Data use and data needs in critical infrastructure risk analysis

Figures & data

Table 1. Search terms and selection when searching for literature in SCOPUS.

Search terms	Number of hits	Part read
Risk assessment	781.983	–
AND Decision support	18.503	–
AND critical infrastructure	442	Title, keyword
Resilience OR risk OR vulnerability AND method AND critical infrastructure	6.596	–
AND data AND decision AND case AND application	1.228	Title
LIMITED TO PUBYEAR 2010-2020	1.155	Title
LIMITED TO papers	801	Absctact
AND NOT TITLE review	776	Abstract + 76 full text
AND (disaster OR cris*s) AND reduction AND strateg*	213	Abstract
AND (national OR regional OR local)	183	Full text

Table 2. Most frequent sources represented in the sample.

Number of papers	Journal	Cite score 2019
19	International Journal of Disaster Risk Reduction	4.4
10	Sustainability (Switzerland)	3.2
8	Natural Hazards	5.0
8	Natural Hazards and Earth System Sciences	5.1
8	Reliability Engineering and System Safety	8.8
8	Risk Analysis	5.1
6	Sustainable Cities and Society	7.5
5	Transportation Research Part A: Policy and Practice	7.1
3	Annals of Operations Research	5.0
3	Applied Energy	16.4
3	Ecology and Society	7.5
3	Journal of Cleaner Production	10.9
3	Journal of Volcanology and Geothermal Research	5.1
3	Water (Switzerland)	3.0

Figure 1. Number of publications per year.

Figure 2. Research areas represented in the sample.

Table 3. Analytical framework.

Perspective	Questions investigated
Application domain	How is the area of application characterized? What is the purpose of the application? What data needs are communicated? How do existing databases cover the need? Which decision makers is the application aimed at supporting? Which level is addressed, regional, national, or international?
Formal properties	Which theory and/or model underpins the application? What assumptions is the method based on? How are goal conflicts and/or uncertainty handled? What limitations are reported upon?
Computer support	What data and databases are used? How are needs and access to data valued? What type of computer support is demonstrated? How is the computer support valued?

Table 4. Overview of the selection of papers for detailed analysis.

Paper	Application domain	Computer support	Formal model
I. Exploring the future: Runtime scenario selection for complex and time-bound decisions – Comes et al. (2015)	Crisis preparedness and crisis management, evacuation, hazardous substances / materials. National to local level.	Unspecified generic software for mathematical modeling	Scenario analysis, Multicriteria decision analysis
II. Risk-informed assessment of regional preparedness: A case study of emergency potable water for hurricane response in Southeast Virginia – Crowther (2010)	Supply of clean drinking water during and after storms. Region of Southwest Virginia (USA). Local and regional level.	Hazus-MH3	Probabilistic risk analysis, simulation of consequences, multi-objective risk tree
III. Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam – Fluixá-Sanmartín et al. (2019)	Dam safety, climate change, risk assessment. Local and regional level.	RS MINERVE and the R software package	Probabilistic risk analysis, index model, hydrological simulation of consequences
IV. Modeling the damage and recovery of interdependent civil infrastructure network using Dynamic Integrated Network model – He & Cha (2020)	Impact on critical infrastructure and their dependencies in the event of a hurricane. Regional level.	Unspecified generic software for mathematical modeling	Dynamic input-output model
V. A model-based systems engineering approach to critical infrastructure vulnerability assessment and decision analysis – Huff et al.(2019)	Focus on critical infrastructure and consequences of attacks and vulnerabilities.	Unspecified generic software for mathematical modeling	Linear programming
VI. Using geographic information systems to support decision making in disaster response – Jefferson & Johannes (2016)	Situational awareness during earthquakes. New Madrid Seismic Zone (USA). Local, regional and national level.	Hazus-MH 2.2, Esri ArcGIS software, Unibalance	Simulation of consequences, Thurstone C model for group decisions
VII. A framework for assessing the resilience of a disaster debris management system – Kim et al. (2018)	Analysis of alternative scenarios for debris removal after hurricane. Local level.	Hazus-MH, Vensim	System dynamics
VIII. Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System – Kong et al. (2019)	Resilience in critical infrastructure in the event of multiple natural disasters. Local level.	Unspecified	Probabilistic risk analysis, Monte-Carlo simulation
IX. Identification and prioritization of critical transportation infrastructure: Case study of coastal flooding – Lu et al. (2015)	Access to critical transport infrastructure, prioritization of criticality in connection with coastal floods. Coastal Area in Florida (USA) Local Level	ArcGIS	Index model, network model, simulation of consequences
X. A framework for comprehensive impact assessment in the case of an extreme winter scenario, considering integrative aspects of systemic vulnerability and resilience – Molarius et al. (2015)	Extreme winter with power outages and its impact on society and households. Hypothetical area in northern Finland. Local level.	VTT House building simulator and CRISECON	Index model, simulation of consequences
XI. A spatial-temporal vulnerability assessment to support the building of community resilience against power outage impacts – Münzberg (2017)	Society’s vulnerabilities and resilience with a focus on critical infrastructure and socially important activities in the event of a power outage. Local level.	Unspecified generic software	Monte-Carlo simulation, Delphi method, index model
XII. Developing a geographic information capacity (GIC) profile for disaster risk management under United Nations framework commitments – Tomaszewski et al. (2020)	Focuses on stakeholders’ ability to understand, access and work with geographic information for disaster risk management	ArcGIS	Statistical methods
XIII. A federated pre-event community resilience approach for assessing physical and social sub-systems – Yang et al. (2020)	Resilience and performance of critical infrastructure, such as extreme rainfall pipelines. Local level.	ArcGIS, Matlab.	Simulation

