The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts

Abstract

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes.

Research on the Difference Between Recreational Walking and Transport Walking Among the Elderly in Mega Cities With Different Density Zones: The Case of Guangzhou City

“…At high densities, due to the fact that the density was sufficiently high, even though the metro network coverage was dense enough, it did not affect the mode of travel of the elderly, and the bus was more convenient than the metro, and it was only a short walk to the entertainment venues. The results of this study were currently different ( 44 ).…”

Air pollution and metabolic disorders: Dynamic versus static measures of exposure among Hispanics/Latinos and non-Hispanics

“…All valid-wear GPS points for the entire participant’s wear time were used to create a time-weighted activity space using the Kernel Density Equation (KDE) model in ArcGIS with a 200 m bandwidth, which has been shown in a previous study to be a reliable distance for exposure estimation ( Lee and Kwan, 2019 ). To account for variation in amount of time participants wore devices, we normalized the time-weighted density measure values for each participant on a scale of 0–1 where the greatest amount of time spent by a participant in a location was equal to 1, and the least equal to 0 ( Jankowska et al, 2021 ).…”

Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories

“…Specifically, 200 m GPS‐based circular buffers are created around each GPS point to calculate the features. In a recent study, 200 m was found to be the most appropriate distance for examining the associations between ATMs and environmental contexts (including trees, parks and open spaces, transit availability (Chicago Metropolitan Agency for Planning (CMAP) Data Hub, 2017), crime, and traffic accidents), obtaining the largest number of higher significance levels in environmental variables when compared to other shorter buffer distances (Lee & Kwan, 2019). As shown in Table 1, 35 predictors related to the physical, social, and safety‐related environments that drew attention from researchers in past studies were used for the accurate prediction of ATMs in likelihood mapping.…”

“…To obtain the travel modes of the trips made by the participants, we modified the classification algorithm developed by Lee and Kwan (2018). The adopted classification algorithm can automatically classify walking, biking, in‐vehicle, and running modes using GPS data as training data, achieving high predictive accuracy (97% overall) (Lee & Kwan, 2019).…”