Abstract
This paper analyses the issue of urban vulnerability assessment, aiming to identify appropriate strategies to mitigate the impacts of climate change, through decision-making processes that are attentive to the spatial, territorial and geographical scale. In complex decision-making problems, the spatial assessment of homogeneous vulnerability classes can become a useful support for translating the value of vulnerability into intervention priorities and enabling the selection of appropriate intervention alternatives. Urban vulnerability is a complex phenomenon requiring significant and effective indicators that allow an adequate assessment both in quantitative and qualitative terms.
Among the different multidimensional approaches present in the literature, as part of the METROPOLIS (Metodologie E Tecnologie integRate e sOstenibili Per l’adattamentO e La sicurezza deI Sistemi urbani) research project, developed by the local unit of the Department of Architecture, University of Naples Federico II, the multi-criteria and multi-group analysis method TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was applied; this method is particularly suitable in complex decisional contexts, such as the level of vulnerability of a territory, where the kind of information relative to the performances to be evaluated presents considerable levels of uncertainty. The integration of the TOPSIS method in the GIS (Geographic Information System) makes it possible to test the opportunities of an integrated and cross-scale evaluation model, by structuring a Spatial Decision Support System (SDSS) applied to the case study of Naples, in the South of Italy.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
UNEP: Cities and Urban Vulnerability in the Context of Urban Environmental Management. A Concept Paper (2007). https://www.unisdr.org/files/5453_092UNE.pdf
IPCC: Climate Change and Biodiversity. IPCC, Geneva, Switzerland (2002). Core Writing Team, Gitai, H., Suárez, A., Watson, R.T., Dokken, D.J. (eds.)
McGeary, M.G.H., Lynn, L.E.: Urban Change and Poverty. National Academy Press, Washington, D.C. (1988)
Perchinunno, P., Rotondo, F., Torre, C.M.: A multivariate fuzzy analysis for the regeneration of urban poverty areas. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008. LNCS, vol. 5072, pp. 137–152. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69839-5_11
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland (2014). Core Writing Team, Pachauri, R.K., Meyer, L.A. (eds.)
Füssel, H.M., Jol, A.: Climate change, impacts and vulnerability in Europe 2012. An indicator-based report. European Environment Agency (EEA) Report, 12, EEA, Copenhagen (2012)
IPCC: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2007). Core Writing Team, Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (eds.)
Bernstein, L.B.P., Canziani, O.: Intergovernmental panel on climate change. Fourth Assessment Report. Climate change 2007, Synthesis Report (2007)
Swart, R., Fons, J., Geertsema, W., van Hove, B., Gregor, M., Havranek, M., Peltonen, L.: Urban vulnerability indicators: a joint report of ETC-CCA and ETC-SIA. ETC-CCA and ETC-SIA Technical report 1/2012 (2012)
Chung, E.S., Lee, K.S.: Identification of spatial ranking of hydrological vulnerability using multi-criteria decision making techniques: case study of Korea. Water Resour. Manag. 23(12), 2395–2416 (2009)
Jun, K.S., Chung, E.S., Kim, Y.G., Kim, Y.: A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts. Expert Syst. Appl. 40(4), 1003–1013 (2013)
Lee, G., Jun, K.S., Chung, E.S.: Integrated multi-criteria flood vulnerability approach using fuzzy TOPSIS and Delphi technique. Nat. Hazards Earth Syst. Sci. 13(5), 1293–1312 (2013)
Kim, Y., Chung, E.S.: Assessing climate change vulnerability with group multi-criteria decision making approaches. Clim. Chang. 121(2), 301–315 (2013)
Kubal, C., Haase, D., Meyer, V., Scheuer, S.: Integrated urban flood risk assessment–adapting a multicriteria approach to a city. Nat. Hazards Earth Syst. Sci. 9(6), 1881–1895 (2009)
Hinkel, J.: “Indicators of vulnerability and adaptive capacity”: towards a clarification of the science–policy interface. Global Environ. Chang. 21(1), 198–208 (2011)
Adger, W.N.: Vulnerability. Global Environ. Chang. 16(3), 268–281 (2006)
Moss, R.H., Brenkert, A.L., Malone, E.L.: Vulnerability to climate change: a quantitative approach, pp. 155–167. Pacific Northwest National Laboratory (PNNL-SA-33642). Prepared for the US Department of Energy (2001)
