A scalable approach to extracting mobility patterns from social media data

Zhenhua Zhang, Shaowen Wang, Guofeng Cao, Anand Padmanabhan, Kaichao Wu

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

Abstract

Social media represents an emerging source of big data with rich mobility information embedded. While extensive studies in cartography, geographic information science, and visualization have been conducted to extract movement patterns from spatial data, new challenges and opportunities arise for deriving geographic patterns of mobility at scale from massive social media data. Conventional methods (e.g. flow mapping) cannot effectively capture geographic features in the process of addressing visualization concerns such as visual clutter. Furthermore, these methods are not scalable to the volume and velocity of social media data. As a consequence, geographic attributes of mobility (e.g. for representing movement trajectories of social media users) are not adequately captured for better understanding actual mobility patterns (e.g. in disease transmission and road traffic). To address this problem, this paper describes a scalable approach to extracting mobility patterns based on geographic routes from massive social media data. To support interactive visualization of mobility patterns by a large number of online users, this approach is implemented as a workflow of data processing, routing optimization, and multi-scale mapping. The scalability of the approach is demonstrated through both a suite of simulation experiments and a cyberGIS application of social media data.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 22nd International Conference on Geoinformatics, Geoinformatics 2014
EditorsXinyue Ye, Xinyue Ye, Shixiong Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479957149
DOIs
StatePublished - Nov 7 2014
Event22nd International Conference on Geoinformatics, Geoinformatics 2014 - Kaohsiung, Taiwan, Province of China
Duration: Jun 25 2014Jun 27 2014

Publication series

NameProceedings - 2014 22nd International Conference on Geoinformatics, Geoinformatics 2014

Other

Other22nd International Conference on Geoinformatics, Geoinformatics 2014
CountryTaiwan, Province of China
CityKaohsiung
Period6/25/146/27/14

Keywords

  • CyberGIS
  • Flow Mapping
  • Geographic Routing
  • Mobility
  • Social Media Data

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Zhang, Z., Wang, S., Cao, G., Padmanabhan, A., & Wu, K. (2014). A scalable approach to extracting mobility patterns from social media data. In X. Ye, X. Ye, & S. Hu (Eds.), Proceedings - 2014 22nd International Conference on Geoinformatics, Geoinformatics 2014 [6950845] (Proceedings - 2014 22nd International Conference on Geoinformatics, Geoinformatics 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GEOINFORMATICS.2014.6950845