Abstract
Analysis and visualization of large volumes of semi-structured information (Big Data) in decision-making support is an important and urgent problem of the digital economy. This article is devoted to solving this problem in the field of digital marketing, e.g. distributing outlets and service centers in the city. We propose a technology of adaptive formation of spatial segments of an urbanized territory based on the analysis of supply and demand areas and their visualization on an electronic map. The proposed approach to matching supply and demand includes 3 stages: semantic-statistical analysis, which allows building dependencies between objects generating demand, automated search for a balance between supply and demand, and visualization of solution options. An original concept of data organization using multiple layer including digital map, semantic web (knowledge base) and overlay network was developed on the basis of the introduced spatial clustering model. The proposed technology, being implemented by an intelligent software solution of a situational center for automated decision-making support, can be used to solve problems of optimization of networks of medical institutions, retail and cultural centers, and social services. Some examples given in this paper illustrate possible benefits of its practical use.
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Ivaschenko, A., Stolbova, A., Golovnin, O. (2019). Spatial Clustering Based on Analysis of Big Data in Digital Marketing. In: Kuznetsov, S., Panov, A. (eds) Artificial Intelligence. RCAI 2019. Communications in Computer and Information Science, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-30763-9_28
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