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.

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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
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article

PostGIS-powered proximity queries: implementation and Google Maps visualization

Pavan Kumar Papasani, Ravi Teja Papasani
PeerJ Computer Science
Data Management and Algorithms
article

PostGIS-powered proximity queries: implementation and Google Maps visualization

Pavan Kumar Papasani, Ravi Teja Papasani
article en

Abstract

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.

PeerJ Computer ScienceVol. 11
University of South Alabama (US)
Openalex Percentile: Top 58%
Data Management and Algorithms
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