BACKGROUND
Locations of people moving about their lives are now commonly tracked through smartphones and wearable devices that access the Global Positioning System (GPS). Immediate measures include the estimated locations that identify visited map points and the travel paths between them. Recent studies suggest that the monitoring and classification of individuals’ locations during daily routines provide accessible measures of human behavior.
OBJECTIVE
Here we introduce DPLocate, an open-source GPS data analysis pipeline designed to derive measures that abstract away from the original locations (and hence the identity of the individuals) and capture dynamics related to social, vocational, sleep, and clinical behaviors.
METHODS
We divide derived measures into primary and secondary. Primarily derived measures stay close to the original location data and extract deidentified metrics, including distance traveled, time spent at the main locations, and estimates of travel activity (entropy). Secondary derived measures estimate life patterns that are captured incidentally by extracting returns to the Points of Interest (POIs) in behaviorally-relevant time-bands. For example, measures of behavioral dynamics and social interactions can be gleaned by estimating the time spent in POIs across day, evening, night, and late-night time-bands.
RESULTS
The utility of these derived measures for research is illustrated in college students and for clinical monitoring in individuals living with psychiatric disorders. Captured dynamics included behavioral transitions at the onset of the Covid-19 lockdown. Limitations of derived data are discussed, including the necessity to protect derived data from identification and possible ways in which the derived data might be misinterpreted.
CONCLUSIONS
The DPLocate pipeline provides a powerful tool to analyze intermittent GPS data and extract behavioral patterns of individuals based on their relocation.