Next Article in Journal
Estimation of 3D Indoor Models with Constraint Propagation and Stochastic Reasoning in the Absence of Indoor Measurements
Previous Article in Journal
Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation
Open AccessArticle

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

by Xiaolu Zhou 1,* and Chen Xu 2
1
Department of Geology and Geography, Georgia Southern University, P.O. Box 8149, Statesboro, GA 30460, USA
2
Department of Geography, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 88; https://doi.org/10.3390/ijgi6030088
Received: 28 November 2016 / Revised: 24 February 2017 / Accepted: 12 March 2017 / Published: 18 March 2017
Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis’ visit to the US) in the New York City—Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information. View Full-Text
Keywords: social media data; geographic information systems; space-time event; spatial analysis social media data; geographic information systems; space-time event; spatial analysis
Show Figures

Figure 1

MDPI and ACS Style

Zhou, X.; Xu, C. Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data. ISPRS Int. J. Geo-Inf. 2017, 6, 88.

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop