Intellectual Data Mining in Socio-Geographic Research

Abstract

In social geography, aimed at understanding the territorial organization of society, various methods are used, including data mining. However, there is no generalization of the experience of using such methods in world science. Therefore, the purpose of this article is to analyze the global array of scientific articles on this issue to identify priorities, algorithms and thematic areas with their capabilities and limitations. Using the author's method of semantic search based on machine learning, about two hundred articles published in the last two decades have been identified in eight bibliographic databases. Their generalization made it possible to identify chronological and chorological priorities, as well as to establish that a limited number of algorithms had been used for the geospatial data mining, which can be combined into groups of neural network, evolutionary, decision trees, swarm intelligence and support vector methods. These algorithms were used in five thematic areas (spatial-urban, regional-typological, area-based, geo-indicative and territorial-connective). The main features and limitations in each direction are given.