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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Oliva, R., Oliveira, A. P. & Barros, J. (2025). Modeling localized social vulnerability through probabilistic simulation: A case study in the Lisbon metropolitan area. International Journal of Disaster Risk Reduction. 129
Exportar Referência (IEEE) 
R. A. Oliva et al.,  "Modeling localized social vulnerability through probabilistic simulation: A case study in the Lisbon metropolitan area", in Int. Journal of Disaster Risk Reduction, vol. 129, 2025
Exportar BibTeX 
@article{oliva2025_1764928316410,
	author = "Oliva, R. and Oliveira, A. P. and Barros, J.",
	title = "Modeling localized social vulnerability through probabilistic simulation: A case study in the Lisbon metropolitan area",
	journal = "International Journal of Disaster Risk Reduction",
	year = "2025",
	volume = "129",
	number = "",
	doi = "10.1016/j.ijdrr.2025.105792",
	url = "https://www.sciencedirect.com/journal/international-journal-of-disaster-risk-reduction"
}
Exportar RIS 
TY  - JOUR
TI  - Modeling localized social vulnerability through probabilistic simulation: A case study in the Lisbon metropolitan area
T2  - International Journal of Disaster Risk Reduction
VL  - 129
AU  - Oliva, R.
AU  - Oliveira, A. P.
AU  - Barros, J.
PY  - 2025
SN  - 2212-4209
DO  - 10.1016/j.ijdrr.2025.105792
UR  - https://www.sciencedirect.com/journal/international-journal-of-disaster-risk-reduction
AB  - Urban communities are increasingly vulnerable to social and environmental risks, necessitating robust tools for assessing and addressing these vulnerabilities. This study develops a localized Social Vulnerability Index (locSVI) model using an established framework and applies it to two Portuguese case studies: Bairro Encosta da Luz and the parish of Santiago, both situated in the Lisbon Metropolitan Area. The model integrates ten macro variables and context-specific weightings derived from resident survey data, supported by Monte Carlo simulations to validate and explore sensitivity in the results. Findings indicate that both communities fall into the “Very High” risk category, with key drivers including high population density, limited disaster preparedness, and in the case of Santiago, inadequate land use practices. The study highlights both the value and limitations of using composite indices for localized vulnerability assessment, emphasizing the need for community-based, data-driven interventions. Recommendations include enhancing civil protection mechanisms, improving land use planning, and promoting neighborhood cohesion. Despite methodological constraints related to sample size and data granularity, this work provides a scalable framework for guiding urban resilience strategies in similar contexts.
ER  -