Geo-tagged social media data as a proxy for urban mobility

Cheng Qian, Philipp Kats, Sergey Malinchik, Mark Hoffman, Brian Kettler, Constantine Kontokosta, Stanislav Sobolevsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We evaluate the utility of geo-tagged Twitter data for inferring a network of human mobility in the New York City through a quantitative and qualitative comparison of the Twitter-based mobility network during business hours versus the ground-truth network based on official statistics. The analysis includes a comparison of the structure of the city inferred through community detection in both networks, comparison of the models of human mobility fitted to both networks, as well as the comparison of the dynamic population distribution across the city presented by the networks. Once the utility of the Twitter data is verified, the availability of an additional temporal component in it can be seen as bringing additional value to numerous urban applications. The data visualization web application is constructed to illustrate one of the examples of such applications.

Original languageEnglish (US)
Title of host publicationAdvances in Cross-Cultural Decision Making - Proceedings of the AHFE 2017 International Conference on Cross-Cultural Decision Making, 2017
EditorsMark Hoffman
PublisherSpringer Verlag
Pages29-40
Number of pages12
ISBN (Print)9783319607467
DOIs
StatePublished - 2018
EventAHFE 2017 International Conference on Cross-Cultural Decision Making, 2017 - Los Angeles, United States
Duration: Jul 17 2017Jul 21 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume610
ISSN (Print)2194-5357

Other

OtherAHFE 2017 International Conference on Cross-Cultural Decision Making, 2017
Country/TerritoryUnited States
CityLos Angeles
Period7/17/177/21/17

Keywords

  • Community detection
  • Gravity model
  • Human mobility
  • LEHD
  • Social media
  • Twitter
  • Urban science

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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