Affiliation:
1. Plekhanov Russian University of Economics, Laboratory of Regional Policy and Regional Investment Processes, Stremyanny lane, 36, 117997, Moscow, Russia;
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.
Reference15 articles.
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.