空间大数据支持下城市安全生产风险评估方法

Urban safety production risk assessment supported by spatial big data

现有城市安全生产风险评估研究大多基于统计调查数据微观地评估单个企业或园区,没有充分考虑安全生产事故空间效应,难以适应于城市尺度安全生产风险评估。本文基于H-V风险评估框架综合分析区域致灾因子危险性、区域目标暴露度和区域救援适应性等多个方面,构建城市安全生产风险评估体系,在多源地理空间数据支撑下量化分析城市安全生产风险指标,揭示城市安全生产风险分布格局,为城市安全生产防控与治理提供决策知识服务。以昆山市为研究区域,实现空间大数据支持下的城市安全生产风险评估。试验结果表明,昆山市安全生产高风险区域主要集中于玉山镇东部和南部,其中玉山镇南部致灾因子危险性较高,玉山镇东部致灾因子危险性与区域目标暴露度均较高。

Most of the existing studies on urban safety production risk assessment are based on statistical survey data to assess a single enterprise or park,and without sufficiently considering the spatial effect of safety production accidents,which is difficult to adapt to the urban scale safety production risk assessment.Based on the H-V risk assessment framework,this paper constructs the urban safety production risk assessment system by comprehensively analyzing regional hazard factors,regional target exposure and regional rescue adaptation.With the support of multi-source geospatial data,the urban safety production risk index is quantified based on spatial analysis to reveal the distribution pattern of urban safety production risk,which could provide decision-making knowledge service for the prevention and control of urban production safety.Taking Kunshan as the study area,the urban safety production risk assessment supported by spatial big data is realized.The experimental results show that the high-risk areas of safety production in Kunshan are mainly concentrated in the east and south of Yushan Town,and the risk of disaster factors is higher in the south of Yushan Town,while the risk of disaster factors and regional target exposure are higher in the east of Yushan Town.