Next Article in Journal
Forest Vertical Parameter Estimation Using PolInSAR Imagery Based on Radiometric Correction
Previous Article in Journal
Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria
Article Menu

Export Article

Correction published on 1 December 2016, see ISPRS Int. J. Geo-Inf. 2016, 5(12), 226.

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2016, 5(10), 187; doi:10.3390/ijgi5100187

Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach

1,2
,
1,2
,
2,3
and
1,2,4,*
1
Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2
CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3
Institute of Geographic Information Science, School of Earth Sciences, Zhejiang University, Hangzhou 310028, China
4
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Marguerite Madden and Wolfgang Kainz
Received: 15 August 2016 / Revised: 19 September 2016 / Accepted: 28 September 2016 / Published: 10 October 2016
View Full-Text   |   Download PDF [9063 KB, uploaded 1 December 2016]   |  

Abstract

Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements. View Full-Text
Keywords: Geo-located tweets; mobility patterns; multi-scale spatiotemporal analysis; scalable visual-analytics framework Geo-located tweets; mobility patterns; multi-scale spatiotemporal analysis; scalable visual-analytics framework
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yin, J.; Gao, Y.; Du, Z.; Wang, S. Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach. ISPRS Int. J. Geo-Inf. 2016, 5, 187.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top