A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

ABSTRACT

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. It 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.

KEYWORDS:

• Multi-criteria spatial decision support systems
• social media data (Twitter)
• disaster management
• big data
• and cyberGIS

Acknowledgement

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We would like to acknowledge Mr Johnathan Rush and other members of the CyberGIS Center for Advanced Digital and Spatial Studies at the University of Illinois at Urbana-Champaign for their kind support and helpful inputs.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Zhe Zhang http://orcid.org/0000-0001-7108-182X

Heng Cai http://orcid.org/0000-0001-6068-5150

Additional information

Funding

This material is based in part upon work supported by the U.S. National Science Foundation under [grant numbers: 1047916, 1429699, 1443080, 1551492, and 1664119].