Authors: Zhe Zhang*, Texas A&M University, Hao Hu, University of Illinois at Urbana-Champaign, U.S.A, Dandong Yin, University of Illinois at Urbana-Champaign, Shakil Kashem, University of Illinois at Urbana-Champaign, Ruopu Li, Southern Illinois University, Heng Cai, Louisiana State University, Dylan Perkins, University of Wyoming, Shaowen Wang, University of Illinois at Urbana-Champaign
Topics: Geographic Information Science and Systems, Hazards, Risks, and Disasters, Hazards and Vulnerability
Keywords: Multi-criteria spatial decision support systems, social media data (Twitter), disaster management, big data, and cyberGIS
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded
With the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. This spatial decision support system combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.