Spatial Comparison Between Densely Built-Up Districts From the Viewpoint of Vulnerability to Road Blockades With Respect to Evacuation Behavior

Abstract

In a densely built-up area, it is conceivable that road blockades will occur widely because of building collapse caused by earthquakes. The road blockades on evacuation routes have a bad influence on earthquake evacuation behavior of residents. It is required for urban management to compare vulnerability to the road blockades between densely built-up districts. The purpose of this study is to discuss a methodology of comparison of the vulnerability based on road blockade risk between the districts. Behavior of the building collapse generally depends on a location of an earthquake's epicenter, intensity of an earthquake motion, and a type of tremor. Various kinds of simulation methods for reproduction of earthquakes have been developed, and estimation methods for damages have depended on the result of the simulation. In the comparison of the districts, an independent method however is also needed because of preventing the damages caused by an unexpected earthquake. We used building properties and Japan Engineering Geomorphologic Classification Map so that we calculated building collapse risk as a relative value. The risk was then applied to GIS analysis to determine risk of road blockade. We applied the road blockade risk as probability to the Monte Carlo simulation for making road blockade patterns. To transform the number of patterns to an index for the district comparison, we determined evacuation sites as key points, and we executed the network voronoi division along the road networks using every result of the simulation. The number of patterns based on the results of the network voronoi division was newly defined as a confusion index in a road-by-road basis: the index meant variations in a ranking of the shortest distance from the evacuation sites. We also calculated isolated roads from the evacuation sites using the results of the network voronoi division while the network spatial analysis was applied to the road blockade patterns from the view point of multidirectional evacuation. We discussed these analysis results using a land use map, historical properties of development, and other data of geographic information.

Integration of earth observation and census data for mapping a multi-temporal flood vulnerability index: a case study on Northeast Italy

“…For instance, detecting buildings material allows to classify their level of vulnerability (Geiß et al 2016;Yuan and Wang 2004) in case of a shock affecting their structure, or detecting the elevation of the buildings compared to the street level allows to understand their vulnerability to floods (Müller 2013). The analysis of road networks and road characteristics allows for VA in relation to flows of people, both in a frame of risk reduction and impacts assessment (Hu et al 2017;Kumagai 2012). They found also several studies focusing on the socio-economic components of vulnerability, where for instance the income of population is inferred by analysing the slope where buildings are located or by the percentage of green in the urban environment (Ebert et al 2009).…”

Post-disaster recovery assessment using remote sensing image analysis and agent-based modeling

“…However, a non-functioning road does not demonstrate the successful recovery processes. In a different study, Kumagai (2012) estimate width of roads as a proxy to assess the vulnerability of the roads to blockades. The results show that in a dense urban area, in which highrise buildings exist and their blow-out debris or in case of collapsing during a disaster event can block the road, width of roads can be used as a reliable proxy to assess the vulnerability of the transport network.…”