Work commuting of the population in the Moscow agglomeration: Estimating commuting flows using mobile operator data

Махрова, А. Г.; Kirillov, P. L.; Bochkarev, A. N.

doi:10.1134/s2079970517010051

Cited by 19 publications

(12 citation statements)

References 5 publications

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“…The following two main data sources are used: For assessing the homebuying activity of residents of Russian cities and regions in the MCR housing market (capital mobility), we used a depersonalized database (based on information from the Federal Registration Service and developers), which contains the permanent registration addresses of homebuyers in the primary housing market of Moscow and Moscow oblast 6 ;…”

Section: Methodsmentioning

confidence: 99%

“…(1) The localization index of labor migrants to the Moscow agglomeration with respect to the population of a territorial unit is calculated by the formula 6 The database contains data on 37800 homebuyers in 2012-2014, or about 35% of the average annual number of co-investment contracts (for 2012-2015 in MCR, it was 80700-135500) 7 The general problems of the reliability of the ARPC-2010 data [7] are exacerbated by the fact that the questionnaires do not assume that a person included in the census of the region where said person is employed has indicated his/her region of permanent residence. In addition, ARPC-2010 does not make it possible to explicitly separate commuting and temporary migratory work (regular labor migrations outside the daily cycle).…”

Section: Methodsmentioning

confidence: 99%

“…The total number of labor migrants from Moscow oblast to federal subjects were 890 900 people; from Moscow, 26000 people; 98200 people from other regions were employed in Moscow oblast (excluding 26000 Muscovites). For comparison, based on the cellular operator data, the value of the centripetal flow of commuters is estimated at 1.3 mln people; centrifugal, 0.4 mln people [6]. 9 According to estimates of a population survey on employment problems conducted by Rosstat in 2016, the number of interregional labor migrants (including commuters from the regions neighboring the oblast) who enter the MCR for employment reached 1.6 mln people.…”

Section: Methodsmentioning

confidence: 99%

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Migration and Investment Activity of Residents of Russian Cities in the Housing Market of Moscow Agglomeration

Kurichev

Kuricheva²

2019

Reg. Res. Russ.

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Based on the data on addresses of real estate buyers, we assess the investment activity of residents of Russian regions and cities in the primary housing market of the Moscow capital region (MCR) compared to the activity of their labor migrations to the MCR. The objects of our analysis are 149 Russian cities and 80 remaining parts of regions. This enabled us to analyze the specifics of migration and investment behavior for the first time, taking into account differentiation between cities and rural areas, between size classes of cities, and between individual large cities. This enabled us to fill in the gap in assessing the mobility of inputs, i.e., capital and labor. A sharp contrast between settlements of different sizes was revealed in the nature of their interaction with the MCR agglomeration. The intensity of labor migration to the Moscow agglomeration is decreasing rapidly and monotonically with increasing settlement size. The activity of nonresident homebuyers, depending on the population of the city of their residence, varies nonmonotonically, reaching its highest level for cities with populations of 250000-500000 people for Moscow's housing market and 100000-500000 people in Moscow oblast. Small towns and rural areas (except for the Khanty-Mansi and Yamalo-Nenets autonomous okrugs) are a source of labor for the Moscow agglomeration and show low investment activity in the capital's housing market. Million-plus cities provide a negligible inflow of labor migrants and are characterized by moderate activity in the MCR housing market, close to the national average. Compared to the premium housing and labor market of the City of Moscow, investment and migration flows to Moscow oblast are shifted to smaller settlements and lower-income regions. The attraction of Moscow oblast rapidly decreases with distance, extending to first-and second-order neighbors, while Moscow's influence is nationwide.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Migration and Investment Activity of Residents of Russian Cities in the Housing Market of Moscow Agglomeration

Kurichev

Kuricheva²

2019

Reg. Res. Russ.

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“…A similar approach based on other initial data was implemented in an analysis of mobile communications in Belgium [25]. Another interesting attempt was made to use mobile data to estimate migration flows in the Moscow agglomeration [14]. However, this approach does not correspond to the area of "information flows with channels and traffic.…”

Section: Definition Of Potential Of Mobile Communication Fieldmentioning

confidence: 99%

Regional Information Flows: Existing and New Approaches to Geographical Study

Блануца

Cherepanov

2019

Reg. Res. Russ.

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The global experience of geographical study of information flows is reviewed for the first time based on journal papers and monographs published in 1988-2017. It is established that the issue is understudied, subject to a shift from understanding the relevance of such studies to attempts to measure and structure information flows. The key areas of geographic study are identified: flows without channels and traffic, channels without traffic, traffic without channels, and flows with channels and traffic. No publication has been retrieved for the fourth area, which is most important for regional studies. Three new approaches are proposed in the framework of next-generation communication network studies and in line with the fourth area: uplink-downlink traffic distribution, redistribution of traffic inside a region proportionally to the population size of settlements, and the definition of potential of mobile communication field. The new approaches are tested with a case study of Russia's Irkutsk oblast.

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“…Estimation of T.G. Nefedova for 2018 using the 2016 All-Russian Agricultural Census data andMakhrova et al (2017).5 The missing values were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF) technique implemented in the R package 'rtsa'(Filipponi, 2019).6 We used the assumption (followingMakhrova et al, 2017) that the maximum number of people per second home in the summer season is three.…”

mentioning

confidence: 99%

Nighttime‐lights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl Oblast)

Sheludkov

Starikova

2022

Regional Science Policy & Practice

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Second home mobility is a well‐known phenomenon in many countries, but is widely prominent in Russia, where millions of city dwellers move to rural areas during the summertime. Combating long‐term economic decline and depopulation, second home mobility creates a promising opportunity to revitalize the countryside. While this phenomenon is largely neglected by official statistics, we suggest using satellite imagery of nighttime lights to investigate its spatial and temporal patterns. We did this with the example of Yaroslavl Oblast in Russia. This region neighbors the Moscow Capital Region. It experiences a significant inflow of second home residents. By tracking the seasonal pixel‐wise changes of nighttime light radiance in monthly composites of satellite imagery from 2015 to 2019, we located hotspots of second homes and factors determining their spatial spread in rural areas. The results were evaluated with field research. Our results confirmed earlier conclusions that second homes’ locations in rural areas are largely determined by their proximity to Moscow, natural conditions, and transport accessibility. City dwellers often choose small and even fully abandoned villages for their second homes, which stresses the important role of second home mobility in preserving cultural landscapes. The proposed data and methods are limited by missing data for the northern regions during summer months and are more suitable for areas beyond the urban fringe where nighttime‐lights data are not biased by the ‘overglow’ of large cities.

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Work commuting of the population in the Moscow agglomeration: Estimating commuting flows using mobile operator data

Cited by 19 publications

References 5 publications

Migration and Investment Activity of Residents of Russian Cities in the Housing Market of Moscow Agglomeration

Migration and Investment Activity of Residents of Russian Cities in the Housing Market of Moscow Agglomeration

Regional Information Flows: Existing and New Approaches to Geographical Study

Nighttime‐lights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl Oblast)

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