SPATIAL ANALYSIS OF CITIES AND AGGLOMERATIONS: INTEGRATION OF GIS AND BIG DATA TECHNOLOGIES

Introduction. Traditional sources of information that are commonly used for spatial analysis of geodemographic processes are not always able to provide an extensive understanding of the effects associated with the growth or decline in the population of cities and agglomerations. In this regard, data obtained with the help of Big Data technologies are of great help for spatial analysis. Modern GIS is an effective tool for solving the problem of processing and interpreting big data. GIS allows you to structure data and visualize it, thus obtaining a geographical interpretation of information. The approbation of the possibilities of using data corrupted by big data technology, based on integration with GIS, as well as the use of traditional information sources (goskomstat) was carried out on the example of cities and urban agglomerations of Lipetsk and Stavropol. Materials and research methods. To analyze the dynamics of the built-up areas of Lipetsk and Stavropol in the period from 2000 to 2020, satellite images from Sentinel-2 and Landsat-8 satellites were used. The capabilities of the ScanEx Image Processor software were used to process satellite images. Work on the allocation of building boundaries in different years was carried out by the method of reverse decryption. To analyze the population density of cities, heat maps provided by the Export Base service were used. The cost of residential premises was estimated by processing information from Internet services for placing ads (Avito and CIAN). Quantum GIS is used as the main geoinformation tool. The results of the study and their discussion. The approbation of integration technologies and research methods on the example of the core cities of Lipetsk and Stavropol revealed that the demographically favorable city of Stavropol has higher rates of development, has higher prices for residential real estate. This process leads to a concentration of the population, including in new neighborhoods, which is not so pronounced in depopulating Lipetsk. Suburban areas included in the half-hour and hourly transport accessibility are generally comparable in terms of the cost of residential premises within both agglomerations. Results. The development and integration of technologies for the collection, processing and analysis of spatial and temporal data contributes to the expansion of the methodological tools of geodemographic research and opens up wide opportunities to comprehensively approach the issue of the development of intra-agglomeration and intra-urban processes. Traditional sources made it possible to assess the dynamics of the number of cities, and the use of big data technology with GIS integrations revealed the features of the development of urban and suburban development, heat maps of density gave an idea of the features of population concentration, including newly built-up areas. An analysis of the cost of housing using data from Internet services for placing ads in central cities and their suburbs confirmed the main trends related to the centrality of places and the periphery.

Big Data

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