A GIS-Based Model for Integrating Risk Estimations of Residential Building Damage and Shelter Capacity in the Case of Earthquakes

Dinh-Thanh Nguyen, Zhenjiang Shen, Kyohei Honda, Kenichi Sugihara, Tatsuya Nishino, Minh-Hoang Truong

This research aims at developing a geographic information system (GIS)–based model for integrating risk estimation for residential building damage with risk estimation for shelter capacity by number of evacuees. The former aims at predicting the number of buildings damaged by an earthquake, while the latter refers to whether each evacuation center (henceforth: shelter) has sufficient accommodation for evacuees through a simulated evacuation following an earthquake. The evacuee number, which is predicted by reference to damaged buildings and building population, is used as input data for estimating risk in relation to shelter capacity. That is, the output of risk estimation for residential building damage is an input for shelter capacity risk estimation. By making this integration, the proposed model can supply an integrated information flow for prediction, preparedness, and response in relation to potential earthquake disasters by offering, inter alia, predictions of seismic intensity, forecasts of building damage and evacuees, models of how residents move to evacuation shelters following earthquakes (evacuation simulations), and data on shelters with insufficient capacity (shelter capacity estimation). Moreover, this research also proposes a method for allocating residents to each building based on building floor area and the total population of each chome (or city district, a common administrative unit in Japan). The number of residents in each building depends on the building floor area and varies within each chome. This method of estimation differs from the average building population used in the literature, which assumes that the number of residents per building is constant throughout a chome. Furthermore, identifying the number of residents in each house or building makes it easier to predict the number of evacuees in each evacuation shelter through an evacuation simulation routing residents from their homes to the nearest shelters. The number of evacuees per shelter is then used to estimate risk in relation to shelter capacity by comparing it with the planned capacity of that shelter.

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