GIS-based Multidimensional Approach for Modeling Infrastructure Interdependency
Rifaat Abdalla, Harris Ali, and Vincent Tao GeoICT Lab, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 Canada. Email [Abdalla.jaoj Oyorku.ca Faculty of Environmental Studies, York University, 4700 Keele Street, Toronto, ON, M3J IP3, Canada. Email:
[email protected]
Abstract The information technology has been challenged to be a facility to improve the efficiency and effectiveness of managing the four phases of natural disasters (Preparedness, Mitigation, Response and Recovery phases). Addressing interrelationships between different critical infrastructure sectors during disasters is a complex process. This paper will present multidimensional approach that addresses the issue of Location-Based Infrastructure Interdependency (LBII). Key Words: Disaster Management, Emergency Management, Infrastructure, Interdependency, Modeling, GIS
Introduction Disasters are dynamic processes (Alexander, 1993) and by their very nature, are spatially oriented (Waugh, 1995). According to (MontoyaMorales, 2002) most current tools that are used for disaster management focus on the temporal component of the four phases of disaster management, leaving an obvious gap in dealing with the spatial element. The emphasis on the spatial dimension makes GIS technologies ideal for simulating the complex spatial relationships among critical infrastructures (i.e., their interdependencies) while still being able integrate other modeling tools. (Nash et al., 2005) showed that temporal GIS can effectively combine both the temporal and the spatial dimensions. Several studies including (Briggs, 2005; Dietzel et al., 2005; Giardino et aI., 2004; Gupta and
296
Rifaat Abdalla, Harris Ali, and Vincent Tao
Singh, 2005) (Abdalla et al., 2006); (Laben, 2002) outlined the importance of addressing the spatio-temporal affects related to disaster management. These studies have tried to establish an efficient and advanced information system that can accommodate multiple events with the aid of GIS. Infrastructure interdependency is a new multidisciplinary filed of research. Despite the clear definition of infrastructure sectors in Canada, there is no consensus about precise definition for the set of activities and operations that shape this field. Decision-making and support tools aided by case studies and scenario development simulations are key research areas in this new field (Trudeau, 2004) The estimation of risk for particular infrastructure sector can not be achieved without complete conceptualization of vulnerabilities and hazards surrounding it. Technology, in particular GIS can playa very positive role in this regard. GIS simulations and decision-making models can provide transferable solution that can be used for similar scenarios regardless of the location. Research in infrastructure interdependency has evolved as a branch of disaster and emergency management very recently. In Canada, the first report that addressed the issue of infrastructure dependency was published by the Department of National Defence, National Contingency Planning Committee in 2000. This study was initially prepared as a stage in preparation for compatibility with Y2K. Eventually, this study has become a major reference in infrastructure interdependency. Since then very few publications came to existence. Particular concerns were raised in order to address the serious questions regarding infrastructure interdependency following certain events such as the power blackout of August 2003 in parts of Ontario and the SARS outbreak of 2003 in Toronto. These two events have illustrated key interdependencies of critical infrastructures, and have contributed to expedite the process of dealing with the issue of infrastructure interdependency. September 11, 2001 event in the US and other human-induced threats have added to the importance of determining interdependencies among infrastructure systems in Canada.
