Analysis of the cyclical movement of the population of the city of Moscow based on data from mobile operators

DOI: 10.35595/2414-9179-2024-2-30-309-326

View or download the article (Rus)

About the Authors

Alexander N. Bereznyatskiy

Central Economics and Mathematics Institute of the Russian Academy of Sciences,
47, Nakhimovsky ave., Moscow, 117418, Russia,
E-mail: artandtech@yandex.ru

Svetlana V. Badina

Lomonosov Moscow State University, Faculty of Geography,
1, Leninskie Gory, Moscow, Russia, 119991,

Bernardo O’Higgins University,
Avenida Viel, 1497, Santiago, Chile,

E-mail: bad412@yandex.ru

Roman A. Babkin

Plekhanov Russian University of Economics, Laboratory of Regional Policy and Regional Investment Processes,
36, Stremyanny ln., Moscow, 117997, Russia,
E-mail: babkin_ra@mail.ru

Abstract

The paper examines the dynamics of the population of the city of Moscow from the point of view of the formation of various cyclical fluctuations. To analyze the phenomena, an extensive data set is used on the position of subscribers of mobile operators in the city with a time frame of 30 minutes for 2018–2019. The availability of data on the population of the city with such a high frequency of updating allowed us to obtain unique information about the patterns of movement of the city’s population, numerically characterize the qualitative results of chronogeography and the concept of social time. Based on the analysis, a model of the movement of the city’s population is developed, consisting of a trend around which fluctuations of various types occur. The quasi-cycle associated with the change of seasons, the daily cycle, the weekly cycle, as well as the cyclicity of the daily gradient associated with the seasons are revealed. At the same time, there are disturbances in cycles associated with the presence of holidays. The methodology used is based on the method of empirical mode decomposition EMD, nonlinear regression analysis. The decomposition of the daily pulsation series into gradients and trends of changes between night and daytime hours on the example of Moscow districts allowed us to identify eight chronotypes of districts: “classic attractors”, “plateaued attractors”, “lark attractors”, “owl attractors”, “classic bedrooms”, “transit bedrooms”, “sporadic attractors” and “bedroom-attractors” reflecting the functional roles of municipalities and the peculiarities of their position in the system of intra-urban mobility.

