Children's physical activity and active travel: a cross-sectional study of activity spaces, sociodemographic and neighborhood associations

Figures & data

Table 1. AS based variables used in the study.

	Variable	Measurement method	Data*	Description
Space and environmental exposure	Area	Geometric measurement (m^2)	–	Captures the overall geographical extent of AS. (Kyttä et al. 2018)
	Total exposure	Measured as the sum of all pixel values from the exposure raster.	–	Captures the overall extent and magnitude of the AS. (Hasanzadeh, Laatikainen, and Kyttä 2018)
	Average exposure	Measured as the average of all pixel values from the exposure raster.	–	Captures the intensity of AS derived from active travel and frequency of travel. (Hasanzadeh, Laatikainen, and Kyttä 2018)
	Elongation	Measured as the length to width ratio of the smallest rectangle enclosing the activity space polygon.	–	Indicates the overall shape of the activity space. A highly elongated activity space may indicate a specialized activity space targeted at a certain location compared to a more balanced and diversely distributed AS resulting in lower elongation (Hasanzadeh, Laatikainen, and Kyttä 2018; Lord, Joerin, and Thériault 2009).
	Centricity	Counting the number of activity clusters yielded from a spatial cluster analysis and categorizing accordingly (Monocentric: one cluster around home; Bicentric: A second cluster beyond home surrounding; Polycentric: two or more activity clusters beyond home surrounding).	–	A high value for centricity indicates a highly polycentric AS whereas a low value indicates a monocentric AS concentrated around the individual's home. (Hasanzadeh 2019)
	Area of minimum convex polygon (MCP)	The area of the minimum convex polygon containing all activity locations was measured in m^2.	–	Captures the geographic extent of an individual's full action space.
Built environments	Greenspace	Measuring the percent of AS area that is in greenspace.	OSM land use data	Captures the percentage of the area that is green.
	LUM	The formula used to calculate the LUM was modified from the formula used by Frank et al. (2005):$H=-1\left(\sum _{i=1}^{n}pi\ast \ln (pi)\right)/\ln (n)$, where H is the LUM score, pi is the proportion of land use i among all land-use classes, and n is the number of land-use types.	OSM land use data	Higher mix of land use generally indicates a higher versatility and variety of destinations within the AS (Sallis et al. 2016). Included land use types: religious, commercial, entertainment, green, sport, transportation, services, education
	Intersection density	Average number of intersections per km^2	Land Information New Zealand	Widely used as an indicator of street connectivity.
	Cycling routes density	Average length of cycling routes per km^2	Auckland Transport Open GIS Data	Widely used as an indicator of cycling infrastructure availability.
	Population density	Average number of residents per km^2	Stats NZ	Population density is widely used in studies as a key characteristic of the built environment.

*other than data collected in the study.

AS = activity space, GIS = geographic information system.

Table 2. HLM results – AS.

			Area of AS (IREM)	Area of MCP	Elongation	Polycentricity	Average exposure	Total exposure
			β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
Built environment measures	Population density		−1.19 (−9.1, 1.3)	−0.64 (−3.9, 0.9)	−1.09 (−2.7, 3.1)	−1.79* (−2.1, 1.9)	−1.74* (−2.0, 7.9)	−1.02 (−7.3, 3.3)
	Greenspace (%)		1.33 (−3.7, 2.0)	0.93 (−1.2, 3.7)	1.03 (-.7, 2.1)	1.04 (−.1, 1.9)	1.55 (−.0, 1.9)	1.99** (0.4, 5.4)
	Cycling routes density		2.98 (1.2, 8.3)	2.63** (0.2, 5.7)	3.07** (1.9, 7.4)	4.51** (2.2, 11.3)	−1.02 (−4.7, 2.1)	1.85* (−5.3, 2.9)
	Land use mix		4.59** (1.7, 6.7)	2.36* (0.1, 2.7)	6.4** (4.3, 7.6)	4.7** (3.2, 6.1)	0.94 (−1.0, 1.23)	4.83** (1.4, 8.3)
	Intersection density		−3.96** (−8.4, −1.1)	−2.63** (−4.2, −1.3)	1.12 (-.5, 2.6)	−2.73** (−7.1, −1.1)	0.16 (−.0, .9)	−4.07** (−10.0, 3.5)
Travel behavior	Walking		−3.59** (−7.7, −1.0)	−3.49** (−6.7, −2.4)	−3.89** (−4.9, −1.1)	−4.52** (−7.2, −3.5)	13.46** (7.1, 18.3)	−2.39** (−3.3, .3)
	Cycling		−0.66 (−2.2, 1.7)	−1.98* (−3.7, −0.4)	−0.35 (−.9, 1.2)	−3.74** (−8.7, −1.5)	−0.35 (−.5, 1.1)	−0.48 (−2.5, 1.5)
	Car		4.24** (1.9, 9.9)	5.79** (3.1, 9.2)	4.13** (2.9, 6.9)	8.90** (2.8, 15.5)	−12.36** (−17.7, −4.9)	2.87*** (.7, 3.9)
Sociodemographic characteristics (H: Household -maternal)	Employment (H)		0.78 (−1.1, 1.8)	−0.08 (−1.2,1.7)	0.51 (−1.3, 1.7)	−2.8 (−4.2, 1.2)	1.54 (−.7, 3.9)	1.88* (−.1, 4.1)
	Education level (H)		0.72 (−.5, 1.7)	.57 (−1.1, 1.9)	−1.92* (−2.9, .6)	−1.70* (−3.2, .4)	0.78 (−2.7, 5.9)	0.69 (−2.1, 2.2)
	Gender (vs. female)		−0.06 (−1.3, 2.2)	−1.55 (−2.6, 2.1)	−1.77* (−2.3, 1.2)	1.23 (−3.4, 3.2)	0.65 (−.7, 1.1)	−0.06 (−16.8, 14.9)
	School year		4.90** (1.9, 9.1)	2.74** (1.6, 5.1)	5.45** (3.5, 7.1)	3.91** (2.9, 5.1)	4.88** (3.2, 6.1)	6.72** (3.1, 8.6)
	Ethnicity (vs. NZ European)
	Maori		−0.23 (−2.2, 3.1)	−1.28 (−3.1, 1.7)	0.22 (−.9, .6)	−1.34 (−2.2, 3.7)	1.49 (−.5, 1.6)	0.39 (−1.1, 2.7)
	Pacific people		−0.49 (−3.2, 4.1)	−1.52 (−3.3, 2.8)	1.12 (−1.9, 1.8)	−1.58 (−3.2, 2.6)	2.30* (.5, 3.8)	0.19 (−.1, .7)
	Asian		0.63 (−1.2, 3.3)	−.42 (−4.2, 3.1)	3.69** (1.0, 5.2)	−2.24* (−2.9, −.6)	1.34 (−.7, 2.2)	1.18 (−2.3, 3.7)
	MELAA		1.11 (−1.7, 5.7)	1.89 (−5.1, 2.7)	0.08 (−1.0, 1.1)	0.27 (−.4, .5)	−0.45 (−.9, 2.1)	0.45 (−5.1, 2.9)
	Other		−0.25 (−4.2, .8)	−0.51 (−2.0, 0.7)	−0.55 (−2.5, .5)	−1.18 (−2.4, 1.5)	1.47 (−.2, 3.6)	−0.05 (−1.8, 4.7)

