Spatial representation of mobile operators’ data on pulsations of the Central Administrative District of Moscow population during 2019–2020 in the context of studying the vulnerability of the population to emergencies

DOI: 10.35595/2414-9179-2022-2-28-111-125

View or download the article (Rus)

About the Authors

Roman O. Bobrovskiy

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

Roman A. Babkin

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

Alexander N. Bereznyatskiy

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

Abstract

The high concentration of residents of large cities in certain localities requires a rethinking of existing methods for assessing the vulnerability of the population to various types of threats and to ways of mapping them. Moscow, being a megalopolis and the center of the largest European agglomeration, forms a zone of an increased level of natural and man-made risk for citizens, primarily due to spatial concentration and mobility of the population. The risks are especially high for the central part of Moscow (in the work considered within the boundaries of the Central Administrative District—CAO). The high business and cultural and entertainment attractiveness of this part of the capital contributes to the highest gradients of pulsations of crowding within the daily and weekday-weekend cycles. The present study is devoted to the qualitative display of these changes. To obtain the most detailed spatio-temporal information, the data of mobile operators on the localization of subscribers aggregated for January 2019–January 2020 were used. The paper tests the approach of displaying changes in the density characteristics of the population of the territory of the CAO districts by superimposing information on a pallet of 500 by 500 meters consolidated for fractional (30 minutes) time intervals of data (median population for all days of the year, separately for weekdays, weekends, and holidays). It was shown that for the central part of the capital, the gradients of daily pulsations on weekdays reach 220–320 %, and on weekends — 120–160 %. At the same time, in contrast to the sleeping areas of the city, seasonal fluctuations are much weaker here. The concentration of various cultural and entertainment activities in such areas as Tverskoy, Arbat and Yakimanka leads to pronounced festive changes in crowding, which are about 50 % stronger than the standard weekend pulsations.

Keywords

mobile phone Data, Moscow, vulnerability of the population to emergencies, population pulsations, “big data”, Central Administrative Okrug (CAO)

References

  1. Aubrecht C., Özceylan D., Steinnocher K., Freire S. Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Natural Hazards. 2013. Vol. 68. No. 1. P. 147–163.
  2. Babkin R.A. The experience of using the mobile phone data in economic geographical researches in foreign. Vestnik of Saint Petersburg University. Earth Sciences. 2021. No. 66 (3). DOI: 10.21638/spbu07.2021.301 (in Russian).
  3. Badina S.V., Babkin R.A., Bereznyatsky A.N. Prospects for the use of mobile operator data in studies of natural and man-made risk. Federalism. 2021. Vol. 26. No. 4 (104). P. 111–126. DOI: http://dx.doi.org/10.21686/2073-1051-2021-4-111-126 (in Russian).
  4. Bogorov V.G., Novikov A.V., Serova E.I. Self-knowledge of the city. The Archaeology of the periphery (proceedings of the Moscow urban forum). Moscow: Meganom & Institute Strelka, 2013. P. 380–405 (in Russian).
  5. Flanagan B.E., Gregory E.W., Hallisey E.J., Heitgerd J.L., Lewis B. A social vulnerability index for disaster management. Journal of homeland security and emergency management. 2011. Vol. 8. No. 1.
  6. Frigerio I., De Amicis M. Mapping social vulnerability to natural hazards in Italy: A suitable tool for risk mitigation strategies. Environmental Science & Policy. 2016. Vol. 63. P. 187–196.
  7. Fritchey G. SQL Server 2017. Query Performance Tuning: Troubleshoot and Optimize Query Performance. Apress, 2018.
  8. Grazhdankin A.I., Pecherkin A.S. On the influence of “integrated risk management” to growth in technological disasters. Problems of risk analysis. 2005. Vol. 2. No. 3. P. 279–280 (in Russian).
  9. IPCC: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer O., Pichs-Madruga R., Sokona Y., et al.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014. 1420 p.
  10. Makhrova A.G., Babkin R.A. Analysis of pulsations of the settlement system of the Moscow agglomeration using data from mobile operators. Regional studies. 2018. No. 2 (60). P. 68–78 (in Russian).
  11. Nelson K.S., Abkowitz M.D., Camp J.V. A method for creating high resolution maps of social vulnerability in the context of environmental hazards. Applied Geography. 2015. Vol. 63. P. 89–100.
  12. Orttung R.W., Anisimov O., Badina S., Burns C., Cho L., DiNapoli B., Jull M., Shaiman M., Shapovalova K., Silinsky L., Zhang E., Zhiltcova Y. Measuring the sustainability of Russia’s arctic cities. Ambio, 2020. P. 1–15.
  13. Osipov V.I., Larionov V.I., Burova V.N., Frolova N.I., Sushchev S.P. Methodology of natural risk assessment in Russia. Nat Hazards. 2017. No. 88. P. 17–41.
  14. Roncancio D.J., Nardocci A.C. Social vulnerability to natural hazards in São Paulo, Brazil. Natural Hazards. 2016. Vol. 84. No. 2. P. 1367–1383.
  15. UNDRR: Global Assessment Report on Disaster Risk Reduction. Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR), 2019. 425 p.

For citation: Bobrovskiy R.O., Babkin R.A., Bereznyatskiy A.N. Spatial representation of mobile operators’ data on pulsations of the Central Administrative District of Moscow population during 2019–2020 in the context of studying the vulnerability of the population to emergencies. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2022. V. 28. Part 2. P. 111–125. DOI: 10.35595/2414-9179-2022-2-28-111-125 (in Russian)