Future exposure modelling for risk-informed decision making in urban planning. (May 2023)

• Record Type: Journal Article
• Authors: Menteşe, Emin Yahya; Cremen, Gemma; Gentile, Roberto; Galasso, Carmine; Filippi, Maria Evangelina; McCloskey, John
• Is Part Of: International journal of disaster risk reduction. Volume 90(2023)
• Publication Date: 2023-05
• Subjects: Land use -- Plan -- Exposure -- Disaster risk assessment -- Future; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2023.103651
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: Population increases and related urban expansion are projected to occur in various parts of the world over the coming decades. These future changes to the urban fabric could fundamentally alter the exposure to natural hazards and the associated vulnerability of people and the built environment with which they interact. Thus, modelling, quantifying, and reducing future urban disaster risk require forward-looking insights that capture the dynamic form of cities. This paper specifically focuses on the exposure component of dynamic natural-hazard disaster risk, by considering urban planning as the centre of future exposure characterisation in a given region. We use the information provided by urban plans and propose an integrated data structure for capturing future exposure to hazards. The proposed data structure provides the necessary detailing for both future physical and socio-demographic exposure in disaster risk modelling. More specifically, it enables users to develop a comprehensive multi-level, multi-scale exposure dataset, characterising attributes of land use, buildings, households and individuals. We showcase the proposed data schema using the virtual urban testbed Tomorrowville. In this case study, we also demonstrate how simplified algorithmic procedures and disaggregation methods can be used to populate the required data. This implementation demonstrates how the proposed exposure data structure can effectively support the development of forward-looking urban visioning scenarios to support decision-making for risk-sensitive and pro-poor urban planning and design in tomorrow's cities. Graphical abstract: Highlights: Land-use plans can be used as a basis for understanding future exposure to hazards We propose a data structure and potential approaches for modelling future exposure We showcase our proposed structure and methods for a Global South context