Telecommunications Connectivity of Russian Regions: Assessment Using Geospatial Artificial Intelligence
The work was carried out at the expense of the state assignment of the Sochava Institute of Geography of the Siberian Branch of the Russian Academy of Sciences (FWEM-2026-0006) «Socio-geographical foundations of the analysis of Siberia's territorial structures in the context of Russia's new Eastern policy and the strengthening of Eurasian continental integration» No. 126020516552-3.
DOI:
https://doi.org/10.52575/2712-7443-2026-50-3-604-618Keywords:
human geography, backbone network, network reliability, telecommunications-network position, territorial communication structure, knowledge graph, Russian FederationAbstract
The purpose of the study was to create a geospatial artificial intelligence system and apply it to assess the telecommunications connectivity of Russia's regions. The study employed ComNews data on Russia's backbone communication network in 2020–2024, which was updated using the author's database on fiber-optic communication lines. The architecture of the built-in system is based on a knowledge graph (85 vertices and 3570 edges that allow for transformations and generalizations), which manages the database and knowledge base with the support of the classifier, router, rule generator, and interpreter subsystems. The connectivity of 85 Russian regions has been analyzed in three aspects: the reliability of the linear-node structure, the region's position in the network, and the backbone communication framework. Five types of reliability for the telecommunications connection between regions are proposed. Problem regions were identified using stress testing on the knowledge graph. The telecommunications-network position of all Russian regions was assessed using two versions. The regions have been distributed between the center, the semi-periphery, the near-periphery, and the far-periphery. The backbone communication network has been identified as a single center, three sub-centers, and the regions connecting them.
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Список источников
Интерактивная карта «Магистральные сети связи в России». ComNews, 2020. Электронный ресурс. URL: https://www.comnews.ru/content/211042/2020-10-21/2020-w43/magistralnye-seti-svyazi-rossii (дата обращения: 10.10.2025).
Интерактивная карта «Магистральные сети связи в России». ComNews, 2021. Электронный ресурс. URL: https://www.comnews.ru/content/217974/2021-12-20/2021-w51/magistralnye-seti-svyazi-rossii-2021 (дата обращения: 10.10.2025).
Интерактивная карта «Магистральные сети связи в России». ComNews, 2022. Электронный ресурс. URL: https://www.comnews.ru/content/223014/2022-11-28/2022-w48/magistralnye-seti-svyazi-rossii-2022 (дата обращения: 10.10.2025).
Интерактивная карта «Магистральные сети связи в России». ComNews, 2023. Электронный ресурс. URL: https://www.comnews.ru/content/230456/2023-12-20/2023-w51/1180/magistralnye-seti-svyazi-rossii-2023 (дата обращения: 10.10.2025).
Интерактивная карта «Магистральные сети связи в России». ComNews, 2024. Электронный ресурс. URL: https://www.comnews.ru/content/236040/2024-11-29/2024-w48/1180/magistralnye-seti-svyazi-rossii-2024 (дата обращения: 10.10.2025).
Оценка численности постоянного населения по субъектам Российской Федерации на 1 января 2024 г. Федеральная служба государственной статистики, 2024. Электронный ресурс. URL: https://docviewer.yandex.ru/view/1676819753/ (дата обращения: 10.10.2025).
Список литературы
Блануца В.И. 2019. Информационно-сетевая география. Москва, ИНФРА-М, 243 с.
Блануца В.И. 2025а. Создание первых автономных систем интернета в Сибири как пространственная диффузия инноваций. Пространственная экономика, 21(1): 7–32. https://doi.org/10.14530/se.2025.1.007-032
Блануца В.И. 2025б. Оценка экономико-географического положения городов и регионов с помощью геопространственного искусственного интеллекта. Вестник Воронежского государственного университета. Серия: География. Геоэкология, 2: 40–48. https://doi.org/10.17308/geo/1609-0683/2025/2/40-48
Полякова А.Г., Симарова И.С. 2014. Обоснование регионального развития с учетом связанности экономического пространства. Тюмень, Тюменский государственный нефтегазовый университет, 104 с.
