Collection and Processing of Spatial Data for Solving Location and Planning Problems

The relevance of the article lies in the need to use high-quality spatial data for solving location problems of retail and infrastructure facilities under conditions of limited access to open geoanalytical sources. The main objective of the research is to develop and systematise approaches to collecting, processing, and aggregating spatial data from various sources for subsequent construction of economic-mathematical models within the framework of the posed location problem. The article examines the main types of spatial data via household information (GIS Housing and Utilities), real estate prices (Avito Real Estate), data on organisations and competition (Yandex Maps), building and road network data (OpenStreetMap), as well as pedestrian and car traffic data and socio- demographic structure of the population (Yandex Geoanalytics). For each source, the data collection process is described, along with the necessary tools and processing methods that ensure georeferencing and the ability to calculate features within given radii. Practical implications. The approach proposed in the study allows for the formation of a rich set of explanatory variables for modeling the economic indicators of locations.

The obtained factors will be used in future work for comparative analysis of modeling results.

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