GEO-INFORMATION MODELING OF BUSINESS PROCESSES: OPTIMIZATION USING SPATIAL ANALYSIS AND DATABASES

Abstract

This article addresses a critical challenge for modern businesses: selecting the optimal location for a new retail outlet. The study focuses on developing a GIS modeling workflow to justify the site selection for a new coffee shop in Kharkiv’s city center. The primary goal is to formulate an algorithm that solves this economic problem using Geographic Information System (GIS) tools. Given that location selection is a multifactorial process involving spatial, infrastructural, and competitive variables, the paper justifies the use of GIS to visualize spatial data and analyze attribute information stored in databases. A key objective of the research is to analyze the capabilities of QGIS Desktop in defining the size and configuration of "service areas" for existing businesses and visualizing them spatially. The research employs general scientific methods of system analysis and geostatistics to transform spatial data from discrete to continuous forms. The methodological framework combines geographical and probabilistic components via the Huff model, allowing for the identification of "unreachable areas" (market gaps) unaffected by existing competitors. To delineate trade zones and service areas, the study proposes using Thiessen polygons, accounting for the real-world spatial distribution of retail objects. As a result, the optimal location for the new coffee shop was identified. The study also provides a comparative analysis of results obtained from different spatial analysis methods, including buffer zones, isochrones, and transport network analysis.It was established that while these methods yield similar results, network analysis proves to be the most accurate as it considers the actual pedestrian and transport network and movement conditions. These findings can assist small and medium-sized business owners in making data-driven management decisions, thereby reducing risks when opening new locations under spatial constraints.