Digital traces of the population as a data source on migration flows In the Russian Arctic
Abstract
The digitalization of the economy and public life has expanded the possibilities of studying the population using digital traces – information that accumulates in the digital environment. Using digital traces, the article explores the migration of the population of the Russian Arctic, a huge macro-region that has experienced a significant outflow of population over the past decades. The text summarizes the experience of using digital traces in demographic research and formulates their strengths and limitations. Data from several digital platforms were used to study the population of the Russian Arctic. An analysis of the profiles of users of the social network VK.com made it possible to study the migration movements of the population of the Russian Arctic, and the data of the ticket service Tutu.ru provided information on air and rail movements. Using network analysis methods, the author studied migration and transport flows in the Russian Arctic at the municipal level. The article defines the features of migration and transport networks in the Arctic: low density, large distances between nodes, high relative mobility with small volumes of movements in absolute terms, a high proportion of hubs in migration exchange. The author identifies migration hubs and clusters, and migration flows are classified according to the directions of movement and types of municipalities. The text shows that the connectivity of the Arctic territories among themselves remains low, and the positive migration balance is mainly in regional capitals or cities outside the Arctic. The results of the study will improve the understanding of migration processes in the North and the Arctic, as well as the quality of demographic forecasts through more accurate modeling of migration flows.
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References
Дудина В.И. (2021). «Пересборка социологии»: цифровой поворот и поиски новой теоретической оптики. Социологические исследования, 11, 3-11. https://doi.org/10.31857/S013216250016829-4.
Замятина Н.Ю., Яшунский А.Д. (2018.). Виртуальная география виртуального населения. Мониторинг общественного мнения. Экономические и социальные перемены, 1, 117-137. https://doi.org/10.14515/monitoring.2018.1.07.
Калабихина И.Е., Лукашевпич Н.В., Банин Е.П., Алибаева К.В., Реблей С.М. (2021). Автоматическое извлечение мнений пользователей социальных сетей по вопросам репродуктивного поведения. Программные системы: теория и приложения, 12:4(51), 33-63. https://doi.org/10.25209/2079-3316-2021-12-4-33-63.
Китчин Р. (2021). Сетевой урбанизм, основанный на данных. В Е. Лапина-Кратасюк, О. Запорожец, А. Возьянов (Ред.), Сети города: Люди. Технологии. Власти (сс. 58-80). Москва: Новое литературное обозрение.
Смирнов А.В. (2021). Цифровое общество: теоретическая модель и российская действительность. Мониторинг общественного мнения: экономические и социальные перемены, 1(161), 129-153. https://doi.org/10.14515/monitoring.2021.1.1790.
Смирнов А.В. (2022). Прогнозирование миграционных процессов методами цифровой демографии. Экономика региона, 18(1), 133-145. https://doi.org/10.17059/ekon.reg.2022-1-10.
Срничек Н. (2020). Капитализм платформ. Москва: Издательский дом Высшей школы экономики.
Судакова А.Е. (2020). Миграция ученых: цифровой след и наукометрия. Перспективы науки и образования, 3(45), 544-557. https://doi.org/10.32744/pse.2020.3.39.
Тард Г. (2016). Монадология и социология. Пермь: Гиле Пресс.
Фаузер В.В., Лыткина Т.С. (2017). Миграционные процессы на российском Севере Социальная политика и социология. 16:1 (120), 141-149. https://doi.org/10.17922/2071-3665-2017-16-1-141-149.
Фаузер В.В., Смирнов А.В. (2020). Миграции населения российской Арктики: модели, маршруты, результаты. Арктика: экология и экономика, 4(40), 4-18. https://doi.org/10.25283/2223-4594-2020-4-4-18.
Ahmad I., Flanagan R., Staller K. (2020). Increased internet search interest for GI symptoms may predict COVID-19 cases in US hotspots. Clinical Gastroenterology and Hepatology, 18(12), 2833-2834. https://doi.org/10.1016/j.cgh.2020.06.058.
Alburez-Gutierrez D., Aref S., Gil-Clavel S., Grow A., Negraia D.V., Zagheni E. (2019). Demography in the Digital Era: New data sources for population research. In: SIS2019. Smart statistics for smart applications. Milano: Pearson. https://doi.org/10.31235/osf.io/24jp7.
Billari F.C., D’Amuri F., Marcucci J. (2013). Forecasting births using Google. Population Association of America Annual Meeting. https://paa2013.princeton.edu/papers/131393.
