, Volume 22, Issue 2, pp 411–433 | Cite as

A Spatio-temporal Scenario Model for Emergency Decision

  • Cheng Liu
  • Jing Qian
  • Danhuai Guo
  • Yi Liu


A structural and quantitative representation of disaster status contributes to efficient emergency decision-making, for this purpose, a representation model for disaster status is developed in this paper, called spatio-temporal scenario model (short for STSM model). Concept of the term ‘scenario’ is discussed at first. Then, based on the concept, STSM model is proposed and introduced in detail. It contains two components: developing scenario connotation and developing spatio-temporal framework. Scenario connotation is to develop representation of disaster status of each object, consisting of object representation and damage representation. Spatio-temporal framework is to develop representation of evolution of disaster status, consisting of representation of spatial relation, temporal relation, natural environment and emergency response. Finally, an example is provided to show the effectiveness of STSM model. Advantages of the developed model lie in four aspects: flexibility in describing dynamic disaster status; universal representation of disaster status contributing to similarity assessment; helping in evaluating emergency severity with the quantitative representation of disaster status. Moreover, it helps decision-makers obtain a more comprehensive representation for disaster evolution in a certain time space.


Scenario representation model Spatio-temporal Framework Emergency Decision-making 



This work is funded by National Key R&D Program of China (No. 2017YFC0803300) and National Natural Science Foundation of China (No.91646101, No.71673158, No.91324022, No.91646201 and No.41371386) and Natural Science Foundation of Beijing Municipality (No.9172023)


