Implementation methodology improves performance benchmarking in location-based services, indicating enhanced scalability for queries up to 1,000,000 locations.
Location-based services require efficient methods to calculate nearby points of interest. This article presents an implementation methodology for proximity queries using PostgreSQL’s PostGIS extension, focusing on the ST_DWithin function and spatial indexing capabilities. We demonstrate circular area optimization through ST_Buffer for enhanced geographic searches. The methodology is validated using a dataset of 108 retail store locations across Texas metropolitan areas, with performance benchmarking showing 19-24x query improvements through spatial indexing. Results are visualized through a web-based interface using Google Maps Application Programming Interface (API). Performance analysis demonstrates scalability up to 1,000,000 locations. This study provides practical guidance for location-based services requiring proximity searches within specified radii.
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Papasani et al. (2025) studied this question.
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