Попков В.К. 2000. Математические модели связности. Ч. 1. Графы и сети. Новосибирск, Изд-во ИВМиМГ СО РАН, 175 с.
Черкашина Т.Ю., Мосиенко Н.Л., Калашникова К.Н. 2023. Связанность регионов юга Западной Сибири в индикаторах пространственной мобильности населения. Регион: экономика и социология, 4(120): 134–169. https://doi.org/10.15372/REG20230406
Bailey M., Johnston D., Kuchler T., Russel D., State B., Stroebel J. 2020. The Determinants of Social Connectedness in Europe. In: Social Informatics (Lecture Notes in Computer Science). Cham, Springer Nature Switzerland: 1–14. https://doi.org/10.1007/978-3-030-60975-7_1
Bernard C., Villanova-Oliver M., Gensel J. 2022. Theseus: A Framework for Managing Knowledge Graphs about Geographical Divisions and Their Evolution. Transactions in GIS, 26(8): 3202–3224. https://doi.org/10.1111/tgis.12988
Blanutsa V.I. 2024. Identification of Critical Telecommunications Infrastructure in Russia: A Geographical Approach. Geography and Natural Resources, 45(1): 1–8. https://doi.org/10.1134/S1875372824700045
Boto-García D., Pérez L. 2023. The Effect of High-Speed Rail Connectivity and Accessibility on Tourism Seasonality. Journal of Transport Geography, 107(3): e103546. https://doi.org/10.1016/j.jtrangeo.2023.103546
Chen C., Tong H. 2022. Network Connectivity: Concepts, Computation, and Optimization. Cham, Springer Nature Switzerland, 151 p.
Darwish D., Chemingui H. 2025. Recent Trends in Geospatial AI. New York, London, Beijing, IGI Global Scientific Publishing, 394 p.
Das R., Mitra M., Singh C. 2023. Era of Artificial Intelligence. The 21st Century Practitioners’ Approach. New York, Chapman & Hall/CRC, 166 p.
Fensel D., Simsek U., Angele K., Huaman E., Kärle E., … Wahler A. 2020. Knowledge Graphs: Methodology, Tools and Selected Use Cases. Cham, Springer Nature Switzerland, 147 p.
Foster R.J. 2023. Tenuous Connectivity: Time, Citizenship, and Infrastructure in a Papua New Guinea Telecommunications Network. The Asia Pacific Journal of Anthropology, 24(2): 91–115. https://doi.org/10.1080/14442213.2023.2177330
Gao S., Hu Y., Li W. 2023. Handbook of Geospatial Artificial Intelligence. Boca Raton, CRC Press, 468 p.
