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Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm

1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
4
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454001, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 136; https://doi.org/10.3390/ijgi9020136
Received: 31 December 2019 / Revised: 11 February 2020 / Accepted: 20 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper. View Full-Text
Keywords: social media; traffic impact area detection; spatiotemporal influence assessment; disaster reduction social media; traffic impact area detection; spatiotemporal influence assessment; disaster reduction
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MDPI and ACS Style

Yang, T.; Xie, J.; Li, G.; Mou, N.; Chen, C.; Zhao, J.; Liu, Z.; Lin, Z. Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm. ISPRS Int. J. Geo-Inf. 2020, 9, 136. https://doi.org/10.3390/ijgi9020136

AMA Style

Yang T, Xie J, Li G, Mou N, Chen C, Zhao J, Liu Z, Lin Z. Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm. ISPRS International Journal of Geo-Information. 2020; 9(2):136. https://doi.org/10.3390/ijgi9020136

Chicago/Turabian Style

Yang, Tengfei; Xie, Jibo; Li, Guoqing; Mou, Naixia; Chen, Cuiju; Zhao, Jing; Liu, Zhan; Lin, Zhenyu. 2020. "Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm" ISPRS Int. J. Geo-Inf. 9, no. 2: 136. https://doi.org/10.3390/ijgi9020136

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