Analyzing GPS Data for Psychological Research: A Tutorial

Abstract

The ubiquity of location data-enabled devices provides novel avenues for psychology researchers to incorporate spatial analytics into their studies. Spatial analytics use GPS data to assess and understand mobility behavior (e.g., locations visited, movement patterns). This tutorial provides a practical guide to using GPS data in R, introducing researchers to key procedures and resources for conducting spatial analytics. We show readers how to clean GPS data, compute mobility features (e.g., time spent at home, number of unique places visited), and visualize locations and movement patterns. In addition, we discuss the challenges of ensuring participant privacy and interpreting the psychological implications of mobility behaviors. The tutorial is accompanied by an R Markdown script and a simulated GPS dataset made available on the Open Science Framework.

Leveraging Ecological Momentary Assessment Data to Characterize Individual Mobility: Exploratory Pilot Study in Rural Uganda

“…GPS points were excluded if they were outside of sub-Saharan Africa (<1% of GPS coordinates per participant). Interpoint distances were totaled over each participant’s roughly 90-day observation period [ 12 ]. Mobility was summarized as the average distance traveled per week to account for certain days of the week with outliers of distance traveled (eg, staying home on Sundays) that can bias centrality estimates [ 13 ].…”

“…Mobility was summarized as the average distance traveled per week to account for certain days of the week with outliers of distance traveled (eg, staying home on Sundays) that can bias centrality estimates [ 13 ]. Two other measures were also described: the number of unique places visited (deduplicated points within 25 m of one another, based on smartphones’ 5-20-m GPS point accuracy [ 12 ]), and the percent of GPS points recorded away from home [ 7 ]. To calculate the latter measure, GPS points were dichotomized as being located within or outside a home buffer region.…”

Psychological Perspectives on Mobile Media: A Flyover Review

“…In particular, while a few recent studies found in MMC take a situational approach, only a minority of studies clearly distinguished between-person from within-person effects or leveraged mobile log data (Aalbers et al, 2021;Dogruel & Schnauber-Stockmann, 2020;Sewall et al, 2020). Likewise, recent calls for measuring person-specific effects (Beyens et al, 2020) and geospatial effects (Muller et al, 2022) are still largely absent within psychological research on mobile media. Given that intra-personal dynamics and situational factors are crucial for a holistic understanding of media effects, research cannot make full use of psychological perspectives without adopting such advancements.…”

Psychological perspectives on mobile media: A flyover review

“…In particular, while a few recent studies found in MMC take a situational approach, only a minority of studies clearly distinguished between-person from within-person effects or leveraged mobile log data (Aalbers et al, 2021;Dogruel & Schnauber-Stockmann, 2020;Sewall et al, 2020). Likewise, recent calls for measuring person-specific effects (Beyens et al, 2020) and geospatial effects (Müller et al, 2022) are still largely absent within psychological research on mobile media. Given that intra-personal dynamics and situational factors are crucial for a holistic understanding of media effects, research cannot make full use of psychological perspectives without adopting such advancements.…”