10.3389/fdata.2019.00012.s001 Avradip Sen Avradip Sen Linus W. Dietz Linus W. Dietz fdata-02-00012_Identifying Travel Regions Using Location-Based Social Network Check-in Data.pdf Frontiers 2020 data-mining human mobility modeling spatial clustering region detection visualization 2020-03-06 06:36:39 Dataset https://frontiersin.figshare.com/articles/dataset/fdata-02-00012_Identifying_Travel_Regions_Using_Location-Based_Social_Network_Check-in_Data_pdf/11947932 <p>Travel regions are not necessarily defined by political or administrative boundaries. For example, in the Schengen region of Europe, tourists can travel freely across borders irrespective of national borders. Identifying transboundary travel regions is an interesting problem which we aim to solve using mobility analysis of Twitter users. Our proposed solution comprises collecting geotagged tweets, combining them into trajectories and, thus, mining thousands of trips undertaken by twitter users. After aggregating these trips into a mobility graph, we apply a community detection algorithm to find coherent regions throughout the world. The discovered regions provide insights into international travel and can reveal both domestic and transnational travel regions.</p>