Analysis of Spatial Aggregation and Activity of the Urban Population of Almaty Based on Cluster Analysis

Gulnara Bektemyssova, Artem Bykov (), Aiman Moldagulova, Sayan Omarov (), Galymzhan Shaikemelev, Saltanat Nuralykyzy and Dauren Umutkulov
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Gulnara Bektemyssova: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan
Artem Bykov: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan
Aiman Moldagulova: Department Software Engineering, Institute of Automation and Information Technologies, Kazakh National Research Technical University named after K.I. Satbayev, Almaty 050013, Kazakhstan
Sayan Omarov: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan
Galymzhan Shaikemelev: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan
Saltanat Nuralykyzy: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan
Dauren Umutkulov: Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan

Sustainability, 2025, vol. 17, issue 7, 1-23

Abstract: This study analyzes the spatial aggregation and activity of the urban population in Almaty using anonymized population density data provided by a telecommunications operator and geographic data from OpenStreetMap. The study focuses on identifying stable zones of high population activity, which facilitates the optimization of transport routes, urban infrastructure planning, and the efficient allocation of city resources. The novelty of this work lies in the integration of aggregated spatiotemporal data with advanced clustering methods, including DBSCAN, KMeans++, and agglomerative clustering. The research methodology involves dividing the city into 500 × 500 m quadrants, calculating normalized population density metrics, and identifying high-activity clusters. Based on a comparative analysis of clustering algorithms, DBSCAN exhibited the highest clustering quality according to the silhouette coefficient and the Davies–Bouldin index, allowing for the identification of key zones of urban activity. The identified clusters were utilized to assess transport load, analyze disparities in the distribution of public transport stops, and develop recommendations to improve public transport accessibility in the most congested areas. The study’s findings are applicable not only to optimizing the transport network but also to addressing a broader range of urban planning challenges, including the strategic placement of infrastructure facilities and the management of population flows. The proposed methodology is scalable and can be adapted to other cities requiring effective tools for analyzing the spatiotemporal activity of urban populations.

Keywords: spatial aggregation; urban population activity; cluster analysis; urban planning; machine learning; geographic information systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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