Utility of GIS in Infrastructure Interdependency Research The work reported by (Abdalla and Tao, 2005b) and some of which is the backbone for this dissertation is unique in Canada, and among very few international studies that highlighted the contribution of GIS in the new field of infrastructure interdepdency. There are many GIS analytical techniques are useable for infrastructure interdependency modeling. In this section,
GIS-based Multidimensional Approach
297
the utility of these techniques for addressing particular issues related to infrastructure interdependency research will be highlighted. Attributes-based Analysis
The power of GIS stems from its capacity to combine both the spatial attributes and the graphical representation of the feature. There are powerful analytical functions that can be used to model critical infrastructure interdependency. Functions like Query Within can provide very useful information about particular sector elements within a specific location. Summarize Table function can provide important information about a specific subset of attributes for a particular location. Index Attributes attribute functions can provide details about indexing critical elements for a particular infrastructure sector unit. Node-based Feature Analysis
Node-based feature analysis or point analysis can provide useful information when modeling LBI!. Functions like Distance can provide actual distance information between different critical infrastructure sectors, or between critical facilities, like hospitals, schools and others. Point-based analysis can also provide very useful information through Attribute Analysis; for instance point features for emergency medical service can provide many attributes about coverage area, its capacity and possible alternatives at peak times. Area-based Analysis
Area-based analysis provides advanced functions for polygon features. Functions like Dissolve and Eliminate can be used for simulation of "what if' scenarios. It can provide detailed information about new polygon features that might be created, with respect to area, neighboring infrastructures and adjacent facilities. Clip operations can be used in conducting polygon-based analysis to show what features might look like in extreme situations. Particular features can also be Split. Buffer operations can be used as spatial constraints in particular sectors, for example the user can identify a buffer of 500 meter zonation.
298
Rifaat Abdalla, Harris Ali, and Vincent Tao
Network-based Analysis Network-based analysis provides a wealth of GIS operations that can be used for modeling infrastructure interdependency. Network operations include line operations such as Buffer, which can be used on infrastructure layers to apply a buffer of a specific distance around a feature. The Dissolve operation can be used to unify features, (e.g. the dissolve operation can be used to dissolve two line segments into one line. Intersect can be used to perform feature intersection operations, for instance, in simulation modeling this function can be used to provide information about location attributes that might need to be split as a result of intersecting with a particular feature. Optimal Route Finding is very useful in determining the shortest path between two locations. In emergency situations this can be of use when trying to determine the optimal path between facilities and services. Raster-based Analysis Raster-based analysis provides functionality that is of importance when dealing with elevation data and with image analysis. There are a number of 3D analysis functions that are based on raster analysis. These include: Contouring, which can be used to provide linear elevation features derived from an elevation grid. This function can be of great use in modeling density grids for a particular distribution. Slope and aspect analysis and Hill shade Analysis are also important and provide information, for instance, when dealing with plume analysis. This information can be of great practical importance.
Concepts of Infrastructure interdependency Increasing complexity and interconnectedness among infrastructures has resulted in a range of interdependencies. These interdependencies have introduced new vulnerabilities and risks to our society. Canada's critical infrastructures are those physical and information technology facilities, networks and assets, which, if disrupted or destroyed, would have a serious impact on the health, safety, security or economic well-being of Canadians, or the effective functioning of governments in Canada (Trudeau, 2004). There is a limited understanding of Canada's infrastructure interdependencies, vulnerabilities and the methods for measuring and quantifying them, or how to mitigate interdependencies, having said that, Geographic
GIS-b ased Multidimensional Approach
299
interdepend ency can further be scaled down to emphasis location specific geographic interdependency.
Modeling infrastructure interdependency Wh en dealin g with the issue of modeling infrastru cture inte rdependency for disaster mana gement always there is a question of about the effec tiveness of modelin g tools to mimic a very complex real world situations . Another question arises about accuracy and validity of these models and to what level decision-maker ca n trust them. Unce rtainty and model accuracy fact ors are cri tica l and might infl uence or misguide the process of informed decision-makin g process, when rapidl y requ ired in ex treme situation s. An oth er issue with modeling infrastru cture interd epend ency is to what le vel can these models fit in extreme situations and how eve ryday operation ca n be balanced with security concern s.(Rinald i, 2004) has classified infras truc ture interdepend enc y mod els into six typ es as followin g
Infrastructure
Characterisrics
Types of Illlerdependencies
Fig. 1. Dimensions of infras tructure interde pendency 1. Aggr egate supply and demand tool s. Thi s category of tools eva luates the total dem and for infrastru cture ser vices in a region and the ability to supply those services . 2. Dynamic Simulations. Thi s model type employs dynamic simulations to examine infrastructures operations, the effects of disruptions, and the associated down stream co nsequences.