Keywords

population dynamics, cycles, social time, mobile operator’s data, Moscow

References

  1. Babkin R.A., Badina S.V., Bereznyatsky A.N. Assessment of temporal variability in the level of population vulnerability to natural and man-made hazards. Geography, Environment, Sustainability, 2022. V. 15. No. 4. P. 90–101. DOI: 10.24057/2071-9388-2022-116.
  2. Babkin R.A., Badina S.V., Mikhaylov A. Intraday dynamics of the urban population in studies of natural and man-made risks (case study of Moscow, Russia). Lecture Notes in Networks and Systems, 2023. No. 622. P. 417–427. DOI: 10.1007/978-3-031-28086-3_35.
  3. Baburin V.L., Chistyakov P.A. et al. The space of cycles. Mir–Russia–region. Moscow: URSS, 2007. 316 p. (in Russian).
  4. Badina S., Babkin R., Bereznyatsky A., Bobrovskiy R. Spatial aspects of urban population vulnerability to natural and man-made hazards. City and Environment Interactions, 2022. V. 15. P. 100082. DOI: 10.1016/j.cacint.2022.100082.
  5. Davidovich V.G. Settlement in suburban areas (quantitative patterns). Questions of geography. Collection 87: Settlement in suburban areas. Moscow: Mysl’, 1971. P. 5–43 (in Russian).
  6. Deville P., Linard C., Martine S., Gilbert M., Steven F., Gaughan A., Blondel V., Tatem A. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences. USA, 2014. V. 111. No. 45. P. 88–93. DOI: 10.1073/pnas.1408439111.
  7. Ellegard K. Thinking time geography: Concepts, methods and applications. New York: Taylor & Francis, 2019. 172 p.
  8. Geyer H.S., Kontuly T. A theoretical foundation of the concept of differential urbanization. International Regional Science Review, 1993. V. 15. Iss. 2. P. 157–177. DOI: 10.1177/016001769301500202.
  9. Gibbs J. The evolution of population concentration. Economic Geography, 1963. V. 2. P. 119–129.
  10. Ho E.L.E. Social geography I: Time and temporality. Progress in Human Geography, 2021. V. 45. No. 6. P. 1668–1677. DOI: 10.1177/03091325211009304.
  11. Huang N., Shen Z., Long S., Wu M.L.C., Shih H., Zheng Q., Yen N.-C., Tung C.-C., Liu H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998. V. 454. P. 903–995. DOI: 10.1098/rspa.1998.0193.
  12. Karachurina L. Internal Russian labor migration: prevalence and geography of movements. Economic Development of Russia, 2013. V. 20. No. 7. P. 45–49 (in Russian).
  13. Khorev B.S., Smolina T.K., Vishnevsky A.G. Pendulum migration in the USSR and its study. Problems of migration of population and labor resources. Moscow: Statistika, 1970. P. 100–112 (in Russian).
  14. Klaassen L.H., Scimeni G. Theoretical issues in urban dynamics. Dynamics of Urban Development. Aldershot: Gower, 1981. P. 8–28.
  15. Lappo G.M., Maergoyz I.M. Geography and urbanization. Geography issues. Collection 96: The Urbanization of the World. Moscow: Mysl’, 1974. P. 5–18 (in Russian).
  16. Makhrova A.G., Kirillov P.L. Seasonal pulsation of settlement in the Moscow agglomeration under the influence of suburban and labor pendulum migration: Approaches to study and assessment. Regional Studies, 2015. No. 1 (47). P. 117–125 (in Russian).
  17. Makhrova A.G., Kirillov P.L., Bochkarev A.N. Pendulum labor migration of the population in the Moscow agglomeration: experience in estimating flows using data from mobile operators. Regional studies, 2016. No. 3 (53). P. 71–82 (in Russian).
  18. May J., Thrift N.J. Time Space: Geographies of Temporality. London: Psychology Press, 2001. 323 p.
  19. Nefedova T.G. Seasonal work in the migration system in post-Soviet Russia. Background. Demoscope Weekly, 2015. No. 641-642. Web resource: http://demoscope.ru/weekly/2015/0641/demoscope641.pdf (accessed 31.07.2024) (in Russian).
  20. Nefedova T.G., Makhrova A.G. Summer residents between two capitals. Journey from St. Petersburg to Moscow: 222 years later. Book 1. Moscow: URSS, 2015. P. 190–207.
  21. Selected works of Kondratievsky Conjunctural Institute. Moscow: Economics, 2010. 719 p. (in Russian).
  22. Shitova Yu.Yu. Pendulum migration in the Moscow Region: a comprehensive socio-economic analysis. Saransk: Mordovian University Press, 2009. 188 p. (in Russian).
  23. Sorokin P.A., Merton R.K. Social Time: A Methodological and Functional Analysis. American Journal of Sociology, 1937. V. 42. No. 5. P. 615–29.
  24. Spatial and temporal analysis of the settlement system of the Moscow capital region. Moscow: Institute of Geography of USSR Academy of Sciences, 1988. 204 p. (in Russian).
  25. Starikova A.V., Demidova E.E. Socio-economic phenomena in the digital age: A chronogeographic approach to geovisualization. Geopolitics and Ecogeodynamics of Regions, 2019. V. 5 (15). No. 3. P. 344–352 (in Russian).
  26. Zemlyansky D.Yu. Seasonal rhythms of socio-economic processes in the regions of Russia: Abstract of the dissertation of the PhD of Geographical Sciences. Moscow: Lomonosov Moscow State University, 2011. 26 p. (in Russian).

For citation: Bereznyatskiy A.N., Badina S.V., Babkin R.A. Analysis of the cyclical movement of the population of the city of Moscow based on data from mobile operators. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2024. V. 30. Part 2. P. 309–326. DOI: 10.35595/2414-9179-2024-2-30-309-326 (in Russian)