A modular approach for vulnerability assessment in the Southern Italy: A forest fire hazard scenario
DOI:
https://doi.org/10.5055/jem.0706Keywords:
vulnerability index, road networks, forest fire hazard, territorial context, emergency response, indices and mapsAbstract
Vulnerability is an important component for risk assessment, representing the main element in the perception of the risk. This paper shows a methodological approach to describe a composite vulnerability index suitable to be used at the census scale. The aim of this study is threefold: at first, a new administrative limit, “Territorial Context” (TC), for the purpose of the emergency management is investigated. Second, it improves the common vulnerability methods applying different weights and a new approach of vulnerability levels classification by using fuzzy analysis. Finally, it provides vulnerability maps, which represent a simple way to identify areas where the capacity to cope with a hazard, during an event, is strongly influenced by social and territorial conditions and the road infrastructure accessibility.
The proposed TC Vulnerability Index (TCVI) aggregates three indices to capture the complex realities of the investigated system that cannot be adequately quantified by a single index. Specifically, two indices measure the social (TCVIpeople) and territorial (TCVIexposure) vulnerability through an inductive approach based on the principal component analysis. The third index (TCVIemergency) provides a measure of the emergency management operating system, essentially based on the possibility of moving to safe areas, for citizens, or to coordination centers and risk areas, for rescuers.
Results show notable differences in the spatial distribution of vulnerability, highlighting the multidimensionality and heterogeneity of the census area characteristics. These findings would provide a scientific base for the public decision-makers to implement effective disaster prevention and mitigation in Mediterranean areas.
References
Birkmann J: Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environ Hazard. 2007; 7(1): 20-31.
Adger WN, Kelly PM, Ninh NH: Living with environmental change: Social vulnerability, adaptation and resilience in Vietnam. 2012; 1-314. DOI: 10.4324/9780203995570.
Adger WN: Vulnerability. Global Environ Change. 2006; 16(3): 268-281.
Fekete A, Damm M, Birkmann J: Scales as a challenge for vulnerability assessment. Nat Hazards. 2010; 55(3): 729-747.
Paul S: Vulnerability concepts and its application in various fields: A review on geographical perspective. J Life Earth Sci. 2014; 8: 63-81.
Hufschmidt G: A comparative analysis of several vulnerability concepts. Nat Hazards. 2011; 58(2): 621-643.
Cardona O: Disaster risk and vulnerability: Notions and measurement of human and environmental insecurity. In Coping with Global Environmental Change Disasters and Security—Threats Challenges Vulnerabilities and Risks. Berlin: Springer, 2011.
Cardona OD: The need for rethinking the concepts of vulnerability and risk from a holistic perspective: A necessary review and criticism for effective risk management. In Mapping vulnerability: Disasters, Development, and People. Milton Park: Routledge, 2013: 37-51.
Ciurean RL, Schroter GT: Conceptual frameworks of vulnerability assessments for natural disasters reduction. In Tiefenbacher J (Ed.): Approaches to Disaster Management—Examining the Implications of Hazards, Emergencies and Disasters. InTech, 2013.
UNISDR: Risk and Poverty in a Changing Climate: Invest Today for a Safer Tomorrow. United Nations International Strategy for Natural Disaster Reduction Global Assessment Report on Disaster Risk Reduction, 2009: 207.
Cutter S, Ismail-Zadeh A, Alcántara-Ayala I, et al.: Global risks: Pool knowledge to stem losses from disasters. Nature. 2015; 522: 277-279.
Orru K, Hansson S, Gabel F, et al.: Approaches to ‘vulnerability’ in eight European disaster management systems. Disasters. 2022; 46(3): 742-767. DOI: 10.1111/disa.12481.
Tate E: Social vulnerability indices: A comparative assessment using uncertainty and sensitivity analysis. Nat Hazards. 2012; 63: 325-347.
Cutter SL, Boruff BJ, Shirley WL: Social vulnerability to environmental hazards. Soc Sci Quar. 2003; 84(2): 242-261.
Borden K, Schmidtlein MC, Emrich CT, et al.: Vulnerability of U.S. cities to environmental hazards. J Homeland Secur Emerg Manag. 2007; 4.
Boruff B, Emrich C, Cutter S: Erosion hazard vulnerability of US coastal counties. J Coastal Res. 2005; 215: 932-942.
Holand I, Lujala P, Rød JK: Social vulnerability assessment for Norway: A quantitative approach. Norwegian J Geogr. 2011; 65: 1-17.
