Journal of Psychology & Psychotherapy
Open Access

Big data driven indicator choosing for disaster response capacity shortage assessment of urban critical infrastructure

17^th World Summit on Positive Psychology, Psychotherapy & Cognitive Behavioral Sciences

May 01-03, 2017 Toronto, Canada

Zhao-ge Liu, Liu Tian-chang and Li Xiang-yang

Harbin Institute of Technology, China

Scientific Tracks Abstracts: J Psychol Psychother

Abstract :

Critical Infrastructure (CI) is a kind of complex and huge network system and its operating failure will cause serious impact on national interest. Indicators of CI disaster response capacity shortage assessment need to be systematic, objective and consistent and analyzing at the aspect of capacity shortage help us to grasp the weak spots and improve response capacity. This paper proposes a big data driven indicator choosing method for CI response capacity shortage assessment. With the big data driven method, we can obtain and grasp historical experience guiding the scenario designing and global indicator choosing of CI response capacity shortage assessment. Besides, we can identify potential basic indicators and guides the basic indicator choosing and improving by data analysis.

Biography :

Zhao-ge Liu is currently a Master’s student in Harbin Institute of Technology in China. He has his expertise in Management Science. He focuses on emergency management, food safety and customer relationship management. He has participated in two projects of Natural Science Foundation of China.

Email: zhaogeliu@hit.edu.cn