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Topology of a Settlement Network as a Factor of Rural Population Dynamics (a Case Study of Tyumen Oblast)

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Abstract

During the last decades, the Russian countryside has been strongly losing in the number of residents. People are moving to large cities, mainly, to regional capitals. Migration outflow increases with distance from cities. Centripetal tendencies in migration can be strengthened or mitigated in the local context due to the specific properties of a territory, rooted in its history. The authors consider the configuration of a settlement network as one such contextual factor. The study poses two questions: do the topological properties of a settlement network, namely, connectivity and centralization, affect the rate the settlement network is shrinking, and how do the population dynamics in individual settlements depend on their position in a settlement network. The authors addressed these questions using Tyumen oblast as a case region, where they studied the settlement network dynamics in 2002–2010. The settlement network was divided into segments according to the cluster analysis based on the shortest road distances matrix. Then the authors measured the connectivity and centralization of each segment and centrality metrics for individual settlements. The results showed no statistical relationship between the topological properties of the network segments and their depopulation rates. Yet, for individual settlements, the position in the settlement network was a significant factor for population dynamics. Together with the population size, the centrality metrics explained 23–24% of the variance in population dynamics among the settlements between 2002 and 2010. Outside the metropolitan area of Tyumen, the settlements with high interdistrict centrality were growing. It is noteworthy that the configuration of the settlement network at the interdistrict scale rooted back in times of the Russian colonization of Western Siberia in the 17th–19th centuries and largely followed the river network pattern. In the 20th century, the rivers lost their transport role, yet the roads connected settlements within existed settlement groups reaffirming the riverine pattern.

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Notes

  1. In the article, we use the term of settlement contraction to denote spatial concentration of the population as a part of the general shrinkage of the socioeconomic space, which is accompanied by degradation of the settlement network [1, 16].

  2. Similarly, when considering the location of production facilities, A.P. Gorkin and L.V. Smirnyagin argued for separating the factors of the production itself (location factors), for example, requirements on the quantity and quality of labor, from the factors of a territory (location conditions), for example, the actual number and qualifications of workers in specific settlements [5].

  3. The region is considered without autonomous okrugs.

  4. This is the fastest population increase rate among Russian cities with a population of over 500 000 people in the indicated period (even taking into account that the expansion of the city’s borders in 2014 accounted for 13 000 of the population increase).

  5. In transport geography, the metric is known as the index of transport accessibility. For details, see [14].

  6. Linear objects with the tag ‘highway’ equals to ‘trunk’, ‘primary’, ‘secondary’, ‘tertiary’, and ‘unclassified’ were used. The downloading date: August 11, 2018. © OpenStreetMap contributors, 2018.

  7. GRASS Development Team, 2017. Geographic Resources Analysis Support System (GRASS) Software, Version 7.2. http://grass.osgeo.org.

  8. The indicator was selected for the very research task and the empirical material used. In general, the change in the coefficient of variation of settlements’ sizes cannot be considered as a universal quantitative indicator of the rates of settlement contraction. The settlement pattern can become also more polarized with the expansion of the settlement network rather than its contraction (for example, in a case of the rapid growth of centers when colonizing a territory).

  9. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018. https://www.R-project.org/.

  10. Below these two levels will be designated as interdistrict (6 groups) and district (18 groups) levels respectively.

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Funding

The research methodology was developed by A.V. Sheludkov (Methods section) at the Institute of Geography, Russian Academy of Sciences, within Russian Science Foundation project no. 19-17-00174 “Early Developed Regions under Socioeconomic Polarization and Shrinkage of Active Space in European Russia.”

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Authors and Affiliations

  1. Institute of Geography, Russian Academy of Sciences, 119017, Moscow, Russia

    A. V. Sheludkov

  2. Institute of Cell Biophysics, Russian Academy of Sciences, 142290, Pushchino, Moscow oblast, Russia

    M. A. Orlov

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  1. A. V. Sheludkov
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  2. M. A. Orlov
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Correspondence to A. V. Sheludkov.

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Translated by L. Solovyova

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Sheludkov, A.V., Orlov, M.A. Topology of a Settlement Network as a Factor of Rural Population Dynamics (a Case Study of Tyumen Oblast). Reg. Res. Russ. 10, 388–400 (2020). https://doi.org/10.1134/S2079970520030119

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