PostGIS-powered proximity queries: implementation and Google Maps visualization
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.
Authors
- Pavan Kumar Papasani (ORCID: https://orcid.org/0009-0005-2295-7633)
- Ravi Teja Papasani (ORCID: https://orcid.org/0009-0003-6017-7895)
Institutions
- University of South Alabama (US)
Publication Details
- Journal
- PeerJ Computer Science
- Published
- 2025-11-11
- DOI
- https://doi.org/10.7717/peerj-cs.3220
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00