Guo X., Qian H., Wu F., Liu J. 2021. A Method for Constructing Geographical Knowledge Graph from Multisource Data. Sustainability, 13(19): e10602. https://doi.org/10.3390/su131910602
Han B., Qu T., Tong X., Wang H., Liu H., … Cheng C. 2023. AugGKG: A Grid-Augmented Geographic Knowledge Graph Representation and Spatio-Temporal Query Model. International Journal of Digital Earth, 16(2): 4934–4957. https://doi.org/10.1080/17538947.2023.2290569
Han B., Qu T., Jiang J. 2025. GN-GCN: Grid Neighborhood-Based Graph Convolutional Network for Spatio-Temporal Knowledge Graph Reasoning. ISPRS Journal of Photogrammetry and Remote Sensing, 220: 728–739. https://doi.org/10.1016/j.isprsjprs.2025.01.023
Hashemi R., Darabi H., Hashemi M., Wang J. 2024. Graph Theory in Ecological Network Analysis: A Systematic Review for Connectivity Assessment. Journal of Cleaner Production, 472: e143504. https://doi.org/10.1016/j.jclepro.2024.143504
Jażdżewska I.A. 2021. Use of Graph Theory to Study Connectivity and Regionalisation of Polish Urban Network. Area, 54(2): 290–303. https://doi.org/10.1111/area.12774
Kierner S., Kucharski J., Kierner Z. 2023. Taxonomy of hybrid Architectures Involving Rule-Based Reasoning and Machine Learning in Clinical Decision Systems: A Scoping Review. Journal of Biomedical Informatics, 144: e104428. https://doi.org/10.1016/j.jbi.2023.104428
Kopczewska K. 2022. Spatial Machine Learning: New Opportunities for Regional Science. The Annals of Regional Science, 68(4): 713–755. https://doi.org/10.1007/s00168-021-01101-x
Laita A., Kotiaho J.S., Monkkonen M. 2011. Graph-Theoretical Connectivity Measures: What do They Tell Us about Connectivity? Landscape Ecology, 26(7): 951–967. https://doi.org/10.1007/s10980-011-9620-4
Liu P., Zhang Y., Biljecki F. 2024. Explainable Spatially Explicit Geospatial Artificial Intelligence in Urban Analytics. Environment and Planning B: Urban Analytics and City Science, 51(5): 1104–1123. https://doi.org/10.1177/23998083231204689
Mai G., Hu Y., Gao S., Cai L., Martins B., … Janowicz K. 2022. Symbolic and Subsimbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning. Transactions in GIS, 26: 3118–3124. https://doi.org/10.1111/tgis.13012
Mai G., Xie Y., Jia X., Lao N., Rao J., … Jiao J. 2025. Towards the Next Generation of Geospatial Artificial Intelligence. International Journal of Applied Earth Observation and Geoinformation, 136(1): e104368. https://doi.org/10.1016/j.jag.2025.104368
Moilanen A. 2011. On the Limitations of Graph-Theoretical Connectivity in Spatial Ecology and Conservation. Journal of Applied Ecology, 48(6): 1543–1547. https://doi.org/10.1111/j.1365-2664.2011.02062.x
Nowak-Brzezińska A., Horyń C. 2020. Exploration of Outliers in If-Then Rule-Based Knowledge Bases. Entropy, 22(10): e1096. https://doi.org/10.3390/e22101096
Papadakis E., Adams B., Gao S., Martins B., Baryannis G., Ristea A. 2022. Explainable artificial Intelligence in the Spatial Domain (X-GeoAI). Transactions in GIS, 26(6): 2413–2414. https://doi.org/10.1111/tgis.12996
Prasad S., Chanussot J., Li J. 2024. Advances in Machine Learning and Image Analytics for GeoAI. Amsterdam, London, Cambridge, Elsevier, 364 p.
Prawira N., Wella, Natalia F. 2025. Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review. International Journal of Information Visualization, 9(4): 1783–1791.
