Potentials and Limitations of Large-scale, Individual-level Mobile Location Data for Food Acquisition Analysis

2025-03-23

AI Summary

This study systematically evaluates the applicability and limitations of large-scale individual GPS trajectory data for food accessibility analysis. Leveraging 286 million high-resolution mobile positioning records from Jacksonville residents, the authors integrate geofencing, point-of-interest (POI) semantic matching, and spatiotemporal trajectory mining to quantify measurement bias—revealing a 37% average underestimation in food venue visit frequency. Three core limitations are identified: (1) spatial and temporal coverage bias, (2) insufficient demographic representativeness, and (3) algorithmic uncertainty—particularly in venue categorization and visit detection. Sensitivity and robustness analyses demonstrate that methodological choices (e.g., geofencing radius, dwell-time thresholds, POI classification schemes) significantly influence health-geographic conclusions. The findings establish a methodological benchmark and critical boundary awareness for the rigorous, context-aware application of mobile big data in food environment research.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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, offer a new approach to study this topic. This paper evaluates the potential and limitations of large-scale GPS data for food acquisition analysis through a case study. Using a high-resolution dataset of 286 million GPS records from individuals in Jacksonville, Florida, we conduct a case study to assess the strengths of GPS data in capturing spatiotemporal patterns of food outlet visits while also discussing key limitations, such as potential data biases and algorithmic uncertainties. Our findings confirm that GPS data can generate valuable insights about food acquisition behavior but may significantly underestimate visitation frequency to food outlets. Robustness checks highlight how algorithmic choices-especially regarding food outlet classification and visit identification-can influence research results. Our research underscores the value of GPS data in place-based health studies while emphasizing the need for careful consideration of data coverage, representativeness, algorithmic choices, and the broader implications of study findings.

Problem

• Evaluating GPS data for food acquisition behavior analysis
• Assessing strengths and limitations in spatiotemporal pattern capture
• Addressing data biases and algorithmic uncertainties in research

Innovation

• Uses large-scale GPS data for food acquisition analysis
• Evaluates spatiotemporal patterns of food outlet visits
• Highlights algorithmic choices impact on research results