Optimizing Retail Site Selection Using Geospatial Analytics

Olga Chumakova, Natalia Trankova, Dmitrii Rykunov, Ivan Giganov, Maksim Pershin, Egor Sachko

Abstract: Geospatial analytics is a crucial tool in strategic retail decision - making, influencing store location selection, market segmentation, and supply chain efficiency. This study addresses the limitations of traditional site selection methods by integrating advanced spatial analysis techniques. Leveraging diverse datasets - including demographic, transportation, and competitive information - the proposed model employs principal component analysis for dimensionality reduction and geographically weighted regression to account for spatial heterogeneity. The findings indicate that this approach enhances predictive accuracy and optimizes retail location selection. By incorporating spatial autocorrelation measures and similarity metrics, this study contributes a novel framework for data - driven site selection, offering practical implications for business strategy and future research.

Keywords: geospatial analytics, site selection, spatial analysis, predictive modeling, retail optimization

How to Cite?: Olga Chumakova, Natalia Trankova, Dmitrii Rykunov, Ivan Giganov, Maksim Pershin, Egor Sachko, "Optimizing Retail Site Selection Using Geospatial Analytics", Volume 14 Issue 2, February 2025, International Journal of Science and Research (IJSR), Pages: 523-527, https://www.ijsr.net/getabstract.php?paperid=MS25201185139, DOI: https://dx.doi.org/10.21275/MS25201185139