Table 3.

Overview of key strengths and limitations related to neighborhood delineation and use of GIS, GPS, and PPGIS in articles included and examples of literature identified.

Topic/issue	Strengths (with examples of studies)	Limitations (with examples of studies)
Neighborhood delineation	Multiple buffer distances explored (Boone-Heinonen et al., 2010a; Carlson et al., 2017; Buck et al., 2015; Carver et al., 2015; van Loon et al., 2014) Multiple neighborhood delineations explored (e.g., use of multiple buffer distances and both simple and kernel intensity approaches (Buck et al., 2015)), child-reported destinations of importance, objective GIS-estimated measures, and shortest distances to destinations (Kyttä et al., 2012), simultaneous consideration of activity spaces and traditional neighborhood buffer boundaries, school-specific walkability measure (Villanueva et al., 2012)	Inconsistent use of multiple buffer distances did not allow for comparability (Nordbø et al., 2019; Boone-Heinonen and Gordon-Larsen, 2011; Boone-Heinonen et al., 2010a, 2010b) Issues with studies examining both home and school environments (e.g., unclear how school and home neighborhoods were combined, and whether there was any consideration of overlap between home and school neighborhoods (Tucker et al., 2009)), some crossover (6%) in school buffers when using an 800 m buffer (Jauregui et al., 2016), and potential overlap and collinearity between home and school neighborhood exposures for children who lived close to school (Burgoine et al., 2015) Activity space calculation did not account for trip frequency and/or duration (Villanueva et al., 2012) Minimum convex polygons used for activity space calculation do not use pedestrian network measures (Villanueva et al., 2012)
Temporal alignment of data	GIS environmental measures calculated at each time point (Boone-Heinonen and Gordon-Larsen, 2011; Boone-Heinonen et al., 2010a)	Temporal mismatch between interview/survey/outcome data and GIS data (Nordbø et al., 2019; Boone-Heinonen and Gordon-Larsen, 2011; Boone-Heinonen et al., 2010a; Carroll-Scott et al., 2013) Longitudinal outcome data but GIS variables at baseline only (unclear if participants had moved) (Carver et al., 2010)
GIS databases	Verified food outlet type where necessary by phoning businesses and conducting store visits (Burgoine et al., 2015)	Limited access to detailed green space GIS data (Bringolf-Isler et al., 2010) Spatial differences between time points (e.g., shifts in census boundaries over study measurement periods (Boone-Heinonen and Gordon-Larsen, 2011; Boone-Heinonen et al., 2010a))
GPS studies	Sensitive and accurate data collected on individual routes to school (Burgoine et al., 2015; Dessing et al., 2016; Helbich et al., 2016) or neighborhood locations visited (and time spent in these) (Carlson et al., 2017; Olsen et al., 2019)	Although GPS used to measure time in locations, buffer distances were estimated rather than actual exposure (Carlson et al., 2015, 2017; Burgoine et al., 2015) Multi-destination tracks were not accounted for in analyses (Dessing et al., 2016)
Data analyses (relevant to GIS/neighborhood environment measures)	Neighborhood exposure considered (e.g., length of residence in the neighborhood included as a covariate (Hinckson et al., 2017); child needed to have lived in the study area for at least 1 year in order to-participate (Moran et al., 2017; Islam et al., 2014), authors measured duration of residence in the-neighborhood, with approximately 70% having lived in the neighborhood for 5 years or more (DeWeese et al., 2018)) Neighborhood self-selection considered (Boone-Heinonen et al., 2010a; Oliver et al., 2014) Statistical adjustment for macrolevel walkability (Sallis et al., 2015; Cain et al., 2014) Stratification of analyses by socio-demographic factors (Boone-Heinonen and Gordon-Larsen, 2011; Carver et al., 2010; Olsen et al., 2019; Buck et al., 2015; van Loon et al., 2014)
Study design (relevant to GIS/neighborhood environment measures)	Heterogeneity in environmental characteristics likely due to stratified recruitment of households (Carlson et al., 2014, 2015, 2017) (albeit noting that in one study although stratified recruitment in higher and lower-socio-economic status neighborhoods occurred, education levels of parents were generally high, likely reflecting education levels of the university city (De Meester et al., 2012)) Environmental variability including low density, single family housing neighborhoods to mixed use and medium-high density neighborhoods characterized by a range of apartment type housing (van Loon et al., 2014) Inclusion of urban and rural areas (Carver et al., 2015)
Specificity of measures/methods	Separate analyses conducted by transport mode (Dessing et al., 2016). Cyclist and pedestrian-specific variables calculated (but did not analyze separately by travel mode due to low cycling numbers) (Smith et al., 2019) Multiple addresses: Excluded children living in postseparation families to make certain that the child lived at the actual address used for the calculation (although this would have increased specificity it may introduce unanticipated bias through excluding a participant group) (Nordbø et al., 2019) Participants living further than 2 miles (Carlson et al., 2014) or 2 km (Villanueva et al., 2012) from their school were excluded (albeit this will produced biased results towards those living close to school)	Walkability index of the school neighborhood used as a proxy for neighborhood walkability (Laxer and Janssen, 2013; Villanueva et al., 2012)

GIS = geographic information system, GPS = global positioning system.