Potentials and limitations of large-scale, individual-level mobile location data for food acquisition analysis

Understanding food acquisition is crucial for developing strategies to combat food insecurity, a major public health concern. The emergence of large-scale mobile location data (typically exemplified by GPS data), which captures people's movement over time at high spatiotemporal resolutions, offers a new approach to study this topic. In this study, we use a high-resolution dataset of 286 million GPS records from individuals in Jacksonville, Florida, to analyze the area's food outlet visit behaviors. We assessed data coverage, evaluated the applicability of GPS-derived metrics, and examined the sensitivity of the findings to the methodological decisions involved in extracting those metrics from the data. Our findings demonstrate that GPS data can generate valuable insights about food acquisition behavior by capturing diverse spatial and temporal patterns at the individual level and revealing how these patterns are distributed across the broad population. However, we also find biases in the data coverage and significantly underestimated visit frequency. Sensitivity analyses further reveal a pronounced sensitivity of results to algorithmic design choices. Our research underscores the value of GPS data in place-based health studies while emphasizing the need for careful evaluation of data coverage and representativeness, reflexive algorithmic design, attention to privacy concerns, and awareness of the broader social implications of the findings.