O’Brien, K., Leichenkob, R., Kelkarc, U., Venemad, H., Aandahla, G., Tompkinsa, H., Javedc, A., Bhadwalc, S., Bargd, S., Nygaarda, L., Westa, J.: Mapping vulnerability to multiple stressors: climate change and globalization in India. Global Environ. Chang. 14(4), 303–313 (2004)
Balena, P., Sannicandro, V., Torre, C.M.: Spatial multicrierial evaluation of soil consumption as a tool for SEA. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8581, pp. 446–458. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09150-1_32
Ishizaka, A., Nemery, P.: Multi-criteria Decision Analysis: Methods and Software. Wiley, Hoboken (2013)
Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making Methods and Applications. Springer, Heidelberg (1981). https://doi.org/10.1007/978-3-642-48318-9
Huang, I.B., Keisler, J., Linkov, I.: Multi-criteria decision analysis in environmental science: ten years of applications and trends. Sci. Total Environ. 409, 3578–3594 (2011)
Assari, A., Mahesh, T., Assari, E.: Role of public participation in sustainability of historical city: usage of TOPSIS method. Indian J. Sci. Technol. 5(3), 2289–2294 (2012)
Hung, C.C., Chen, L.H.: A fuzzy TOPSIS decision making model with entropy weight under intuitionistic fuzzy environment. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists IMECS, Hong Kong (2009)
Roszkowska, E.: Multi-criteria decision making models by applying the topsis method to crisp and interval data. Mult. Criteria Decis. Mak. 6, 200–230 (2011)
Eicher, C.L., Brewer, C.A.: Dasymetric mapping and areal interpolation: implementation and evaluation. Cartogr. Geogr. Inf. Sci. 28(2), 125–138 (2001)
Sleeter, R.: Dasymetric mapping techniques for the San Francisco Bay region, California. In: Proceedings of the Urban and Regional Information Systems Association, Annual Conference, Reno, Nevada, 7–10 November 2004 (2004)
Mennis, J.: Generating surface models of population using dasymetric mapping. Prof. Geogr. 55(1), 31–42 (2003)
O’Sullivan, D., Unwin, D.: Geographic Information Analysis. Wiley, Hoboken (2014)
Maantay, J.A., Maroko, A.R., Herrmann, C.: Mapping population distribution in the urban environment: the cadastral-based expert dasymetric system (CEDS). Cartogr. Geogr. Inf. Sci. 34(2), 77–102 (2007)
Holloway, S.R., Schumacher, J., Redmond, R.L.: People and place: dasymetric mapping using arc/info. Cartographic Design Using ArcView and Arc/Info, Wildlife Spatial Analysis Lab, University of Montana, Missoula (1997)
Chu, T.C.: Facility location selection using fuzzy TOPSIS under group decisions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(06), 687–701 (2002)
Shih, H.S., Shyur, H.J., Lee, E.S.: An extension of TOPSIS for group decision making. Math. Comput. Model. 45(7), 801–813 (2007)
Saari, D.G.: The Bordà dictionary. Soc. Choice Welf. 7(4), 279–317 (1990)
Amato, F., Pontrandolfi, P., Murgante, B.: Supporting planning activities with the assessment and the prediction of urban sprawl using spatio-temporal analysis. Ecol. Inform. 30, 365–378 (2015)
Fusco, G.L., Cerreta, M., De Toro, P.: Integrated assessment for sustainable choices. Scienze Regionali 13(1), 111–142 (2014)
Mokhtarian, M.N., Hadi-Vencheh, A.: A new fuzzy TOPSIS method based on left and right scores: an application for determining an industrial zone for dairy products factory. Appl. Soft Comput. 12(8), 2496–2505 (2012)
Cerreta, M., Panaro, S.: From perceived values to shared values: a multi-stakeholder spatial decision analysis (M-SSDA) for resilient landscapes. Sustainability 9, 1–20 (2017)
Cerreta, M., Inglese, P., Malangone, V., Panaro, S.: Complex values-based approach for multidimensional evaluation of landscape. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8581, pp. 382–397. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09150-1_28
Cerreta, M., Poli, G.: Landscape services assessment: a hybrid multi-criteria spatial decision support system (MC-SDSS). Sustainability 9, 1–18 (2017)
Attardi, R., Cerreta, M., Sannicandro, V., Torre, C.M.: Non-compensatory composite indicators for the evaluation of urban planning policy: the land-use policy efficiency index (LUPEI). Eur. J. Oper. Res. 264, 491–507 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Cerreta, M., Mele, R., Poli, G. (2018). Urban Vulnerability Assessment: Towards a Cross-Scale Spatial Multi-criteria Approach. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-95168-3_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95167-6
Online ISBN: 978-3-319-95168-3
eBook Packages: Computer ScienceComputer Science (R0)