Assessment of socioeconomic vulnerability to landslides using an indicator-based approach: methodology and case studies View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-02-12

AUTHORS

U. M. K. Eidsvig, A. McLean, B. V. Vangelsten, B. Kalsnes, R. L. Ciurean, S. Argyroudis, M. G. Winter, O. C. Mavrouli, S. Fotopoulou, K. Pitilakis, A. Baills, J.-P. Malet, G. Kaiser

ABSTRACT

The severity of the impact of a natural hazard on a society depends on, among other factors, the intensity of the hazard and the exposure and resistance ability of the elements at risk (e.g., persons, buildings and infrastructures). Social conditions strongly influence the vulnerability factors for both direct and indirect impact and therefore control the possibility to transform the occurrence of a natural hazard into a natural disaster. This article presents a model to assess the relative socioeconomic vulnerability to landslides at the local to regional scale. The model applies an indicator-based approach. The indicators represent the underlying factors that influence a community’s ability to prepare for, deal with, and recover from the damage and loss associated with landslides. The proposed model includes indicators that characterize the demographic, social and economic setting as well as indicators representing the degree of preparedness, effectiveness of the response and capacity to recover. Although this model focuses primarily on the indirect losses, it could easily be extended to include physical indicators accounting for the direct losses. Each indicator is individually ranked from 1 (lowest vulnerability) to 5 (highest vulnerability) and weighted, based on its overall degree of influence. The final vulnerability estimate is formulated as a weighted average of the individual indicator scores. The proposed model is applied for six case studies in Europe. The case studies demonstrate that the method gives a reasonable ranking of the vulnerability. The practical experience achieved through the case studies shows that the model is straightforward for users with knowledge on landslide locations and with access to local census data. More... »

PAGES

307-324

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10064-014-0571-2

DOI

http://dx.doi.org/10.1007/s10064-014-0571-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026237331


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