Categorization of regions by level of socio-economic development: PCA-based approaches

in the context of digitalization and increasing complexity of socio-economic processes, there is a growing need to develop effective tools for analyzing regional development. This article proposes a methodology for categorizing the regions of the Russian Federation based on the analysis of population needs using the Principal Component Analysis (PCA) method. A comparison is made between two approaches – mathematical and heuristic – to the implementation of the categorization algorithm. The article outlines the data processing steps, including correlation analysis, standardization, principal component construction, and quantile classification. The proposed approach has proven effective in assessing the socio-economic status of Russian regions and identifying key factors influencing their development. This study was carried out as part of the project "Development of a methodology for forming an instrumental base for analyzing and modeling the spatial socio-economic development of systems in the context of digitalization based on internal reserves" (FSEG-2023-0008).

monitoring, mood category, socio-economic development, categorization of regions, categorization methodology, mathematical and heuristic categorization of regions