300
Rifaat Abdalla, Harris Ali, and Vincent Tao
3. Agent-based models. In this modeling technique, physical components of infrastructures can be readily modeled as agents, allowing analyses of the operational characteristics and physical states of infrastructures. 4. Physics-based models. Physical aspects of infrastructures can be analyzed with standard engineering techniques. For example, power flow and stability analyses can be performed on electric power grids. 5. Population mobility models. This model examines the movement of entities through urban regions. For example, the entities may be people following their daily routines in a city. 6. Leontief Input-Output Models. Leontief's model of economic flows can be applied to provide a linear, aggregated, time-independent analysis of the generation, flow, and consumption of various commodities among infrastructure sectors.
Case study The mock storyline for the earthquake used in this paper has focused on a shallow 7.3 MMI subduction earthquake occurs in the Strait of Georgia (Latitude 49.45 Longitude 123.941) with no surface rupture. At this magnitude and location, it is plausible to have the following occur: Landslide on Hornby Island, Fracture building damage in the City of Vancouver, Dam breach (assumed) and Flood Inundation in the west coast (Tsunami Wave). Figure 2 is showing study area and scenario details.
GIS-based Mult idim ension al Approach
30 1
Population at risk Using the Shake map and based on the magnitude distributi on it was possible to ident ify population catego ries at each risk zone. The obtained result s were then summarized as shown in Fig. 2..Abd alla and Tao, (2004); Amdahl , (200 1) have show n a Shakemap of California 's Northridge earthqua ke , which applies the principle of using soil clas sificati on to detect the most vuln erable zones . Further analysis was co nducted to provide den sity of popul ation at risk in each zone. Th e result s obtained popul ation potenti al loss density was in complete ag ree ment with result s obtained fro m building dama ge densit y analysis as sho wn in Fig.3. A tabular report that outlines distribut ion or population at risk was produc ed using GIS analytica l cap abiliti es .
302
Rifaat Abda lla, Harris A li, and Vincent Tao
Population by MMI Zones 150000 100000 50000 o
5 6 9 10 3 2 7 4 8
Fig. 3. Popul ation at risk distribut ion by zone
--
a
9 ,500
19 ,00 0
33 ,000
57 ,00 0
Fig. 4. Map showing vulnerable popul ation
GIS-based Multidimensiona l Approach
303
Critical infrastructure at risk Th e City of Va ncouver critica l infrastru cture asse ts were ex plored and ana lyzed usin g GIS. It was possib le to model and visua lize cri tica l infras truc tur e on the area and that are vulnerable to damage. All infrastructure sectors at ris k were se lec ted using GIS ana lysis functions and after they were ide ntifie d in the map (A bda lla and Tao , 2005 a), it was possibl e to produce a tabl e that lists vul nerab le seg ment based on the data. T abl e 1 is showing highways that are vulne rab le to damage. Th e table co ntai ns informa tion abo ut the name of the highway, in which mun icipa lity it is located, its class , and highway len gth . It is also possible to estimate potent ial fin ancial for each highway class based on a generic cost per kilometer estim ation. Table 1. Attr ibutes of vulnerable highways LEFT_MUll 2INANAIAO,SU8D.8
