Understanding Skout users’ mobility patterns on a global scale: a data-driven study

Rong Xie, Yang Chen*, Shihan Lin, Tianyong Zhang, Yu Xiao, Xin Wang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

Location-based social apps, such as Skout, have been widely used by millions of users for sharing their location information. In this work, we collected all the location information published by over 1.2 million Skout users during December 2012 and June 2016. Based on the collected information, we model the inter-city mobility of Skout users with a global city network, and analyze the evolution of the network based on its structural characteristics. Moreover, we look into Skout users’ mobility patterns by discovering the most popular inter-city routes, destinations, and tightly connected city groups, and analyze the impact on the mobility patterns from geographical distances, languages and cultures. Finally, we leverage machine learning techniques to build a model for identifying the most influential cities in the world according to the Skout data. The results are able to assist individuals, governors and business leaders in making better decisions regarding traveling, immigrating, measuring city improvements and cooperation with cities.
Original languageEnglish
Pages (from-to)2655–2673
Number of pages19
JournalWORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Volume22
Issue number6
Early online date13 Apr 2018
DOIs
Publication statusPublished - Nov 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Global city network
  • Human mobility
  • PageRank
  • Skout

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