21-5.90
Child perceives that the park closest to their house has fun or interesting things for them to do	No	1.00		1.00
	Neither	0.94	0.35-2.57	1.28	0.44-3.77
	Yes	1.95*	1.05-3.65	1.13	0.54-2.34
Social factors
Confidence in child’s ability to walk to the closest shop without an adult^e,f	No	1.00		1.00
	Yes	2.52*	1.01-6.27	2.52*	1.61-3.95
Child perceives lots of children their own age to hang out within neighborhood	No	1.00		1.00
	Yes	1.48	0.87-2.50	1.18	0.71-1.97
Individual factors
I can actively travel to the local shops without an adult^f,g	—	1.34*	1.02-1.77	1.33*	0.99-1.78
Child perceives access to a bike at home to ride	No	1.00		1.00
	Yes	3.04	0.95-9.78	4.71*	1.55-14.33

Note: CI = confidence interval.

Smaller food store: bakery, ice cream, candy store, delicatessen, mini mart, convenience store.

Recreation: amusement centers (games), community halls/centers, recreation centers/indoor sports venues, dancing venues, martial arts venues, sports grounds, tennis courts, squash centers, tenpin bowling.

Community services: library, post office.

Retail shops: CD/DVD/video/games, book shop, crafts/stationery, gifts/novelties/souvenir, newsagents, pet shops, sports store, toys/hobbies.

Parent perception

Subscale (Likert-type scale 1 = strongly agree to 5 = strongly disagree)

Refer to Table 1 for full descriptions of items forming the subscale.

p<0.05 Adjusted for socioeconomic status (low, medium, high), age (10, 11, 12 years), maternal education (less than secondary education; secondary education/trade/diploma; bachelor degree or higher), whether or not child was sick last week (yes/no), school clustering (n = 25).

Results

Sample Description

There were no significant demographic differences by gender (Table 2). However, in the week preceding the survey, more boys (28.6%) than girls (20.4%) indicated that they were sick (p < 0.05; df = 1; ϕ = 0.103).

Independent Mobility

Children’s IM scores ranged from 0 to 10 (mean = 1.79, SD = 1.49). This level of IM for children was relatively low (i.e., mean IM score was 1.79 destinations/activities visited independently), although higher compared with a number of other studies (Johansson, 2006; Mackett et al., 2007; Mitchell, Kearns, & Collins, 2007). Independent mobility scores were dichotomized into a binary variable: 20.4% of children had no IM and 79.6% had some IM. More boys than girls were independently mobile, although not statistically significant (82.1% vs. 77.2%, p < 0.1; df = 1; ϕ = 0.061).

Visited Destinations

Table 3 examines whether IM children visited more local destinations than those without IM and presents the shortest and average distance that children with and without IM traveled to parks, shops, friends’/relatives’ houses, and other places. Although not statistically significant for all destinations, compared with those with no IM, a higher proportion of boys and girls with some IM traveled to local destinations (particularly to friends/relatives’ houses). Although not statistically significant, on average boys with IM traveled further than girls with IM to reach any destination (890 m vs. 865 m, respectively). Children accompanied by an adult (i.e., no IM) traveled 43% to 45% further than IM children to any destination, and independently mobile boys traveled shorter distances to a park than boys without IM (p = 0.043).

Environmental, Social, and Individual Factors Associated With Children’s IM

Table 4 shows the factors associated with children’s IM. If parents reported that they lived on a busy road, the likelihood of children’s IM decreased by 52% to 64% (boys, p = .039; girls, p = .023). However, if children (boys p = 0.038; girls p = 0.055) and their parents (boys, p = .047; girls, p = .000) were confident in the child’s ability to actively travel without an adult, their likelihood of IM more than doubled. Independent mobility was 42% to 67% higher in boys with more retail shops (p = .005) and recreation venues (p = .029) within 800 m of their home and 75% to 82% lower in boys with more local community services (p = .005) and shopping centers (p = .024). Boys’ IM was also positively associated with their parent’s perceptions of safe road crossings (p = .020) and a friendly neighborhood (p = .026) and their own perceptions that their local park had fun or interesting things to do (p = .035). However, irrespective of the type of destination category, density of destinations was not associated with girls’ IM. Rather, girls’ likelihood of IM more than doubled if they perceived that their local park was safe (p = .015), lived in a high walkable area (p = .016), and owned a bike (p = .006).

Discussion and Conclusions

This article examined the types of nonschool destinations visited by children, and the associations between objectively measured local destinations and children’s IM. Independent mobility was substantially lower in both boys and girls whose parents perceived that they lived on a busy road but more than doubled if parents were confident in their child’s ability to travel independently. To this end, neighborhood walkability was positively associated with IM among girls but not boys. In boys, the presence of specific local destinations were positively associated with IM (i.e., recreation venues and retail shops), whereas the presence of other larger scale destinations (i.e., shopping centers and community centers) were negatively associated. These results suggest that not all destinations necessarily encourage IM, and, irrespective of gender, real and perceived traffic dangers may reduce IM among children in this age group but particularly in girls. Clearly, parental perceptions of traffic danger combined with their child’s abilities to be independently mobile are major factors influencing whether children are afforded the right to be autonomous (Weir, Etelson, & Brand, 2006; Timperio et al., 2004). However, the built environment can influence these perceptions.

