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A Comparative Study of Data-Driven Models for Travel Destination Characterization.

Frontiers in big data·2022

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Identifying Travel Regions Using Location-Based Social Network Check-in Data.

Avradip Sen¹, Linus W Dietz¹

¹Department of Informatics, Technical University of Munich, Munich, Germany.

Frontiers in Big Data

|March 11, 2021

Summary

This study identifies travel regions using Twitter user mobility data. It reveals how people travel across borders, uncovering both domestic and transnational travel patterns.

Keywords:

data-mining human mobility modeling region detection spatial clustering visualization

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Area of Science:

Computational Social Science
Geospatial Analysis
Network Science

Background:

Traditional travel region definitions often rely on political boundaries, which may not reflect actual human movement patterns.
The Schengen Area exemplifies how administrative borders can be transcended in real-world travel.

Purpose of the Study:

To identify and delineate travel regions irrespective of political or administrative boundaries.
To leverage social media mobility data for understanding global travel patterns.
To uncover both domestic and transnational travel regions.

Main Methods:

Collection of geotagged tweets to create user trajectories.
Analysis of aggregated trip data to construct a mobility graph.
Application of community detection algorithms to identify coherent travel regions.

Main Results:

Discovery of thousands of individual trips from Twitter user data.
Identification of coherent geographical regions based on aggregated mobility patterns.
Insights into the nature of international and domestic travel flows.

Conclusions:

Mobility analysis of social media data can effectively define travel regions beyond political borders.
The methodology provides a novel approach to understanding human mobility and spatial interactions.
Discovered regions offer valuable insights for tourism, urban planning, and policy-making.