* Significant at p < 0.05.

** Significant at p < 0.01.

Note: There is no multicollinearity between variables (VIF_max=  3.436, Tolerance < 1).

Table 3. HLM results – PA.

Category		MVPA (%)	Steps	Active travel to school	Active travel to destinations
		β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
Activity space measures	Population density	−3.10** (−6.3, −2.1)	−2.63** (−6.1, −1.2)	−3.9** (−4.1, −3.3)	1.8 (−0.9, 3.2)
	Greenspace (%)	2.18* (1.7, 4.3)	1.72* (−1.0, 5.3)	−2.8** (−3.3, −1.8)	1.1 (−2.9, −1.1)
	Cycling routes	2.20* (−.5, 4.5)	1.15 (−0.1, 1.5)	3.4** (2.0, 5.3)	2.69** (1.7, 6.9)
	LUM	−0.20 (−.4, .4)	−1.30 (−7.3, 5.6)	2.99** (2.1, 6.1)	−4.55** (−7.3, −2.2)
	Intersection density	1.20 (−1.2, 1.4)	−1.53 (−15.0, 2.6)	0.50 (−1.0, 2.3)	1.7* (0.5, 3.8)
	Elongation	0.76 (−.5, 2.5)	−1.14 (−7.3, 3.5)	−4.17** (−4.9, −3.6)	−2.6** (−3.9, −.5)
	Area	−1.15 (−2.8, 7.4)	−.31 (−.5, .5)	−4.13** (−5.4, −3.3)	−4.1 (−7.7, −3.1)
	Polycentricity	0.86 (−.39, 1.0)	0.29 (−.12.1, 13.3)	−1.67* (−2.1, 3.3)	−7.80** (−12.3, 1.1)
	Average exposure	4.06** (1.2, 7.3)	2.89** (1.2, 4.7)	1.95 (−0.0, 2.0)	12.26** (2.5, 15.7)
Sociodemographic characteristics (H: Household -maternal)	Employment (H)	0.15 (−.3, .6)	1.31 (−10.4, 11.2)	−1.11 (−2.0, 1.7)	1.5 (−2.1, 2.3)
	Education level (H)	−0.21 (−.3, −.7)	−0.99 (−5.9,1.3)	−0.05 (−.1, .06)	−2.84** (−3.3, 2.7)
	Gender (vs. female)	4.95** (3.1, 5.6)	5.43** (2.6, 6.7)	3.32** (2.1, 4.6)	1.0 (−1.1,3.4)
	School year	0.11 (−0.2, 0.4)	2.76** (0.5, 3.2)	−0.85 (−1.0, 1.2)	5.2** (−2.3, 2.4)
	Ethnicity (vs. NZ European)
	Maori	0.33 (−.9, 1.2)	0.80 (−.3, 1.3)	−1.68* (−3.0, 2.3)	−0.3 (−1.1, .6)
	Pacific people	−0.55 (−1.3, .7)	0.97 (−1.3, 1.0)	−0.22 (−.7, 1.9)	−0.7 (−2.1, 1.6)
	Asian	−2.16* (−2.7, 5.7)	−1.54 (−2.2, 1.3)	−1.7* (−1.9, 3.3)	−0.4 (−1.2, .6)
	MELAA	−1.1 (−4.9, .32)	0.05 (−.3, 1.7)	−1.37 (−2.0, .7)	−1.3 (−2.1, 1.6)
	Other	0.25 (−4.1, 6.9)	0.34 (−2.3, .5)	0.50 (−.7, 1.1)	−0.3 (−2.1, 1.1)

Note: There is no multicollinearity between variables (VIF_max = 3.007, Tolerance < 1).

Figure A1. AS modeling process.

Figure A2. An exemplary individual AS modeled by IREM (Hasanzadeh, Laatikainen, and Kyttä 2018; Laatikainen, Hasanzadeh, and Kyttä 2018).

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