Cox Regression Analysis of National Terrorist Attacks Considering Spatial and Temporal Factors

Cox regression model is an important method for event data survival analysis, which can effectively identify the correlations between event occurrence time and its influencing factors. The basic Cox regression model can only study on a single unique event, and the model can be adjusted expanded through event grouping and event start time setting, which can be used to deal with repeated events. Although the existing Cox model can also be easily modified to deal with time-varying explanatory variables, it is still lack of considering the comprehensive influence from temporal and spatial factors. In light of the repeated national survival status, this paper takes the occurrence probability of national terrorist attacks high risk status as the research object, and establishes the repeated event Cox model considering multiple spatial and temporal factors based on global terrorism database. Additionally, this paper has proposed a new terrorist attack risk level as well as spatial lag calculating method based on composited weight factor and similarity to ideal solution, which provide research foundation for the following expanded Cox model establishment. The results show that political and military factors have a more important influence on the occurrence probability of high-risk state from 1995 to 2016, compared with economic social and geographical factors. The regression effect of the repeated event Cox model with introducing time interaction and spatial lag explanatory variables have been improved, and time interaction as well as spatial lag explanatory variables have significant effects on the occurrence probability of high-risk state.