Volume XLI-B2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 579-581, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-579-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 579-581, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-579-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Jun 2016

08 Jun 2016

Using social media for disaster emergency management

Y. D. Wang1, T. Wang1, X. Y. Ye2, J. Q. Zhu1, and J. Lee3,2 Y. D. Wang et al.
  • 1State key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • 2Department of Geography, Kent State University, Kent, USA
  • 3College of Environment and Planning, Henan University, Kaifeng, Henan, China

Keywords: social media, Sina-Weibo, emergency information, timely, Beijing rainstorm

Abstract. Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011).

In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management.