I
14 AL8ER1lfIIIY 15AL8ER1lfIIIY
2NANAIAO, SU8D.8
INANftJMO,SU8D.8I'9P NANJlJMO, SU8D.8 ygp
OBJECTID'
STREET
Cf1
16PARKSym 8YPASS I NANAIAO,SU8D. 8 17 AL8ER1lfIIIY
2NANAIAO,SU8D.8
18 AL8ER1lfIIIY
2NANAIAO, SU8D.8
RIGHT_MUll LEFTJSRlGHTJSAI LEFT]RV RlGHURV UIIIQUEID 'NANftJMO,S~D. 8 WI WI 'Be 8C 3820086
,NANJlJMO,SU8D.8 ygp NANJlJMO, SU8D.8 ygp
WI Y9P ygp ygp
ShipeJength 0.001594
i8C !8C
8C
3168775
0.014105
8C
382(1187
0.004812
'8C
8C
3820388
0.001943
8C
3820389
0000364
19 N..ANDI:lAND fIllY I NANAIAO, SU8D. 8
jNAN!lJMO,SU8D.8Y9P
WI
i8C 'Be
8C
382(1190
0005613
20 !"-ANDISlANDfIllY I NANAIAO, SUBO.8
,NAN!lJMO,SU8D8'I'9P
ygp
!8C
Be
382(1191
0006573
21 AL8ER1lfIIIY
NAN!lJMO, SU8D.8Y9P
ygp
8C
382(1192
0004732
22 PARKSYLLE8YPASS I NANAIAO, SU8D. 8
INANftJMO,S~D 8 WI
ygp
i8C i8C
8C
382(1393
0.007128
23 'N..ANDa AND HVW I NANAIAO, SU8D.8
INANftJMO,SU8D.8 I'9K
Y9!(
i8C
'8C
31 68802
0.063191
24N..ANDISlANDHVW I NANAIAO, SU8D.8
INANftJMO,SU8D.8Y9K ,NANAIMO,SU8D.8Y9!(
Y9!(
8C
8C
3168803
0.062780
Y9!(
,8C i8C
8C
3168846
0.004250
Y9!(
8C
3168847
0.005610
WI
iBe
3168942
0.003353
!8C
Be Be
3168943
0.01 4E1J8
i8C
8C
31689EIJ
0007829
iBe
8C
3168962
0011551
!8C
'8C
3168969
0011125
2NANAIAO, SU8D.8
25AL8ER1l fIIIY
2NANAIAO, SU8D.8
26 AL8ERM fIIIY
2NANAIAO, SU8D.8
27AL8ER1lfIIIY
2NANAIAO, SU8D.8
iNANftJMO,SU8D8 Y9!( :NANftJMO,SU8D.8Y9P
28AL8ERMfIIIY
2NANAIAO,SUBD 8
,NANftJMO,SU8D8 Y9P
ygp
29AL8ER1lfIIIY
2NANAIAO, SU8D. 8
NAN!lJMO, SUBD 8!Y9P
ygp
i
30 IN..ANDISlANDHVW I NANAIAO,SU8D.8
iNANftJMO,SU8D 8 WI
ygp
31iN..ANDaANDfIllY I NANAIAO, SU8D.8
:NANJlJMO,SU8D 8I'9P
ygp
32N..ANDISlAND HVW I NANAIAO,SU80.8
iNANftJMO,SU8D 8 Y9P .NANftJMO,SU8D.8 ygp
ygp
8C
8C
3168982
0021135
ygp
3168983
0007>38
ygp
,8C i8C
8C
iNANftJMO,SU8D.8 ygp NANftJMO,SU8D.8 Y9!(
8C
31 69047
0.027176
8C
3169085
0.006195 0.005]5
33 AL8ER1l fIIIY
2NANAIAO, SU8D. 8
34lU ND a ANO fIllY 1INANAIAO, SU8D.8
Y91{
36 GLANDHVW W
2NANAIAO, SUBD.8
NANftJMO, SU8D.8iY9!(
Y91{
iBe i8C
Be
3169094
37ELAND HVW W
2NANAIAO, SU8D.8
NANAIAO, SU8D.8Y9!(
Y91{
i8C
8C
3169113
0.005271
38GLANDHVW W
2NANAIAO, SU8D.8
iNANftJMO,SU8D.8Y9!(
Y91{
,Be
8C
3169161
0.011 485
35GLANDHVW W
2NANAIAO, SU8D.8
304
Rifaat Abdalla, Harris Ali, and Vincent Tao
Conclusions The application of GIS in disaster management activities has clearly proven its feasibility. Today's emergency management planning in its all levels utilize GIS data and functionality for simulation of "what if' scenarios. This paper has presented efforts that provide contribution in this field. The simulated earthquake model of the city of Vancouver has clearly demonstrated that GIS utility can further be utilized in modeling location based critical infrastructure interdependency. The presented model has provided a visual quantitative model of infrastructure and assets that are vulnerable within limited spatial extent. Geospatial data stored in municipal spatial databases and provincial data banks can be very useable when modeling small scale scenarios, however large scale simulations may require data and systems interoperability for effective decision-making process.
