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

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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.

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Notes

  1. http://www.skout.com/

  2. A check-in of a user is a record containing the time and venue of her visit.

  3. https://intransit.blogs.nytimes.com/2015/03/03/an-app-that-connects-travelers-with-locals/

  4. https://www.transtats.bts.gov/Tables.asp?DB_ID=120&DB_Name=Airline%20On-Time%20Performance%20Data&DB_Short_Name=On-Time

  5. https://d3js.org

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Acknowledgements

This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), Academy of Finland (No. 268096).

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Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, China

    Rong Xie, Yang Chen, Shihan Lin, Tianyong Zhang & Xin Wang

  2. Engineering Research Center of Cyber Security Auditing and Monitoring, Ministry of Education, Shanghai, China

    Rong Xie, Yang Chen, Shihan Lin, Tianyong Zhang & Xin Wang

  3. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

    Rong Xie, Yang Chen, Shihan Lin, Tianyong Zhang & Xin Wang

  4. Department of Communications and Networking, Aalto University, Espoo, Finland

    Yu Xiao

Authors
  1. Rong Xie
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  2. Yang Chen
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  3. Shihan Lin
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  4. Tianyong Zhang
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  5. Yu Xiao
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  6. Xin Wang
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Corresponding author

Correspondence to Yang Chen.

Additional information

This article belongs to the Topical Collection: Special Issue on Web and Big Data

Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao

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Xie, R., Chen, Y., Lin, S. et al. Understanding Skout users’ mobility patterns on a global scale: a data-driven study. World Wide Web 22, 2655–2673 (2019). https://doi.org/10.1007/s11280-018-0551-8

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  • DOI: https://doi.org/10.1007/s11280-018-0551-8

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