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

• Published in: Health & place 2023-01, Vol.79, p.102706-102706, Article 102706
• Main Authors: Jankowska, Marta M., Yang, Jiue-An, Luo, Nana, Spoon, Chad, Benmarhnia, Tarik
• Format: Article
• Language: English
• Subjects: Activity space; Dynamic exposure; Environmental Exposure; Geographic Information Systems; Human health; Humans; Movement; Spatial Analysis; Spatial-temporal analysis; Time weighting; Walking; Wearable sensors
• ISSN: 1353-8292; 1873-2054
• DOI: 10.1016/j.healthplace.2021.102706

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. •Time-weighted spatial averaging exposures vary with mobility and environmental data inputs.•Kernel Density Estimation and Point Overlay methods result in similar mean and standard deviation exposure estimates.•Density Ranking results in larger mean and lower standard deviation estimates compared to other methods.•Density Ranking generally provides more reliable estimates of exposure within individuals.•Between individual variability is consistent between methods; clustering of individual movement does not impact results.