Boullier D. (2017). Big data challenges for the social sciences: from society and opinion to replications. In eSymposium, 7(2), 1-17. https://www.boullier.bzh/wp-content/uploads/EBul-Boullier-Jul2017.pdf
Cesare N., Lee H., McCormick T., Spiro E., Zagheni E. (2018). Promises and pitfalls of using digital traces for demographic research. Demography, 55, 1979-99. https://doi.org/10.1007/s13524-018-0715-2.
Danchev V., Porter M.A. (2018). Neither global nor local: Heterogeneous connectivity in spatial network structures of World migration. Social Networks, 53, 4-19. https://doi.org/10.1016/j.socnet.2017.06.003
Danchev V., Porter M.A. (2021). Migration networks: applications of network analysis to macroscale migration patterns. In M. McAuliffe (Ed.), Research handbook on international migration and digital technology (pp. 70-90). Cheltenham: Edward Elgar Publishing. https://doi.org/10.4337/9781839100611.
Gansner E., Koren Y., North S. (2004). Graph Drawing by Stress Majorization. Lecture Notes in Computer Science, 3383, 239-250. https://doi.org/10.1007/978-3-540-31843-9_25.
Gebru R., Krause J., Wang Y., Chen D., Deng J. Aiden E.L., Fei-Fei L. (2017). Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States. PNAS, 114(50), 13108-13113. https://doi.org/10.1073/pnas.1700035114.
Golder S.A., Macy M.W. (2014). Digital footprints: opportunities and challenges for online social research. Annual Review of Sociology, 40(1), 129-152. https://doi.org/10.1146/annurev-soc-071913-043145.
Edelmann A., Wolff T., Montagne D., Bail C. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46, 61-81. https://doi.org/10.1146/annurev-soc-121919-054621.
Fruchterman T.M.J., Reingold E.M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102.
Heleniak T., Bogoyavlenskiy D. (2014). Arctic Populations and Migration. In Larsen J.N., Fondahl G., Rasmussen H. (Eds.), Arctic Human Development Report. Regional Processes and Global Linkages (pp. 53-104). Copenhagen: Nordic Council of Ministers. https://doi.org/10.6027/TN2014-567.
Hughes C., Zagheni E., Abel G., Wi´sniowski A., Sorichetta A., Weber I., Tatem A.J. (2016). Inferring migrations: Traditional methods and new approaches based on mobile phone, social media, and other big data. Luxembourg: Publications Office of the European Union. https://doi.org/10.2767/61617.
Igntatow G. (2016). Theoretical foundations for digital text analysis. Journal for the Theory of Social Behaviour, 46(1), 104-120. https://doi.org/10.1111/jtsb.12086.
Katzenbach C., Bächle T.C. (2019). Defining concepts of the digital society. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1430.
Kitchin R. (2014). Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, 1(1), 1-12. https://doi.org/10.1177/2053951714528481.
Lazer D., Radford J. (2017). Data ex Machina: introduction to big data. Annual Review of Sociology, 43(1), 19-39. https://doi.org/10.1146/annurev-soc-060116-053457.
Ledford H. (2020). How Facebook, Twitter and other data troves are revolutionizing social science. Nature, 582, 328-330. https://doi.org/10.1038/d41586-020-01747-1.
Maier G., Vyborny M. (2008). Internal migration between US States: A social network analysis. In J. Poot, B. Waldorf, L.W. Wissen (Eds.), Migration and Human Capital. Cheltenham, UK: Edward Elgar Publishing. URL: https://www.econstor.eu/handle/10419/117573.
McCormick T.H., Lee H., Cesare N., Shojaie A., Spiro E.S. (2017). Using Twitter for demographic and social science research: tools for data collection and processing. Sociological Methods & Research, 46(3), 390-421. https://doi.org/10.1177/0049124115605339.
Petrov A.N., Welford M., Golosov N., DeGroote J., Degai T., Savelyev A. (2021). The “second wave” of the COVID-19 pandemic in the Arctic: regional and temporal dynamics. International Journal of Circumpolar Health, 80(1). https://doi.org/10.1080/22423982.2021.19254461.
Raghavan U.N., Albert R., Kumara S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(3). https://doi.org/10.1103/physreve.76.036106.
Taylor L., Floridi L., van der Sloot L. (Eds.). (2017). Group privacy: New challenges of data technologies. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-46608-8.
Zagheni E., Weber I., Gummadi K. (2017). Leveraging Facebook’s advertising platform to monitor stocks of migrants. Population and Development Review, 43(6178), 721-734. https://doi.org/10.1111/padr.12102.
Zamyatina N., Yashunsky A. (2017). Migration cycles, social capital and networks. A new way to look at Arctic mobility. In M. Laruelle (Ed.), New Mobilities and Social Changes in Russia’s Arctic Regions (pp. 59-84). London and New York, Routledge.