Rajabi E., Etminani K. 2024. Knowledge-Graph-Based Explainable AI: A Systematic Review. Journal of Information Science, 50(4): 1019–1029. https://doi.org/10.1177/01655515221112844
Rutherford J., Gillespie A., Richardson R. 2004. The Territoriality of Pan-European Telecommunications Backbone Networks. Journal of Urban Technology, 11(3): 1–34. https://doi.org/10.1080/10630730500064166
Saleh H.J.H., Arrang J.R.T., de Barros Cardoso L.M.O. 2025. Applications of Geospatial AI in human geography and spatial networks: A literature review. EDRAAK, 2025: 76–84. https://doi.org/10.70470/EDRAAK/2025/010
Tang H. 2020. Regional Patterns and Hierarchical Tendencies: Analysis of the Network Connectivity of 63 Service-Oriented Tourist Cities in China. Sustainability, 12(16): e6532. https://doi.org/10.3390/su12166532
Tóth G., Elekes Z., Whittle A., Lee C., Kogler D.F. 2022. Technology Network Structure Conditions the Economic Resilience of Regions. Economic Geography, 98(4): 355–378. https://doi.org/10.1080/00130095.2022.2035715
Tranos E., Gillespie A. 2011. The Urban Geography of Internet Backbone Networks in Europe: Roles and Relations. Journal of Urban Technology, 18(1): 35–50. https://doi.org/10.1080/10630732.2011.578408
Tsiotas D. 2025. Socioeconomic Attributes in the Topology of the Intercity Road Network in Greece. Future Transportation, 5(1): e3. https://doi.org/10.3390/futuretransp5010003
Wilke A., Ngonga Ngomo A.-C. 2023. LauNuts: A Knowledge Graph to Identify and Compare Geographic Regions in the European Union. In: The Semantic Web. Cham, Springer Nature Switzerland: 408–418. https://doi.org/10.1007/978-3-031-33455-9_24
Xing J., Sieber R. 2023. The Challenges of Integrating Explainable Artificial Intelligence into GeoAI. Transactions in GIS, 27(3): 626–645. https://doi.org/10.1111/tgis.13045
Yan B. 2019. Geographic Knowledge Graph Summarization. Amsterdam, IOS Press, 168 p.
Zang Y., Jiaxiang E., Fiondella L. 2024. A Network Reliability Analysis Method for Complex Real-Time Systems: Case Studies in Railway and Maritime Systems. Mathematics, 12(19): e3014. https://doi.org/10.3390/math12193014
References
Blanutsa V.I. 2019. Information Network Geography. Moscow, Pabl. INFRA-M, 243 p. (in Russian).
Blanutsa V.I. 2025a. Creating the first Autonomous Systems of Internet in Siberia as a Spatial Diffusion of Innovations. Spatial Economics, 21(1): 7–32 (in Russian). https://doi.org/10.14530/se.2025.1.007-032
Blanutsa V.I. 2025b. Assessment of Economic and Geographical Position of Cities and Regions Using Geospatial Artifi cial Intelligence. Proceedings of Voronezh State University. Series: Geography. Geoecology, 2: 40–48 (in Russian). https://doi.org/10.17308/geo/1609-0683/2025/2/40-48
Polyakova A.G., Simarova I.S. 2014. Obosnovanie regional'nogo razvitiya s uchetom svyazannosti ekonomicheskogo prostranstva [Justification of Regional Development Taking into Account the Connectedness of the Economic Space]. Tyumen, Pabl. Tyumenskiy gosudarstvennyy neftegazovyy universitet, 104 p.
Popkov V.K. 2000. Matematicheskie modeli svyaznosti. Ch. 1. Grafy i seti [Mathematical Models of Connectivity. Part 1. Graphs and Networks]. Novosibirsk, Publ. Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences, 175 p.
Cherkashina T.Yu., Mosienko N.L., Kalashnikova K.N. 2023. Examining Regional Connectivity in Southern Western Siberia Through Population Mobility Indicators. Region: Economics and Sociology, 4(120): 134–169 (in Russian). https://doi.org/10.15372/REG20230406
Bailey M., Johnston D., Kuchler T., Russel D., State B., Stroebel J. 2020. The Determinants of Social Connectedness in Europe. In: Social Informatics (Lecture Notes in Computer Science). Cham, Springer Nature Switzerland: 1–14. https://doi.org/10.1007/978-3-030-60975-7_1
Bernard C., Villanova-Oliver M., Gensel J. 2022. Theseus: A Framework for Managing Knowledge Graphs about Geographical Divisions and Their Evolution. Transactions in GIS, 26(8): 3202–3224. https://doi.org/10.1111/tgis.12988
Blanutsa V.I. 2024. Identification of Critical Telecommunications Infrastructure in Russia: A Geographical Approach. Geography and Natural Resources, 45(1): 1–8. https://doi.org/10.1134/S1875372824700045
Boto-García D., Pérez L. 2023. The Effect of High-Speed Rail Connectivity and Accessibility on Tourism Seasonality. Journal of Transport Geography, 107(3): e103546. https://doi.org/10.1016/j.jtrangeo.2023.103546
Chen C., Tong H. 2022. Network Connectivity: Concepts, Computation, and Optimization. Cham, Springer Nature Switzerland, 151 p.
