Abstract
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.
Highlights
The destinations visited within a trip may overarch existing administrative divisions of provinces, federal states, and countries
When developing a travel region recommender system for composite trips this is a challenge, because one needs a region model to choose the recommendations from Dietz (2018)
To come up with such a model, we propose to observe traveler mobility behavior, aggregate it using spatial clustering methods, thereby re-drawing the boundaries of the world’s travel regions using a data-driven approach
Summary
The destinations visited within a trip may overarch existing administrative divisions of provinces, federal states, and countries. From the series of tweets, we determine the home location of the user and extract the trips (Dietz et al, 2018). These trips are aggregated into a weighted graph of tourist flows with nodes being cities and edges being the number of trips from one city to another. This graph is fed into a community detection algorithm (Bohlin et al, 2014), whose results constitute the world’s travel regions irrespective of established political boundaries
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