The study of labor pendulum migration using big data processing technologies and the capabilities of the VKontakte social network

Official statistical data do not allow estimating the magnitude of labor pendulum migration in the context of regions and municipalities on a regular basis. The purpose of this research was to study the possibility of using open data of the VKontakte social network to identify and analyze labor pend...

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Bibliographic Details
Main Authors: Logacheva Natalia M., Uskova Anna, Salomatova Julia
Format: Article
Language:English
Published: EDP Sciences 2023-01-01
Series:E3S Web of Conferences
Subjects:
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/72/e3sconf_rec2023_04007.pdf
Description
Summary:Official statistical data do not allow estimating the magnitude of labor pendulum migration in the context of regions and municipalities on a regular basis. The purpose of this research was to study the possibility of using open data of the VKontakte social network to identify and analyze labor pendulum migration on the example of Ekaterinburg and 12 satellite cities. Depersonalized information of users of the VKontakte social network and Rosstat data acted as empirical data. The research established a connection between the distance from the city of residence to Ekaterinburg and the proportion of citizens-users of the VKontakte network included in the process of pendulum labor migration. The analysis of incoming and outgoing flows of labor pendulum migration showed that despite significant differentiation by cities, in all cities the outgoing flow of labor migrants to Ekaterinburg exceeded the incoming one. The results of the research can be used in the analysis of the features, intensity and directions of pendulum migration for their consideration in the elaboration of strategic documents in the field of spatial development of territories. Further research by the authors will be aimed at improving the methodology for studying migration processes that occur between cities of millions and satellite cities, in order to identify agglomeration effects using big data technologies.
ISSN:2267-1242