Телекоммуникационная связанность российских регионов: оценка с помощью геопространственного искусственного интеллекта
Работа выполнена за счет средств государственного задания Института географии им. В.Б. Сочавы СО РАН (FWEM-2026-0006) «Общественно-географические основы анализа территориальных структур Сибири в контексте новой восточной политики России и усиления евразийской континентальной интеграции» № 126020516552-3.
DOI:
https://doi.org/10.52575/2712-7443-2026-50-3-604-618Ключевые слова:
общественная география, магистральная линия связи, надежность сети, телекоммуникационно-сетевое положение, территориальная структура связи, граф знаний, Российская ФедерацияАннотация
Построена система геопространственного искусственного интеллекта в виде графа знаний, базы данных, базы знаний и четырех вспомогательных блоков. Для оценки телекоммуникационной связанности регионов России использовались данные ComNews о магистральной сети связи России в 2020–2024 гг., уточненные по авторской базе данных о трассировке оптоволоконных линий связи. Связанность 85 российских регионов проанализирована в трех аспектах – надежность линейно-узловой структуры, положение региона в сети и каркас магистральной связи. Выявлено 17 проблемных регионов по надежности. Проведена оценка телекоммуникационно-сетевого положения каждого региона по двум версиям. Идентифицирован каркас магистральной сети связи в виде одного центра, трех субцентров и соединяющих их регионов.
Скачивания
Библиографические ссылки
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).
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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
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Darwish D., Chemingui H. 2025. Recent Trends in Geospatial AI. New York, London, Beijing, IGI Global Scientific Publishing, 394 p.
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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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