Figure 3. Three main dimensions of data sources for scenario modelling.

Table A1. Data sources used in sample.

Data source	Global, National, Regional, Local	Open data	Satellite data	Registry data	GIS/Spatial data	Historical data	Climate/Weather data	Specifc data
OpenStreetMap^2	G	X	X		X
FEMA - Federal Emergency Management Agency^3	N	X	X	X	X	X		X
U.S. Bureau of Economic Analysis^4	N	X		X
US Census Bureau^5	N	X		X
Census of the UK and Scotland^6	N	X		X
Common Vulnerabilities and Exposures (CVE)^7	G	X				X		X
Emergency Events Database EM-DAT^8	G	X	X		X	X		X
National Disaster Management Agency of Indonesia^9	N					X
Australian Government Department of Transport and Regional Services^10	N	X		X		X
The World Bank and Queensland Reconstruction Authority^11	R	X		X	X	X
National Register of Radio Frequencies (New Zealand)^12	N	X						X
Defense Meteorological Satellite Program platforms’ Operational Linescan System, Visible Infrared Imaging Radiometer Suite on-board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project platform^13	G	X	X		X
Volusia County’s Geographic Information Services branch^14 Florida Geographic Data Library^15	R	X			X
Florida Department of Transportation^16	R	X	X	X	X
Florida Bureau of Economic and Business^17	R	X		X
Paul Scherrer Institute’s ENergy-related Severe Accident Database (ENSAD)^18	G					X		X
World Input-Output Database^19	G	X		X
European Centre for Medium-Range Weather Forecasts (ECMWF)^20	G	X	X				X
National Centers for Environmental Prediction (NCEP)^21	N	X	X				X
National Centers for Environmental Information at NOAA^22	G	X	X				X
The Statistical Center of Iran^23	N	X		X
Ordnance Survey^24	N		X	X	X
British Geological Survey GeoSure landslide susceptibility data^25	N	X	X		X			X
Dryad digital repositories^26	G	X					X	X
New York City Taxi and Limousine Commission^27	L							X
MOnitoring of STructures of Strategic Public Buildings - MOSTspb^28	L	X						X
LaMEVE - Large Magnitude Explosive Volcanic Eruptions^29	G	X			X	X		X
New Zealand, national baseline data on health, QoL, well-being, and emergency preparedness at the population level^30	N	X		X
Federal Office for the Environment (Schweiz)^31	N	X	X		X		X
Federal Office of Topography swisstopo (Schweiz)^32	N	X	X		X	X
Federal Office for Statistics (Schweiz)^33	N	X		X		X
HAZUS-MR3 database (FEMA)^34	N	X	X	X	X	X		X
IEEE test cases^35	L	X
Pima County Regional Flood Control District - Alert Map website, (real-time rainfall and streamflow data)^36	R	X	X		X		X
UN Int Strategy for Disaster Reduction – The Global Assessment Report^37	G	X		X		X
Human Development Index^38	G	X		X		X
World Data Atlas^39	G	X	X	X	X	X	X	X
DesInventar – Disaster Information Management System^40	G	X	X		X	X
Cemaden (Brazil) meterological monitoring system^41	N	X	X		X		X
Annual African Property and Construction Handbook^42 Cool Roofs and Cool Pavements Toolkit & Guide^43	N	X			X		X	X
NASA Shuttle Radar Topography Mission project website^44 NASA Earth Science Data ^45	G	X	X		X		X
CSIR [Council of Scientific Industrial Research] repositories^46	N	X		X
Survey of Tanzania’s Geological and Mineral Information System^47	N		X		X
World Soil Information data^48	G	X	X		X			X
Online Tidal Prediction data^49	G	X	X				X
NASA Tropical Rainfall Measuring Mission (data processing ended)^50	G	X	X				X
NOAA – Bathymetry Digital Elevation Model och data^51	G	X	X		X
US Geological Survey website^52	N	X	X			X
Instituto Geográfico Nacional^53	N	X	X			X
University of Wyoming, Dept of Atmospheric Science^54	N	X					X
Inventory of New York City’s Greenhouse Gas Emissions, 2016^55	L	X		X			X
Korean Statistical Information Service^56	N	X		X
Korea Meterological Administration Weather Data Service^57	N	X					X
Disaster management programme of the Government of Bangladesh^58	N	X						X
New South Wales Road and Maritime Services database^59	R	X		X	X			X
Provincial Government of Lower Austria datasets^60	R	X	X		X
Ministry of Transportation and Communications, Taiwan^61	N	X		X	X
INSEE-National Institute of Statistics and Economic Studies, France^62	N	X		X
State Meteorological Agency (AEMET), Spain^63	N	X					X
Earth System Grid Federation^64 [software/platform/simulations]	G	X						X
ECMWF- European Centre for Medium-Range Weather Forecasts^65	G	X	X				X
Global Risk Data Platform^66	G	X	X		X
Department of Homeland Security - National Bridge Inventory^67	N	X			X			X
US Census Bureau (TIGER geodatabases)^68	N	X	X		X
UK Power network data^69	N	X						X
Government of Canada - CanVec, Topographic Data^70	N	X			X
NOAA - Cyclone intensity and frequency data^71	N	X					X
USGS Global Visualization Viewer^72	G	X	X		X
JAXA-Japan Aerospace Exploration Agency^73	G	X	X				X
EURO-CORDEX^74	G	X			X		X
Copernicus CORINE Land Cover inventory^75	G	X	X		X
NOAA - Historical hurricane data^76	G	X	X				X
National Wild and Scenic River System^77	N	X			X

