TY - GEN
T1 - Modeling threats of mass incidents using scenario-based Bayesian network reasoning
AU - Huang, Lida
AU - Cai, Guoray
AU - Yuan, Hongyong
AU - Chen, Jianguo
AU - Wang, Yan
AU - Sun, Feng
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 71790613 and Grant No. 71373139), the Ministry of Science and Technology of the People’s Republic of China under Grant No. 2015BAK10B02, and the scholarship from China Scholarship Council (CSC) (NO. 201706210127). The work of the second author is also partially supported by a grant from the National Science Foundation (US) under award IIS-1211059, and a grant from the Chinese Natural Science Foundation under award 71373108.
Publisher Copyright:
© Information Systems for Crisis Response and Management ISCRAM. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library of mass incidents in China, a BN capturing the interaction of twelve key factors in mass incidents is developed, where the network structure is determined by data and expert knowledge. The model is compared with two base-line BN models (use only expert knowledge or data) and a logistic regression model, proving to be the most robust. Using sensitivity analysis, we further identify a more critical subset of those threat-predicting factors. Finally, we present a case study to demonstrate how to apply the proposed framework to assessing the threat of ongoing mass incidents.
AB - Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library of mass incidents in China, a BN capturing the interaction of twelve key factors in mass incidents is developed, where the network structure is determined by data and expert knowledge. The model is compared with two base-line BN models (use only expert knowledge or data) and a logistic regression model, proving to be the most robust. Using sensitivity analysis, we further identify a more critical subset of those threat-predicting factors. Finally, we present a case study to demonstrate how to apply the proposed framework to assessing the threat of ongoing mass incidents.
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M3 - Conference contribution
AN - SCOPUS:85060736266
T3 - Proceedings of the International ISCRAM Conference
SP - 121
EP - 134
BT - Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2018
A2 - Tomaszewski, Brian
A2 - Boersma, Kees
PB - Information Systems for Crisis Response and Management, ISCRAM
T2 - 15th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2018
Y2 - 20 May 2018 through 23 May 2018
ER -