Next Article in Journal
Determination of Areas Susceptible to Landsliding Using Spatial Patterns of Rainfall from Tropical Rainfall Measuring Mission Data, Rio de Janeiro, Brazil
Previous Article in Journal
A Hybrid Process/Thread Parallel Algorithm for Generating DEM from LiDAR Points
Article

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

1
Geoinformation and Environmental Technology, Carinthia University of Applied Sciences, Villach, 9524, Austria
2
Geomatics Program, Fort Lauderdale Research and Education Center, University of Florida, Davie, FL-33314, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 302; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100302
Received: 5 June 2017 / Accepted: 24 June 2017 / Published: 2 October 2017
Over the past few years, analysts have begun to materialize the “Citizen as Sensors” principle by analyzing human movements, trends and opinions, as well as the occurrence of events from tweets. This study aims to use geo-tagged tweets to identify and visualize refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015, which was caused by war and political and economic instability in those regions. The focus of this study is on exploratory data analysis, which includes refugee trajectory extraction and aggregation as well as the detection of topical clusters along migration routes using the V-Analytics toolkit. Results suggest that only few refugees use Twitter, limiting the number of extracted travel trajectories to Europe. Iterative exploration of filter parameters, dynamic result mapping, and content analysis were essential for the refinement of trajectory extraction and cluster detection. Whereas trajectory extraction suffers from data scarcity, hashtag-based topical clustering draws a clearer picture about general refugee routes and is able to find geographic areas of high tweet activities on refugee related topics. Identified spatio-temporal clusters can complement migration flow data published by international authorities, which typically come at the aggregated (e.g., national) level. The paper concludes with suggestions to address the scarcity of geo-tagged tweets in order to obtain more detailed results on refugee migration patterns. View Full-Text
Keywords: tweets; trajectory; migration; hashtag; refugee; V-Analytics tweets; trajectory; migration; hashtag; refugee; V-Analytics
Show Figures

Figure 1

MDPI and ACS Style

Hübl, F.; Cvetojevic, S.; Hochmair, H.; Paulus, G. Analyzing Refugee Migration Patterns Using Geo-tagged Tweets. ISPRS Int. J. Geo-Inf. 2017, 6, 302. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100302

AMA Style

Hübl F, Cvetojevic S, Hochmair H, Paulus G. Analyzing Refugee Migration Patterns Using Geo-tagged Tweets. ISPRS International Journal of Geo-Information. 2017; 6(10):302. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100302

Chicago/Turabian Style

Hübl, Franziska, Sreten Cvetojevic, Hartwig Hochmair, and Gernot Paulus. 2017. "Analyzing Refugee Migration Patterns Using Geo-tagged Tweets" ISPRS International Journal of Geo-Information 6, no. 10: 302. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100302

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop