Estimating SME numbers within cities and agglomerations: applying machine learning methods to Russian data

Introduction: the development of agglomerations, which, as research shows, are becoming productivity growth poles, is one of the priorities in the spatial development policy in Russia. Despite numerous discussions, the term “urban agglomeration” is currently absent in federal legislation, but it is widely used in the regulatory framework at other levels of government. This leads to a lack of coordinated, accurate, and up-to-date information on the size and structure of agglomerations, thus bringing forward the need to formulate a well-based approach to identify their economic and geographical boundaries.