References Abdalla, R. and Tao, V., 2004. Applications of 3D Web-Based GIS in Earthquake Disaster Modeling and Visualisation. Environmental Informatics Archives, 2(2004): 814-817. Abdalla, R. and Tao, V., 2005a. Integrated Distributed GIS Approach for Earthquake Disaster Modeling and Visualization. In: v. Oosterom, Zlatanova and Fendel (Editors), Geo-information for Disaster Management. Springer, New York, pp. 1183-1192. Abdalla, R. and Tao, V., 2005b. A Network-Centeric Spatial Decision Support System for Modeling Infrastructure Interdpendency, Joint Infrastructure Interdepndencies Research Program Annual Symposium, Ottawa. Abdalla, R., Tao, V., Wu, H. and Maqsood, 1., 2006. A GIS-supported 3D approach for flood risk assessment of the Qu' Appelle River, Southern Saskatchewan. Int. J. Risk Assessment and Management, 6(4/5/6): 440-455. Alexander, D., 1993. Natural Disasters. Chapman & Hall, New York, 632 pp. Amdahl, G., 2001. Disaster response: GIS for public safety. ESRI Press, Redlands, Calif., v, 108 pp. Briggs, D., 2005. The role of GIS: Coping with space (and time) in air pollution exposure assessment. Journal of Toxicology and Environmental Health-Part aCurrent Issues, 68(13-14): 1243-1261. Dietzel, C., Herold, M., Hemphill, J.J. and Clarke, K.C., 2005. Spatio-temporal dynamics in California's central valley: Empirical links to urban theory. International Journal of Geographical Information Science, 19(2): 175-195. Giardino, M., Giordan, D. and Ambrogio, S., 2004. GIS technologies for data collection, management and visualization of large slope instabilities: two applications in the Western Italian Alps. Natural Hazards and Earth System Sciences, 4(2): 197-211.
GIS-based Multidimensional Approach
305
Gupta, P.K. and Singh, A.P., 2005. Disaster management for Nandira watershed district Angul (Orissa) India, using temporal Remote Sensing data and GIS. Environmental Monitoring and Assessment, 104(1-3): 425-436. Laben, C., 2002. Integration of remote sensing data and geographic information system technology for emergency managers and their applications at the Pacific Disaster Center. Optical Engineering, 41(9): 2129-2136. Montoya-Morales, A., 2002. Urban Disaster Management: A case study of Earthquake Risk Assessn1ent in Cartago, Costa Rica. Ph.D. Thesis, Ineternational Institute For Geo-Information Science And Earth Observation (ITC), Enschede, The Netherlands. Nash, E., James, P. and Parker, D., 2005. A model for spatio-temporal network planning. Computers & Geosciences, 31(2): 135-143. Rinaldi, S.M., 2004. Modeling and Simulating Critical Infrastructures and Their Interdependencies, Proceedings of IEEE 37th Hawaii International Conference on System Sciences, Hawaii, pp. 1-8. Trudeau, M., 2004. Infrastructure Interdependencies Workshop Report. PSEPC, Ottawa, pp. 27. Waugh, W.L., 1995. Geographic Information-Systems - the Case of Disaster Management. Social Science Computer Review, 13(4): 422-431.