Wood E, Sanders M, Frazier T: The practical use of social vulnerability indicators in disaster management. Int J Disas Risk Reduct. 2021; 63: 102464.
Spielman S, Tuccillo J, Folch D, et al.: Evaluating social vulnerability indicators: Criteria and their application to the social vulnerability index. Nat Hazards. 2020; 100: 417-436.
Flanagan B, Hallisey E, Sharpe JD, et al.: On the validity of validation: A commentary on Rufat, Tate, Emrich, and Antolini’s “how valid are social vulnerability models?” Annal Am Assoc Geogr. 2021; 111: Em-i-6.
Berdica K: An introduction to road vulnerability: What has been done, is done and should be done. Transport Policy. 2002; 9: 117-127.
Jenelius E, Mattsson L-G: Developing a methodology for road network vulnerability analysis. 2006.
Taylor M, D’Este G: Transport network vulnerability: a method for diagnosis of critical locations in transport infrastructure systems. In Critical Infrastructure. Berlin: Springer, 2007: 9-30.
Jenelius E, Petersen T, Mattsson L-G: Road network vulnerability: Identifying important links and exposed regions. Transport Research Arena Conference, June 12-15, 2006: 20.
Union E, Commission J: Handbook on Constructing Composite Indicators: Methodology and User Guide. Brussels: European Commission, 2008: 163. DOI: 10.1787/533411815016.
Nardo M, Saisana M, Saltelli A, et al.: Handbook on Constructing Composite Indicators and User Guide. vol. 2005, 2008.
Papathoma-Köhle M, Schlögl M, Fuchs S: Vulnerability indicators for natural hazards: An innovative selection and weighting approach. Sci Rep. 2019; 9.
Garschagen M, Hagenlocher M, Comes M, et al.: WorldRiskReport 2016 [English Version]. Berlin: Bündnis Entwicklung Hilft and UNU-EHS, 2016.
Trigila A, Iadanza C, Lastoria B, et al.: Dissesto idrogeologico in Italia: pericolosità e indicatori di rischio, 2021; 356.
Provitolo D, Dubos-Paillard E, Müller J-P: Emergent human behaviour during a disaster: Thematic versus complex systems approaches. 2011.
Caloiero T, Coscarelli R, Ferrari E: Analysis of monthly rainfall trend in Calabria (Southern Italy) through the application of statistical and graphical techniques. 3rd EWaS International Conference Insights on the Water-Energy-Food Nexus, 2018; 2: 489-497.
Mavhura E, Manyena B, Collins AE: An approach for measuring social vulnerability in context: The case of flood hazards in Muzarabani district, Zimbabwe. Geoforum. 2017; 86: 103-117.
Thomas R: Traffic Assignment Techniques. England: Avebury Technical, 1991.
Camargo P: AequilibraE—A free QGIS add-on for transportation modeling. Available at http://aequilibrae.com/. Accessed June 20, 2022.
Kazmierczak A, Cavan G: Surface water flooding risk to urban communities: Analysis of vulnerability, hazard and exposure. Landsc Urban Plann. 2011; 103: 185-197.
Dwyer A, Zoppou C, Nielsen OM, et al.: Quantifying social vulnerability: A methodology for identifying those at risk to natural hazards. 2004; 14.
Török I: Qualitative assessment of social vulnerability to flood hazards in Romania. Sustainability. 2018; 10: 3780.
D’Este GM, Taylor M: Network vulnerability: An approach to reliability analysis at the level of national strategic transport networks. Netw Reliabil Transport. 2003: 23-44.
Zadeh L: Fuzzy sets. Proc IEEE. 1965; 8: 338-353.
Raines GL, Sawatzky DL, Bonham-Carter GF: Incorporating expert knowledge: New fuzzy logic tools in ArcGIS 10. 2010. Available at https://www.esri.com/news/arcuser/0410/fuzzylogic.html. Accessed June 20, 2022.
Brunsdon C, Fotheringham A, Charlton M: Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr Anal. 2010; 28: 281-298.
Fotheringham A, Brunsdon C, Charlton M: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Hoboken, NJ: John Wiley & Sons, 2002: 13.
Osborne JW: Best Practices in Exploratory Factor Analysis. Scotts Valley, CA: CreateSpace, 2014.
Maletta R, Mendicino G: A methodological approach to assess the territorial vulnerability in terms of people and road characteristics. Georisk Assess Manag Risk Eng Syst Geohazards. 2020: 1-14.
Published
How to Cite
Issue
Section
License
Copyright 2007-2023, Weston Medical Publishing, LLC and Journal of Emergency Management. All Rights Reserved