Comes, T., N. Wijngaards, and B. Van de Walle. 2015. “Exploring the Future: Runtime Scenario Selection for Complex and Time-Bound Decisions.” Technological Forecasting and Social Change 97: 29–46. doi:10.1016/j.techfore.2014.03.009.

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Crowther, K. G. 2010. “Risk-Informed Assessment of Regional Preparedness: A Case Study of Emergency Potable Water for Hurricane Response in Southeast Virginia.” International Journal of Critical Infrastructure Protection 3 (2): 83–98. doi:10.1016/j.ijcip.2010.03.001.

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Fluixá-Sanmartín, J., A. Morales-Torres, I. Escuder-Bueno, and J. Paredes-Arquiola. 2019. “Quantification of Climate Change Impact on Dam Failure Risk under Hydrological Scenarios: A Case Study from a Spanish Dam.” Natural Hazards and Earth System Sciences 19 (10): 2117–2139. doi:10.5194/nhess-19-2117-2019.

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Jefferson, T. L., and T. W. Johannes. 2016. “Using Geographic Information Systems to Support Decision Making in Disaster Response.” Intelligent Decision Technologies 10 (2): 193–207. doi:10.3233/IDT-160255.

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Kim, J., A. Deshmukh, and M. Hastak. 2018. “A Framework for Assessing the Resilience of a Disaster Debris Management System.” International Journal of Disaster Risk Reduction 28: 674–687. doi:10.1016/j.ijdrr.2018.01.028.

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Kong, J., S. P. Simonovic, and C. Zhang. 2019. “Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System.” Risk Analysis 39 (5): 1141–1168. doi:10.1111/risa.13222.

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Lu, Q.-C., Z.-R. Peng, and J. Zhang. 2015. “Identification and Prioritization of Critical Transportation Infrastructure: Case Study of Coastal Flooding.” Journal of Transportation Engineering 141 (3). doi:10.1061/(ASCE)TE.1943-5436.0000743.

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Molarius, R., P. Tuomaala, K. Piira, M. Räikkönen, C. Aubrecht, M. Polese, G. Zuccaro, K. Pilli-Sihvola, and K. Rannat. 2015. “A Framework for Comprehensive Impact Assessment in the Case of an Extreme Winter Scenario, considering Integrative Aspects of Systemic Vulnerability and Resilience.” Computer Modeling in Engineering and Sciences 109 (2): 131–158.

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Münzberg, T., M. Wiens, and F. Schultmann. 2017. “A Spatial-Temporal Vulnerability Assessment to Support the Building of Community Resilience against Power Outage Impacts.” Technological Forecasting and Social Change 121: 99–118. doi:10.1016/j.techfore.2016.11.027.

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Tomaszewski, B. M., E. A. Moore, K. Parnell, A. M. Leader, W. R. Armington, O. Aponte, L. Brooks, et al. 2020. “Developing a Geographic Information Capacity (GIC) Profile for Disaster Risk Management under United Nations Framework Commitments.” International Journal of Disaster Risk Reduction 47: 101638. doi:10.1016/j.ijdrr.2020.101638.

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Yang, Y., S. T. Ng, S. Zhou, F. J. Xu, D. Li, and H. Li. 2020. “A Federated Pre-Event Community Resilience Approach for Assessing Physical and Social Sub-Systems: An Extreme Rainfall Case in Hong Kong.” Sustainable Cities and Society 52: 101859. doi:10.1016/j.scs.2019.101859.

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