Risk is widely conceptualized as a factor of hazards, exposure and vulnerability. One critical component of the risk equation is geography. The physical location of people and assets very much defines the likelihood and impact of a specific hazard which make geographic information systems (GIS) a critical tool of any risk assessment strategy.
However, a limitation of a static overlay of hazard and exposure GIS data is that it simplifies the dynamic nature of disasters and the complex networks of human settlements. Indeed, most disasters occur at a specific time and space. Riverine floods will affect river banks until water is drained, and wildfires will affect forests and communities until they are controlled. These physical geographies can be identified using various data collection methods. However, the complexity of human settlement networks also creates indirect consequences of disasters. An earthquake in a city that destroys a tertiary health facility will not only affect residents but also the entire health system that depends on it for complex procedures. Cities and smaller human settlements can be analyzed as complex systems organized around critical resources and basic infrastructure that need to be better understood for adequate risk management. Understanding the catchment areas of geographies both directly and indirectly affected by disasters is critical to improve disaster response to all affected communities.
To address this challenge, this research presents how using network analysis and GIS can provide a dynamic understanding of risk for evidence-based disaster risk management. First, the paper explains some basic concepts of systems thinking and network analysis applied in urban environments as a background to the research. The Methodology and Limitations section covers the main primary and secondary data used in the analysis, which is followed by a case study of how network analysis was used to evaluate the cascading impacts of disasters in a densely built environment affected by conflict.
This paper is a contribution to the 2019 edition of the Global Assessment Report on Disaster Risk Reduction (GAR 2019).
To cite this paper:
Wetterwald, J. and Smirnov, Y. Using network analysis to evaluate the cascading impacts of crises on service and market systems. Contributing Paper to GAR 2019