Table 2.

Three categories of RBEC assessment method categories and methods under each category, as well as data inputs, GIS data processing, spatial/spatiotemporal operations, data outputs, and counts of each method that has been applied by reviewed studies.

Category	Method	Data Inputs	GIS Data Processing	Spatial Averaging Operations	Data Outputs	# of Studies
Domain-based Exposure Assessment	Visual Inspection	Aerial images, TAGA point data	N/A	Manually assign each point with the designated BE domains by visually identifying its spatial relationships with image pixels that represent BE domains (e.g., home, park)	A database with built environment contextual domains that link to epoch-level physical activity outcomes	6
	Spatial Join	BE GIS layers, TAGA point data	1. construct GIS layers for specific domains (e.g., home, school, park)	Perform spatial join operation to assign each TAGA point with designated BE domains (e.g., home, park) if the point falls into polygon geometries that represent those domains in GIS	The same as above	34
	Hierarchical Domain Assignment	The same as above	1. perform trip identification and trip mode classification on TAGA point with data processing software (e.g., PALMsa); 2. the same as step 1 above	In a hierarchical order (e.g., home > school > transport > leisure > others), perform spatial join operation as described in Spatial Join method.	The same as above	13
Buffer-based Exposure Assessment	Point Buffer	The same as above	1. buffer TAGA point data by a pre-determined radius distance	Measure desired BE characteristics (e.g., walkability) within each point buffer.	A database with spatially-averaged built environment characteristics that link to epoch-level physical activity outcomes	14
	Trip Buffer	The same as above	1. the same as step 1 of Hierarchical Domain Assignment; 2. connect TAGA points that represent trips of interest (e.g., home/school commutes) into lines and generate buffer polygons along lines in GIS software	Measure desired BE characteristics (e.g., walkability) within trip buffers in GIS.	A database with spatially-averaged built environment characteristics that link to trip-level physical activity outcomes	3
Activity Space-based Exposure Assessment	Direct Path Area	The same as above	1. connect temporally-integrated (e.g., 1 day) TAGA points as lines based on timestamp sequences and aggregate the epoch-level PA outcome by the same temporal unit with GIS software; 2. buffer each trajectory by a distance and merge all buffers to generate direct path area polygons in GIS;	Measure desired BE characteristics (e.g., walkability) within activity space polygons in GIS.	A database with spatially-averaged built environment characteristics that link to day or any study period level physical activity outcomes	5
	Minimum Convex Hull	The same as above	1. execute the “minimal bounding geometry” operation in GIS to temporally-integrated (e.g., 1 day) TAGA points to generate minimum convex hull polygons and aggregate the epoch-level PA outcome by the same temporal unit	The same as above.	The same as above	3
	Standard Deviation Ellipse	The same as above	1. the same as the Minimum Convex Hull method above; however, in step 1, the “standard deviation ellipse” operation is executed to generate ellipse polygonsb	The same as above.	The same as above	2
	Kernel Density Estimation	GPS and Accelerometry datasets, BE GIS layers	1. create radius buffers around hostpots detected for each participant and average built environment characteristics within the buffer as RBECs exposure	1. Derive a list of all visits over the period made to each detected location (i.e. space-and-time peaks from GPS data) with their start and end times by applying the kernel density estimation method; 2. Implement a web-survey to obtain trip origins and destinations based on locations detected.	A database with time-weighted spatially-averaged built environment characteristics that link to day or any study period level physical activity outcomes	2
Total Counts						79c

Notes. BE=Built Environment, PA=Physical Activity, RBECs = Relevant Built Environment Contexts, TAGA point data = time-aligned GPS accelerometry point data

^aPhysical Activity and Location Measurement System (University of California San Diego, San Diego, CA, USA).

^bA ellipse polygon generated from the “Standard Deviation Ellipse” starts from the geographic centroid of the GIS points and covers one-standard deviation (68% of points) from the center, with major and minor axes determined by directional distributions of those points.

^cThe Hirsh et al. (2016) applied all three Activity Space-based methods and the Zenk et al. (2011) applied two Activity Space-based methods; therefore, the Total Counts were subtracted by 3 when adding up all counts.