Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg

Fantazzini, Dean; Pushchelenko, Julia; Mironenkov, Alexey; Kurbatskii, Alexey

doi:10.3390/forecast3040048

Cited by 10 publications

(7 citation statements)

References 90 publications

(123 reference statements)

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“…Similarly, three classes of model were employed by Fantazzini et al for out-of-sample forecasting of interregional migration in Russia; these include short-term forecasting using ARIMA and Google-augmented ARIMA models, as well as multivariate models for long-term forecasting. The empirical analysis finds that including Google Trends data in a model enhances the prediction of migration flows [64].…”

Section: Time-series Modelsmentioning

confidence: 99%

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Huang

Yang

et al. 2023

Sustainability

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As a fundamental, holistic, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities, given the constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Based on the analysis, there is no “best” migration prediction model, and data are key to forecasting human migration. Social media’s popularity and its increase in data have enabled the application of artificial intelligence in population migration prediction, which has attracted the attention of researchers and government administrators. Future research will aim to incorporate uncertainty into the predictive analysis framework, and explore the characteristics of population migration behaviors and their interactions. The integration of machine-learning and traditional data-driven models will provide a breakthrough for this purpose.

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Section: Time-series Modelsmentioning

confidence: 99%

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Huang

Yang

et al. 2023

Sustainability

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“…internal migration, long-term, [38]; international migration, long-term, [39]; international migration, long-term, [40] ; international migration, long-term, [41]; international migration, short-term, [42]. internal migration, short-term, [43]; international migration, short-term, [44]; international migration, long-term, [45]; international migration, long-term, [46]; international migration, long-term, [47]; international migration, long and shortterm, [48]; international migration, longterm, [49]; internal migration, short-term, [50]; internal migration, short and longterm, [51].…”

Section: Research Progressmentioning

confidence: 99%

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Pu¹,

Huang²,

Yang³

et al. 2023

Preprint

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As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

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“…In addition, between 2010 and 2021, nearly 150,000 Russians were internally displaced, mostly by floods or fires, which creates short‐term challenges for the Russian state different from long‐term migration (Katsova, 2022). Projections for future migration suggest a continued minor role for climate change as a motivating factor (Fantazzini et al, 2021; Karachurina & Mkrtchyan, 2022; Sardadvar & Vakulenko, 2020).…”

Section: Climate Change Impactsmentioning

confidence: 99%

Russia in a changing climate

Javeline,

Orttung,

Robertson

et al. 2023

WIREs Climate Change

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Climate change will shape the future of Russia, and vice versa, regardless of who rules in the Kremlin. The world's largest country is warming faster than Earth as a whole, occupies more than half the Arctic Ocean coastline, and is waging a carbon‐intensive war while increasingly isolated from the international community and its efforts to reduce greenhouse gas emissions. Officially, the Russian government argues that, as a major exporter of hydrocarbons, Russia benefits from maintaining global reliance on fossil fuels and from climate change itself, because warming may increase the extent and quality of its arable land, open a new year‐round Arctic sea route, and make its harsh climate more livable. Drawing on the collective expertise of a large group of Russia‐focused social scientists and a comprehensive literature review, we challenge this narrative. We find that Russia suffers from a variety of impacts due to climate change and is poorly prepared to adapt to these impacts. The literature review reveals that the fates of Russia's hydrocarbon‐dependent economy, centralized political system, and climate‐impacted population are intertwined and that research is needed on this evolving interrelationship, as global temperatures rise and the international economy decarbonizes in response.This article is categorized under: Policy and Governance > National Climate Change Policy Trans‐disciplinary Perspectives > National Reviews Trans‐disciplinary Perspectives > Regional Reviews

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Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg

Cited by 10 publications

References 90 publications

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Russia in a changing climate

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