Journal of Geo-information Science ›› 2018, Vol. 20 ›› Issue (6): 807-816.doi: 10.12082/dqxxkx.2018.180022.

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Assessing the Effectiveness of Social Media Data in Mapping the Distribution of Typhoon Disasters

LIANG Chunyang1(), LIN Guangfa1,2,3,*(), ZHANG Mingfeng1,2,3, WANG Weiyang1, ZHANG Wenfu1, LIN Jinhuang1, DENG Chao1   

  1. 1. Institute of Geography, Fujian Normal University, Fuzhou 350007, China
    2. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disaster, Fuzhou 350007, China
    3. Research Center for National Geographical Condition Monitoring and Emergency Support in the Economic Zone on the West Side of the Taiwan Strait, Fuzhou 350007, China
  • Received:2018-01-02 Revised:2018-04-08 Online:2018-06-20 Published:2018-06-20
  • Contact: LIN Guangfa;
  • Supported by:
    National Key Research and Development Program of China, No.2016YFC0505905;Non-profit Research Projects of Fujian Province, No.2015R1034-1;Development Foundation of Surveying, Mapping and Geoinformatics of Fujian Province, No.2017JX03


When a disaster occurs, a large number of images and texts with geographic information quickly flood the social network, which provides a new data source for timely awareness of disaster situations. However, due to the regional variation in the number of social media users and characteristics of information diffusion in cyberspace, new problems have risen in the mode analysis of spatial point processes represented by the check-in data. Examples are the correlation between check-in point density and disaster location density, spatial relation between check-in points or spatial heterogeneity of point pattern and associated influences. In this study, we took Typhoon No.14 in 2016 as an example and collected Sina Weibo data between September 14 and September 17, 2016 using keywords “Typhoon” and “Meranti”. We classified the Weibo texts using Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) algorithms and constructed a disaster database containing relevant check-in information. In addition, considering the spatial heterogeneity of Weibo users, we proposed a weighted model based on user activity at the check-in points. Using the global autocorrelation statistics Moran′s I as an indicator, we compared the check-in data before and after adding weights and discovered obvious spatial autocorrelation of the check-in data in real geographical locations. We tested our model on Weibo data with keyword “rain” and “power failure”. The results show that a series of maps generated by our model is able to reflect the typhoon disaster spatio-temporal process trends.

Key words: social media, typhoon disaster, spatial analysis, data mining, spatial autocorrelation