Consistent with previous studies, destinations such as parks, shops, and friend’s houses are common places to which children travel (Mackett et al., 2007; Veitch et al., 2008). However, it is also important to consider the availability of local destinations and the design of neighborhoods, as this may improve opportunities for children to be independently mobile. In this study, objective measures of the built environment were significantly associated with children’s IM, although there were important gender differences. Access to recreational destinations (e.g., recreation centers, grounds, dance venues, etc.) and retail shops increased the likelihood of IM among boys, but not girls. This is likely to reflect the fact that boys have more freedom to be independently mobile than girls as repeatedly shown in previous studies. For instance, compared with girls, boys are more likely to be independently mobile (Johansson, 2006; Mackett et al., 2007; O’Brien et al., 2000; Page et al., 2009), travel independently more frequently (Mackett et al., 2007), have a larger territorial range (Matthews, 1987; van Vliet, 1983; Webley, 1981), and are allowed to do more local activities or errands (Mackett et al., 2007). Thus, as boys become more independent, access to local destinations may become important for facilitating IM, as appears to be the case in this study. Indeed, this study also showed that more independently mobile children traveled to local destinations than their nonindependently mobile peers, albeit not a significant finding for all.

With some exceptions (Alton et al., 2007), child and parent-reported presence of neighborhood destinations such as parks, play areas, sporting, or recreation venues are generally positively associated with children’s active transport (Carver et al., 2005; Evenson et al., 2006; Kerr et al., 2007; Timperio et al., 2004). For example, Carver et al. (2005) found that boys, but not girls, were more likely to cycle if their parents perceived the presence of good sporting facilities nearby. We found that objectively measured recreational destinations were associated with only boys’ IM.

Notably, while the presence of local retail shops was positively associated with boys’ IM, the converse was true for shopping centers. This latter finding may reflect the fact that shopping centers are usually a conglomeration of shops surrounded by busy roads, which attract both more vehicular traffic and strangers into local areas. The presence of traffic is repeatedly shown to be a barrier to children using active modes and being IM (Giles-Corti et al., 2009). In this study, living on a busy road was negatively associated with IM in both boys and girls. Conversely, parent perceptions of neighborhood friendliness and the presence of safe road crossings increased the likelihood of boys’ IM. Therefore, although the presence of certain local destinations may facilitate children being independently mobile, the presence of destinations associated with significant vehicular and pedestrian (e.g., strangers) traffic (e.g., shopping centers) may enhance parental concerns about neighborhood safety, which in turn may facilitate reluctance to allow their children to travel without an adult and lead to parents restricting their children’s IM. Indeed, lower traffic exposure (Carlin et al., 1997; Giles-Corti et al., 2011; Timperio et al., 2006; von Kries, Kohne, Bohm, & von Voss, 1998) and connected street networks have generally been shown to positively influence 9- to 11-year-old children’s active transport (Bejleri, Steiner, Provost, Fischman, & Arafat, 2009; Boarnet, Andersen, Day, McMillan, & Alfonzo, 2005; Braza, Shoemaker, & Seeley, 2004; Falb, Kanny, Powell, & Giarrusso, 2007; Kerr et al., 2006; Mota et al., 2007). Connected street networks increase proximity to local destinations, thereby reduces the distance children travel to a destination. Living in a highly walkable neighborhood characterized by low traffic and high street network connectivity was positively associated with girls’ IM in this study. Such a neighborhood may overcome parental concerns about traffic safety (Timperio et al., 2004; Weir et al., 2006).

Although numerous studies have suggested that perceived and objectively measured short distances to destinations and parks are positively associated with both active transport (Ewing, Schroeer, & Greene, 2004; Falb et al., 2007; Larsen et al., 2009; Page et al., 2010; Pont, Ziviani, Wadley, Bennett, & Abbott, 2009; Timperio et al., 2004) and IM (Fyhri & Hjorthol, 2009; Prezza et al., 2001; van Oel, n.d.; Zwerts, Allaert, Janssens, Wets, & Witlox, 2010), other factors may also influence children’s IM. In this study, perceptions about the quality of the green space appeared to be of equal or greater importance. Girls’ IM increased if they perceived their closest green space as safe, whereas for boys, their perception that their closest park had fun or interesting things to do increased their likelihood of IM. Thus, proximity of destinations may be necessary but not sufficient. For example, children (and their parents) may perceive other less proximate parks to be safer or more appealing. Veitch et al. (2006) found that children did not necessarily visit the closest green space. Rather, they visited parks with the most appealing aesthetics and attributes. These findings highlight the importance of combining various built environment attributes to facilitate IM. These results suggest that designs that support children’s IM require both safe routes with connected networks and nearby safe and appealing destinations.

