The role of GI-supported methods in crisis management

Figures & data

Figure 1.  (a) Geographically weighted regression model about incidents and social attributes in Helsinki (modified from Špatenková 2009). (b) SOM analysis on incidents (Špatenková 2009).

Figure 2.  (a) Impact model of a blast wave (Ahola et al. 2007). (b) Spatio-temporal population model together with a dispersion model of a gas leak (Molarius et al. 2009, reprinted with permission from WIT press).

Figure 3.  Vulnerability analysis in the Helsinki area. Example of edges with high betweenness value matching with the main roads (Demsar et al. 2008, reprinted with permission from John Wiley & Sons Inc.).

Table 1. Different phases of crisis with the classification of methods.

	Risk assessment	Impact modeling	Vulnerability analysis	Decision support	Presentation
Mitigation
Preparedness
Response
Recovery

Table 2. The focus of the SAR exercise was on decision support and situation awareness.

SAR	Risk assessment	Impact modeling	Vulnerability analysis	Decision support	Presentation
Mitigation
Preparedness				X	X
Response				X	X
Recovery

Table 3. The MNE5 experiment focused on comprehensive use of different means.

MNE5	Risk assessment	Impact modeling	Vulnerability analysis	Decision support	Presentation
Mitigation	X	X	X
Preparedness	X	X	X	X	X
Response				X	X
Recovery

Table 4. The focus of the EK08 exercise was on minimizing the system vulnerability.

EK08	Risk assessment	Impact modeling	Vulnerability analysis	Decision support	Presentation
Mitigation
Preparedness	X	X	X	X	X
Response				X	X
Recovery

Figure 4.  Different approaches in different phases (adapted from Cova 1999).

Špatenková , O. , 2009 . Discovering spatio-temporal relationships: a case study of risk modelling of domestic fires . Thesis (PhD), Helsinki University of Technology .

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Demsar , U. , Špatenková , O. and Virrantaus , K. 2008 . Identifying critical locations in a spatial network with graph theory . Transactions in GIS , 12 ( 1 ) : 61 – 82 .

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