Detection of Spatial Trade Clusters by Means of Geoinformation Technologies

A methodology for detection of spatially distributed trade clusters across the territory was developed based on the kernel density function used in GIS technologies. To determine the optimal parameters for the placement of trade enterprises, interpolation distance for density assessment of point objects and a minimum concentration threshold are applied, allowing for the identification of various types of trade clusters in the Republic of Crimea. The aim of the article is to identify evidence of the existence of spatially distributed trade clusters of different types in the region, determine their concentration, economic role, and possible functioning prospects. In the paper spatial modeling methods using GIS technologies were used. It is shown that trade facilities can be of various morphological types, such as marshal, nodal, satellite, and sectoral ones. A classification of trade clusters in the Republic of Crimea was made, and the potential weight of each cluster type was calculated. Heat maps of population density distribution were created to assess potential zones for trade development. Using the kernel density function, spatially distributed trade clusters of different types were identified in the area under analysis. This approach is applied to detect hotspots based on extrapolated estimates of the number of stores and their distribution across the regions. Cartography was carried out using the ArcGIS software tool. The analysis confirms the existence of spatially distributed trade clusters with varying morphology on the peninsula and emphasizes their advantages and disadvantages. The role of these clusters in regional development is shown. The results of the article may be used in development of regional strategies that contribute to various forms of trade relations in the region.