Exploring urban risk reduction strategy based on spatial statistics and scenario planning

Abstract

Cities with dense population are vulnerable to disasters as they may cause losses of human life and property. Mitigation is most effective for reducing the risks of communities as well as creating more resilient communities in cities. However, exploring the spatial prioritization for resources investment and evaluating the relative efficiency of different alternative risk reduction strategies are still challenges. In this study, we proposed an innovative methodology framework for exploring risk reduction strategies in large-scale urban areas. GIS spatial statistics technique is introduced to investigate global regional risk pattern as well as identify local risk hot spots in spatial scale, and scenario planning paradigm is used to explore the dynamically changing regional risk pattern and risk hot spots in temporal scale due to risk reduction. Moreover, scenario sets were developed to evaluate and analyze the effectiveness and cost of different alternative risk reduction strategies for trade-off. We then applied the methodology to a typical urban district of north China to demonstrate its utility. The results successfully obtained reveal the necessity of concerning dynamically changing nature of regional risk spatial pattern during the risk reduction process. The findings show that the methodology framework proposed in this study has potential to provide decision support for guiding future urban risk reduction and hazard mitigation planning. This work offers new perspectives and valuable demonstration on promoting urban risk management with the integration of emerging GIS spatial statistics and scenario planning technique.