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ISPRS Int. J. Geo-Inf. 2016, 5(10), 193; doi:10.3390/ijgi5100193

A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

1
State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
Department of Geo-informatics, Central South University, Changsha 410083, China
4
Department of Computer Science, Virginia Tech, Falls Church, VA 22043, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Marguerite Madden and Wolfgang Kainz
Received: 28 March 2016 / Revised: 1 October 2016 / Accepted: 9 October 2016 / Published: 18 October 2016

Abstract

In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data. View Full-Text
Keywords: Evolving spatio-temporal patterns; target domains; spatio-temporal Twitter events; spatial clustering; spatio-temporal clustering Evolving spatio-temporal patterns; target domains; spatio-temporal Twitter events; spatial clustering; spatio-temporal clustering
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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).

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MDPI and ACS Style

Shi, Y.; Deng, M.; Yang, X.; Liu, Q.; Zhao, L.; Lu, C.-T. A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter. ISPRS Int. J. Geo-Inf. 2016, 5, 193.

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