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

• URL: http://arxiv.org/abs/2503.18119v1
• Date: Sun, 23 Mar 2025 15:52:36 GMT
• Title: Potentials and Limitations of Large-scale, Individual-level Mobile Location Data for Food Acquisition Analysis
• Authors: Duanya Lyu, Luyu Liu, Catherine Campbell, Yuxuan Zhang, Xiang Yan,
• Abstract summary: This paper evaluates the potential and limitations of large-scale GPS data for food acquisition analysis.<n>Using a high-resolution dataset of 286 million GPS records from individuals in Jacksonville, Florida, we conduct a case study.<n>Our findings confirm that GPS data can generate valuable insights about acquisition behavior but may underestimate visitation frequency to food outlets.
• Score: 7.951464014836594
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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