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Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County

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Abstract

Research on retail food environment (RFE) relies on data availability and accuracy. However, the discrepancies in RFE datasets may lead to imprecision when measuring association with health outcomes. In this research, we present a two-tier hierarchical point of interest (POI) matching framework to compare and triangulate food outlets across multiple geospatial data sources. Two matching parameters were used including the geodesic distance between businesses and the similarity of business names according to Levenshtein distance (LD) and Double Metaphone (DM). Sensitivity analysis was conducted to determine thresholds of matching parameters. Our Tier 1 matching used more restricted parameters to generate high confidence-matched POIs, whereas in Tier 2 we opted for relaxed matching parameters and applied a weighted multi-attribute model on the previously unmatched records. Our case study in San Diego County, California used government, commercial, and crowdsourced data and returned 20.2% matched records from Tier 1 and 18.6% matched from Tier 2. Our manual validation shows a 100% matching rate for Tier 1 and up to 30.6% for Tier 2. Matched and unmatched records from Tier 1 were further analyzed for spatial patterns and categorical differences. Our hierarchical POI matching framework generated highly confident food POIs by conflating datasets and identified some food POIs that are unique to specific data sources. Triangulating RFE data can reduce uncertain and invalid POI listings when representing food environment using multiple data sources. Studies investigating associations between food environment and health outcomes may benefit from improved quality of RFE.

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Data Availability

Data are not available to be transferred from the authors upon request. Yelp data are owned by Yelp.com and can be accessed through official API. Esri Data are part of the ArcGIS Business Analyst product and corresponding licenses are required for access. Government data are owned by San Diego Department of Environmental Health and require paperwork to access.

Notes

  1. Yelp fusion: https://www.yelp.com/developers/documentation/v3/get_started

  2. Yelp business category list: https://blog.yelp.com/businesses/yelp_category_list/

  3. North American Industry Classification System (NAICS): https://www.census.gov/naics/

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Acknowledgements

This research was funded by the NIH National Cancer Institute grant R01 CA228147, and by National Science Foundation grant 1561112.

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Authors and Affiliations

  1. Department of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong

    Yanjia Cao

  2. Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA

    Jiue-An Yang & Marta M. Jankowska

  3. Department of Geography, San Diego State University, San Diego, CA, USA

    Atsushi Nara

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  1. Yanjia Cao
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Correspondence to Yanjia Cao.

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Cao, Y., Yang, JA., Nara, A. et al. Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County. J Urban Health 101, 155–169 (2024). https://doi.org/10.1007/s11524-023-00817-9

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