  1. 1.
    Kaibin, Z (2014) Emergency Decision-making: theory and practice. Social Sciences Academic PressGoogle Scholar
  2. 2.
    Vitoriano B, Montero J, Ruan D (2013) Decision Aid Models for Disaster Management and Emergencies. Atlantis PressGoogle Scholar
  3. 3.
    Reeder B, Demiris G (2010) Building the PHARAOH Framework Using Scenario-Based Design: A Set of Pandemic Decision-Making Scenarios for Continuity of Operations in a Large Municipal Public Health Agency. J Med Syst 34:735–739CrossRefGoogle Scholar
  4. 4.
    Liu L, Wei Y, and Shen Y (2010) Scenario-based research on unconventional emergency decision-making IEEE International Conference on Emergency Management and Management Sciences. IEEE, 519-22Google Scholar
  5. 5.
    Alvear D, Abreu O, Cuesta A (2013) Decision support system for emergency management: Road tunnels. Tunn Undergr Space Technol 34:13–21CrossRefGoogle Scholar
  6. 6.
    Moehrle S, Raskob W (2015) Structuring and reusing knowledge from historical events for supporting nuclear emergency and remediation management. Eng Appl Artif Intell 46:303–311CrossRefGoogle Scholar
  7. 7.
    Harries C (2003) Correspondence to what? Coherence to what? What is good scenario-based decision making? Technol Forecast Soc Chang 70:797–717CrossRefGoogle Scholar
  8. 8.
    Heugens PAR, Oosterhout JV (2001) To boldly go where no man has gone before: integrating cognitive and physical features in scenario studies. Futures 33:861–872CrossRefGoogle Scholar
  9. 9.
    Comes T, Hiete M, and Wijngaards N (2010) Enhancing Robustness in Multi-criteria Decision-Making: A Scenario-Based Approach. International Conference on Intelligent NETWORKING and Collaborative Systems. IEEE, 484-89Google Scholar
  10. 10.
    Comes, T (2011). Decision Maps for Distributed Scenario-Based Multi Criteria Decision Support. KITGoogle Scholar
  11. 11.
    Chong-guang W, Xin X, Bei-ke Z, Yu-liang N (2013) Domain ontology for scenario-based hazard evaluation. Saf Sci 60:21–34CrossRefGoogle Scholar
  12. 12.
    Carroll JM (2000) Five reasons for scenario-based design. Interact Comput 13:43–60CrossRefGoogle Scholar
  13. 13.
    Conrado, Claudine, and Patrick de Oude (2014) Scenario-Based Reasoning and Probabilistic Models for Decision Support. 17th International Conference on Information FusionGoogle Scholar
  14. 14.
    Fogli D, Guida G (2013) Knowledge-centered design of decision support systems for emergency management. Decis Support Syst 55:336–347CrossRefGoogle Scholar
  15. 15.
    Hui Z, Yi L (2012) Key problems on fundamental science and technology integration in ‘scenario-response’ based national emergency response platform system. Syst Eng Theory Pract 32:947–953Google Scholar
  16. 16.
    Pidd M, Eglese R, Silva FND (1996) Cemps: a prototype spatial decision support system to aid in planning emergency evacuations. Trans GIS 1(4):321–334CrossRefGoogle Scholar
  17. 17.
    Mccarthy N, Neville K, Pope A (2016) The creation of a training model to support decision-making of emergency management practitioners: a design research study. J Decis Syst 25:558–565CrossRefGoogle Scholar
  18. 18.
    Weicheng, F (2013) An introduction to public safety science. Science PressGoogle Scholar
  19. 19.
    Qian J, Liu Y, Liu C, Jiao Y (2015) Study on case analysis and scenario deduction based on multi-dimensional scenario space method. Syst Eng Theory Pract 35:2588–2595Google Scholar
  20. 20.
    Zixue G, Qi M, Zheng Y, Li X (2013) A multi-criteria decision making approach for evaluating efficiency of emergency plan based on intuitionistic fuzzy Information. Inf Technol J 12:3452–3456CrossRefGoogle Scholar
  21. 21.
    Nedas KA, Egenhofer MJ (2008) Spatial-scene similarity queries. Trans GIS 12(6):661–681CrossRefGoogle Scholar
  22. 22.
    A. G. Cohn and J. Renz (2007) Qualitative spatial reasoning. In Handbook of Knowledge Representation. Foundations of Artificial Intelligence, ElsevierGoogle Scholar
  23. 23.
    Chen J, Anthony G, Cohn DL, Shengsheng W, Qiangyuan Y (2015) A survey of qualitative spatial representations. Knowl Eng Rev 30:106–136CrossRefGoogle Scholar
  24. 24.
    Danhuai, G (2008) Propagation and visualization of uncertainty in NL-based spatial analysis. Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental SciencesGoogle Scholar
  25. 25.
    Zhilin L (2005) A Theoretical Discussion on the Scale Issue in Geospatial Data Handling. Geom World 3:1–5Google Scholar
  26. 26.
    Danhuai, G (2008) Study of the key technique of spatial similarity assessment. IRSAGoogle Scholar
  27. 27.
    Danhuai, G (2016) Spatial analysis based on the spatial similarity. Science PressGoogle Scholar
  28. 28.
    Randell, D. A., Cui, Z. and Cohn, A. G (1992) A spatial logic based on regions and connection. Knowledge Representation and Reasoning, 165–176Google Scholar
  29. 29.
    Goyal, R. K., and M. J. Egenhofer (2001) Similarity of cardinal directions. Advances in Spatial and Temporal DatabasesGoogle Scholar
  30. 30.
    Wei S, Peng L, Zhiming W, Ling D, Yongshan L (2005) A New Method for Representation of the Cardinal Direction Relations Using Rectangle Algebra. Comput Eng Appl 41:79–81Google Scholar
  31. 31.
    Renzhong, G (2001) Spatial Analysis (2nd). Higher Education PressGoogle Scholar
  32. 32.
    Qinglin, Z (2014) Analysis of typical depot fire incidents. Tianjin University PressGoogle Scholar
  33. 33.
    Weishan Y (2015) Study of analyzing the influence range of oil tank explosion. Fire Sci Technol 1:22–25Google Scholar
  34. 34.
    Cui, Z., Cohn, A. G. and Randell, D. A (1993) Qualitative and topological relationships in spatial databases. Advances in Spatial Databases, 293–315Google Scholar
  35. 35.
    Gerevini A, Renz J (2002) Combining topological and size information for spatial reasoning. Artif Intell 137:1–42CrossRefGoogle Scholar
  36. 36.
    Thora Tenbrink, Jan Wiener, and Christophe Claremont (2013) Representing space in cognition: Interrelations of behavior, language, and formal models. Oxford university pressGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Institute of Public Safety Research, Department of Engineering PhysicsTsinghua UniversityBeijingChina
  2. 2.Computer Network Information CenterChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations