Accounting for space, time, and behavior using GPS derived dynamic measures of environmental exposure

Abstract

Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.

Keywords: Activity space; Dynamic exposure; Human health; Movement; Spatial-temporal analysis; Time weighting; Wearable sensors.

Figure 1.

Analysis workflow for calculating time-weighted measures of environmental exposure across TWSA method, mobility data input, and environmental layer input.

Figure 2.

Time-weighted activity space surface calculated for one participant. Rows represent different TWSA methods (KDE, DR, PO) and columns represent activity data inputs (AllPoints, Stationary, InVehicle, Walking). Areas with darker red indicate greater weighted time spent in location during the study period for a particular data type.

Figure 3.

Zoomed-in time-weighted activity space surfaces of one participant with the AllPoints and InVehicle activity types. Cells with darker red indicate higher total exposure during the study period.

Figure 4.

Intraclass correlation coefficient (ICC) values by TWSA method and mean/standard deviation by mobility data input type (top) and environmental data layer type (bottom).

Figure 5.

Multidimensional scaling of participants’ exposure based on the cosine similarity considering the 12 exposure scenarios (3 mobility data types * 4 environmental data layers). Each point in the scatter plot represents one participant and is colored by the nearest neighbor index (NNI). Colors range from GPS data being clustered on NNI index (yellow) to dispersed (red). For KDE:mean (top left), a zoom-in of the main cluster is shown.