Independent of other factors, children’s and parents’ confidence in the child’s ability to travel independently was positively associated with both boys’ and girls’ IM. Increasing children’s traffic safety competence, including their skills and ability to negotiate traffic and identify safe and unsafe places, may enhance parental trust (Johansson, 2006), confidence (and competence), which may then encourage more IM. Although there is a need to create safe streets and places that are conducive for children’s autonomy around their neighborhoods, these findings highlight the importance of influencing parents’ (and children’s) views about the likely safety of children doing so. The results also highlight the importance of involving parents, children, schools, and the community in developing programs that promote IM, safety, and self-efficacy (e.g., bicycle education classes, Neighborhood Watch programs, and walking school bus programs).

This study also has several limitations. The walkability index of the school neighborhood was used as a proxy for the child’s neighborhood walkability. Although the majority of children’s homes were located near their school, measurement error may have been introduced, particularly for those living on the edge of the school neighborhood. Moreover, although objective measures provide potentially unbiased data, existing data sets from which the destination data were obtained may be inaccurate and incomplete. Few studies, for example, have validated the use of GIS data sets (Bader, Ailshire, Morenoff, & House, 2010; Boone, Gordon-Larsen, Stewart, & Popkin, 2008; Hoehner & Schootman, 2010; Paquet, Daniel, Kestens, Léger, & Gauvin, 2008).

Simply increasing the presence of local destinations in neighborhoods may not result in increases in children’s IM, and in some cases, the presence of large-scale destinations that also attract substantial traffic and strangers may reduce children’s IM. Rather, a comprehensive range of environmental interventions are required, including designing safe neighborhoods with less traffic and proximate destinations, as well as safe routes and places that are both appealing and comfortable for children. At the same time, education programs are required to increase children’s skills to safely navigate their neighborhoods. Together these interventions should be important in shaping parent’s and child’s feelings of safety while enhancing their confidence in the child’s ability to use active modes without an adult.

A multisector approach is required to develop strategies that ensure children have access to safer neighborhoods, aesthetically pleasing places, and the necessary skills to explore their neighborhood safely and independently. More research is required to explore the mix of destinations required to encourage IM and the attributes of parks and other destinations that are appealing to children and make them feel safe. Nevertheless, this study’s findings have several research and policy implications, and these are summarized in the Online Appendix (at http://eab.sagepub.com).

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this project is from the National and Medical Research Council (#403933). Walking WA is the industry partner on this project and the input of members of this Committee is gratefully acknowledged, particularly Alice Haning (Department of Transport) who has supported the project. Spatial data based on information provided by and with the permission of the Western Australian Land Information Authority was used, with the access to the data provided by the Department of Planning. This research was also supported by Sensis Pty Ltd for providing access to destination data obtained from the Sensis (Yellow Pages) database. Bridget Beesley is gratefully acknowledged for developing the TREK GIS Destinations Application for the purpose of digitising destinations marked in TREK mapping activity. Karen Villanueva was supported by scholarships provided by an Australian Postgraduate Award (APA) and Ad Hoc Scholarships; Georgina Trapp was supported by an APA and NHMRC Capacity Building Grant (#458668); Billie Giles-Corti is supported by a NHMRC Principal Fellow Award (#1004900); Anna Timperio by a VicHealth Public Health Research Fellowship (2004 0536); and Gavin McCormack by an Alberta Heritage Foundation Award for Medical Research Postdoctoral Fellowship.

Author Biographies

Karen Villanueva is a PhD candidate at The Centre for the Built Environment and Health, The University of Western Australia. Her research focuses on the influence of the built environment on children’s independent mobility and activity spaces.

Billie Giles-Corti, PhD, is the director of the McCaughey Centre, the VicHealth Centre for the Promotion of Mental Health and Community Wellbeing, at the School of Population Health, University of Melbourne, and formerly the director of the UWA Centre for the Built Environment and Health. Her research focuses on the impact of urban design on health and health behaviors.

Max Bulsara, PhD, is the chair in Biostatistics at the Institute of Health and Rehabilitation Research, University of Notre Dame. He has been involved in epidemiological studies for over two decades both in Zambia and in Australia. His research includes many local, Australian, and international projects in fields such as Public health, Endocrinology, Gastroenterology, Hepatology, and General and Internal Medicine.

Anna Timperio, PhD, is a senior lecturer in the Centre for Physical Activity and Nutrition Research, Deakin University. Her research focuses on understanding contextual influences on physical activity, eating behavior, and obesity.

Gavin McCormack, PhD, is a postdoctoral research fellow at the Population Health Intervention Research Centre, University of Calgary. His research focuses on the relationships between the physical and social environments and health among children and adults.

Bridget Beesley is a senior GIS analyst at the Centre for the Built Environment and Health, School of Population Health at The University of Western Australia. Her research focuses on the application of GIS to the impact of urban design and place on health and physical activity in children and adults.

Georgina Trapp, BHSc (Hons), is a PhD candidate and research associate at the Centre for the Built Environment and Health, School of Population Health, The University of Western Australia. Her research interests are situated in the field of public health and include adult and child obesity, nutrition, and active commuting to school.

Nicholas Middleton is a research associate with the Centre for the Built Environment and Health at the University of Western Australia, specializing in Spatial Analysis. Nick develops spatial metrics to describe the urban environment to examine its relationship to physical activity and impacts on mental and physical health.

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