Tropical Geography ›› 2020, Vol. 40 ›› Issue (2): 184-193.doi: 10.13284/j.cnki.rddl.003239

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A Research Framework for the Application of Volunteered Geographic Information in Post-Disaster Recovery Monitoring

Yan Yingwei1,2, Ma Dawei3(), Fan Hongchao4   

  1. 1.Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System// Guangdong Open Laboratory of Geospatial Information Technology and Application// Guangzhou Institute of Geography, Guangzhou 510070, China
    2.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
    3.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    4.Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway
  • Received:2020-03-12 Revised:2020-04-07 Online:2020-03-10 Published:2020-05-15
  • Contact: Ma Dawei


In recent years, many studies on the application of Volunteered Geographic Information (VGI) to natural disaster management have been reported. To date, it has been shown that VGI can provide big geospatial data, incorporating rich content and extensive spatiotemporal coverage, in a real-time and cost-effective manner. Thus, it can facilitate disaster management by filling information voids known to occur in traditional geospatial datasets. However, existing studies have mostly focused on disaster prevention, preparedness, and response phases, and few studies have focused on post-disaster recovery. The aim of the proposed work is to help bridge this research gap by investigating how VGI data can facilitate post-disaster recovery monitoring, both in general, and for specific aspects such as tourism, business, industry, and the daily routines of residents. In the work reported here, we first review the development of VGI research in the decade since 2007, and then propose a research framework for post-disaster recovery monitoring, based on VGI data. The research framework involves three key components—data acquisition, data quality control, and data mining. Data acquisition is generally referred to as VGI collection (for example, OpenStreetMap, Twitter, and Flickr data), while authoritative data (such as remote sensing data, official statistics, and field survey data) can be collected as ancillary information. Data quality control is based on a fuzzy expert system, which considers Linus’ law, metadata, data lineage and provenance, geographic contexts (Tobler’s first law of geography), user credibility, spatiotemporal data density, and user activeness, and leverages artificial neural networks (deep learning) for optimizing the fuzzy rule sets of the expert system. Data mining is based on the transformative paradigm, using a combination of quantitative and qualitative approaches, involving text mining, spatial statistics, and machine learning. Using this combination, the status of infrastructure (roads, electricity, water, transport, housing, buildings, and telecoms), the economy (external sources of economy, internal sources of economy, and services), and safety (reputation, secondary disasters, health, security, and stability), which are the three main measures of post-disaster recovery, are investigated. This work thus paves a way for future studies related to this topic, identifying both research and practical implications. We also identify limitations in using VGI for post-disaster recovery management. The first of these is that a VGI source may rapidly become obsolete due to the fast development of cyberspace, making it imperative for researchers to keep the technique up-to-date, adapting it to the diverse and emerging VGI sources. The second limitation is that certain VGI platforms do not provide all the data available in their databases, and have imposed constraints on their Application Programing Interfaces (APIs). The third limitation is related to privacy issues pertinent to VGI data use. We suggest that the future work should also focus on seamlessly integrating VGI with traditional post-disaster recovery monitoring approaches, such as remote sensing, field surveys, and resident interviews.

Key words: Volunteered Geographic Information, big data, disaster management, post-disaster recovery monitoring, research framework

CLC Number: 

  • P208