Darwish D., Chemingui H. 2025. Recent Trends in Geospatial AI. New York, London, Beijing, IGI Global Scientific Publishing, 394 p.
Das R., Mitra M., Singh C. 2023. Era of Artificial Intelligence. The 21st Century Practitioners’ Approach. New York, Chapman & Hall/CRC, 166 p.
Fensel D., Simsek U., Angele K., Huaman E., Kärle E., … Wahler A. 2020. Knowledge Graphs: Methodology, Tools and Selected Use Cases. Cham, Springer Nature Switzerland, 147 p.
Foster R.J. 2023. Tenuous Connectivity: Time, Citizenship, and Infrastructure in a Papua New Guinea Telecommunications Network. The Asia Pacific Journal of Anthropology, 24(2): 91–115. https://doi.org/10.1080/14442213.2023.2177330
Gao S., Hu Y., Li W. 2023. Handbook of Geospatial Artificial Intelligence. Boca Raton, CRC Press, 468 p.
Guo X., Qian H., Wu F., Liu J. 2021. A Method for Constructing Geographical Knowledge Graph from Multisource Data. Sustainability, 13(19): e10602. https://doi.org/10.3390/su131910602
Han B., Qu T., Tong X., Wang H., Liu H., … Cheng C. 2023. AugGKG: A Grid-Augmented Geographic Knowledge Graph Representation and Spatio-Temporal Query Model. International Journal of Digital Earth, 16(2): 4934–4957. https://doi.org/10.1080/17538947.2023.2290569
Han B., Qu T., Jiang J. 2025. GN-GCN: Grid Neighborhood-Based Graph Convolutional Network for Spatio-Temporal Knowledge Graph Reasoning. ISPRS Journal of Photogrammetry and Remote Sensing, 220: 728–739. https://doi.org/10.1016/j.isprsjprs.2025.01.023
Hashemi R., Darabi H., Hashemi M., Wang J. 2024. Graph Theory in Ecological Network Analysis: A Systematic Review for Connectivity Assessment. Journal of Cleaner Production, 472: e143504. https://doi.org/10.1016/j.jclepro.2024.143504
Jażdżewska I.A. 2021. Use of Graph Theory to Study Connectivity and Regionalisation of Polish Urban Network. Area, 54(2): 290–303. https://doi.org/10.1111/area.12774
Kierner S., Kucharski J., Kierner Z. 2023. Taxonomy of hybrid Architectures Involving Rule-Based Reasoning and Machine Learning in Clinical Decision Systems: A Scoping Review. Journal of Biomedical Informatics, 144: e104428. https://doi.org/10.1016/j.jbi.2023.104428
Kopczewska K. 2022. Spatial Machine Learning: New Opportunities for Regional Science. The Annals of Regional Science, 68(4): 713–755. https://doi.org/10.1007/s00168-021-01101-x
Laita A., Kotiaho J.S., Monkkonen M. 2011. Graph-Theoretical Connectivity Measures: What do They Tell Us about Connectivity? Landscape Ecology, 26(7): 951–967. https://doi.org/10.1007/s10980-011-9620-4
Liu P., Zhang Y., Biljecki F. 2024. Explainable Spatially Explicit Geospatial Artificial Intelligence in Urban Analytics. Environment and Planning B: Urban Analytics and City Science, 51(5): 1104–1123. https://doi.org/10.1177/23998083231204689
Mai G., Hu Y., Gao S., Cai L., Martins B., … Janowicz K. 2022. Symbolic and Subsimbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning. Transactions in GIS, 26: 3118–3124. https://doi.org/10.1111/tgis.13012
Mai G., Xie Y., Jia X., Lao N., Rao J., … Jiao J. 2025. Towards the Next Generation of Geospatial Artificial Intelligence. International Journal of Applied Earth Observation and Geoinformation, 136(1): e104368. https://doi.org/10.1016/j.jag.2025.104368
