Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA)
                Authored by Erick Mas, Daniel Felsenstein, Luis Moya, A YairGrinberger, Rubel Das, Shunichi Koshimura
                
                    Date Published: 2018
                
                
                    DOI: 10.20965/jdr.2018.p1257
                
                
                    Sponsors:
                    
                        JST
                        
                
                
                    Platforms:
                    
                        Python
                        
                        MATLAB
                        
                
                
                    Model Documentation:
                    
                        Other Narrative
                        
                        Flow charts
                        
                
                
                    Model Code URLs:
                    
                        Model code not found
                    
                
                Abstract
                The DIM2SEA research project aims to increase urban resilience to
large-scale disasters. We are engaged in developing a prototype Dynamic
Integrated Model for Disaster Management and Socioeconomic Analysis
(DIM2SEA) that will give disaster officials, stakeholders, urban
engineers and planners an analytic tool for mitigating some of the worst
excesses of catastrophic events. This is achieved by harnessing
stateof-the-art developments in damage assessment, spatial simulation
modeling, and Geographic Information System (GIS). At the heart of
DIM2SEA is an agent-based model combined with post-disaster damage
assessment and socioeconomic impact models. The large amounts of
simulated spatial and temporal data generated by the agent-based models
are fused with the socioeconomic profiles of the target population to
generate a multidimensional database of inherently ``synthetic{''} big
data. Progress in the following areas is reported here: (1) Synthetic
population generation from census tract data into agent profiling and
spatial allocation, (2) developing scenarios of building damage due to
earthquakes and tsunamis, (3) building debris scattering estimation and
road network disruption, (4) logistics regarding post-disaster relief
distribution, (5) the labor market in post-disaster urban dynamics, and
(6) household insurance behavior as a reflection of urban resilience.
                
Tags
                
                    urban simulation
                
                    resilience
                
                    Tsunami
                
                    Scenarios
                
                    Earthquake
                
                    Framework
                
                    Fragility functions
                
                    Damage assessment
                
                    Socioeconomic impact
                
                    Disaster
management
                
                    Disaster simulation
                
                    Indicator