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Article

Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics

1
Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria
2
Department of Data Science, University of Passau, 94032 Passau, Germany
3
Information Technology and Systems Management, Salzburg University of Applied Sciences, 5412 Puch, Austria
4
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(8), 498; https://doi.org/10.3390/ijgi10080498
Received: 8 June 2021 / Revised: 16 July 2021 / Accepted: 20 July 2021 / Published: 23 July 2021
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data to provide more information about refugee movements. The approach combines methods to analyse the textual, temporal and spatial features of social media data and the number of arriving refugees of historical refugee movement statistics to provide relevant and up to date information about refugee movements and expected numbers. The results include spatial patterns and factual information about collective refugee movements extracted from social media data that match actual movement patterns. Furthermore, our approach enables us to forecast and simulate refugee movements to forecast an increase or decrease in the number of incoming refugees and to analyse potential future scenarios. We demonstrate that the approach proposed in this article benefits refugee management and vastly improves the status quo. View Full-Text
Keywords: spatio-temporal; machine learning; refugee movements; simulation; forecasting; social media spatio-temporal; machine learning; refugee movements; simulation; forecasting; social media
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MDPI and ACS Style

Havas, C.; Wendlinger, L.; Stier, J.; Julka, S.; Krieger, V.; Ferner, C.; Petutschnig, A.; Granitzer, M.; Wegenkittl, S.; Resch, B. Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. ISPRS Int. J. Geo-Inf. 2021, 10, 498. https://doi.org/10.3390/ijgi10080498

AMA Style

Havas C, Wendlinger L, Stier J, Julka S, Krieger V, Ferner C, Petutschnig A, Granitzer M, Wegenkittl S, Resch B. Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. ISPRS International Journal of Geo-Information. 2021; 10(8):498. https://doi.org/10.3390/ijgi10080498

Chicago/Turabian Style

Havas, Clemens, Lorenz Wendlinger, Julian Stier, Sahib Julka, Veronika Krieger, Cornelia Ferner, Andreas Petutschnig, Michael Granitzer, Stefan Wegenkittl, and Bernd Resch. 2021. "Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics" ISPRS International Journal of Geo-Information 10, no. 8: 498. https://doi.org/10.3390/ijgi10080498

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