Moilanen A. 2011. On the Limitations of Graph-Theoretical Connectivity in Spatial Ecology and Conservation. Journal of Applied Ecology, 48(6): 1543–1547. https://doi.org/10.1111/j.1365-2664.2011.02062.x
Nowak-Brzezińska A., Horyń C. 2020. Exploration of Outliers in If-Then Rule-Based Knowledge Bases. Entropy, 22(10): e1096. https://doi.org/10.3390/e22101096
Papadakis E., Adams B., Gao S., Martins B., Baryannis G., Ristea A. 2022. Explainable artificial Intelligence in the Spatial Domain (X-GeoAI). Transactions in GIS, 26(6): 2413–2414. https://doi.org/10.1111/tgis.12996
Prasad S., Chanussot J., Li J. 2024. Advances in Machine Learning and Image Analytics for GeoAI. Amsterdam, London, Cambridge, Elsevier, 364 p.
Prawira N., Wella, Natalia F. 2025. Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review. International Journal of Information Visualization, 9(4): 1783–1791.
Rajabi E., Etminani K. 2024. Knowledge-Graph-Based Explainable AI: A Systematic Review. Journal of Information Science, 50(4): 1019–1029. https://doi.org/10.1177/01655515221112844
Rutherford J., Gillespie A., Richardson R. 2004. The Territoriality of Pan-European Telecommunications Backbone Networks. Journal of Urban Technology, 11(3): 1–34. https://doi.org/10.1080/10630730500064166
Saleh H.J.H., Arrang J.R.T., de Barros Cardoso L.M.O. 2025. Applications of Geospatial AI in human geography and spatial networks: A literature review. EDRAAK, 2025: 76–84. https://doi.org/10.70470/EDRAAK/2025/010
Tang H. 2020. Regional Patterns and Hierarchical Tendencies: Analysis of the Network Connectivity of 63 Service-Oriented Tourist Cities in China. Sustainability, 12(16): e6532. https://doi.org/10.3390/su12166532
Tóth G., Elekes Z., Whittle A., Lee C., Kogler D.F. 2022. Technology Network Structure Conditions the Economic Resilience of Regions. Economic Geography, 98(4): 355–378. https://doi.org/10.1080/00130095.2022.2035715
Tranos E., Gillespie A. 2011. The Urban Geography of Internet Backbone Networks in Europe: Roles and Relations. Journal of Urban Technology, 18(1): 35–50. https://doi.org/10.1080/10630732.2011.578408
Tsiotas D. 2025. Socioeconomic Attributes in the Topology of the Intercity Road Network in Greece. Future Transportation, 5(1): e3. https://doi.org/10.3390/futuretransp5010003
Wilke A., Ngonga Ngomo A.-C. 2023. LauNuts: A Knowledge Graph to Identify and Compare Geographic Regions in the European Union. In: The Semantic Web. Cham, Springer Nature Switzerland: 408–418. https://doi.org/10.1007/978-3-031-33455-9_24
Xing J., Sieber R. 2023. The Challenges of Integrating Explainable Artificial Intelligence into GeoAI. Transactions in GIS, 27(3): 626–645. https://doi.org/10.1111/tgis.13045
Yan B. 2019. Geographic Knowledge Graph Summarization. Amsterdam, IOS Press, 168 p.
Zang Y., Jiaxiang E., Fiondella L. 2024. A Network Reliability Analysis Method for Complex Real-Time Systems: Case Studies in Railway and Maritime Systems. Mathematics, 12(19): e3014. https://doi.org/10.3390/math12193014
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