顾及时空因素的国家恐怖袭击事件Cox回归分析

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

Cox回归模型能够对事件发生时间及其影响因素进行有效关联,是面向事件数据生存分析的重要方法。基础的Cox回归模型仅能对单次独特事件进行研究,通过事件分组、事件起始时间设置调整后的模型能够对重复事件进行解析,尽管现有的Cox模型能够处理时变解释变量,但仍不能考虑事件时空因素的综合影响。国家恐怖袭击事件的不同生存状态是重复出现的,因此,基于全球恐怖主义数据库,以国家恐怖袭击高风险状态再次发生的概率为研究对象,通过引入时间交互、空间滞后解释变量,提出一种顾及多种时空因素的重复事件Cox回归分析方法。结果表明,1995—2016年,相比于经济社会与地理类因素,政治、军事类因素对国家恐怖袭击高风险状态再次发生概率的影响程度更为明显。引入时间交互、空间滞后解释变量的Cox模型回归效果有所提高,空间滞后解释变量对国家高风险状态的发生概率作用显著,时空因素的引入具有重要意义。

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.