Multi-hazard disaster impact analysis outputs from synthetic future urban scenarios in 10 cities

This dataset comprises spatially explicit risk analysis outputs for 10 study areas, representing potential disaster impacts across various future urban development scenarios, that are created by different community groups within each study area. Study areas include Istanbul (Türkiye), Nablus (Palestine), Chattogram (Bangladesh), Cox's Bazaar (Bangladesh), Nairobi (Kenya), Nakuru (Kenya), Quito (Ecuador), Kokhana (Nepal), Rapti (Nepal) and Darussalam (Tanzania). The data quantify the physical, social, and economic risks resulting from seismic activity or floods or landslides interacting with projected land-use plans and building typologies. Key components of the dataset include attributes on damage states for each building and eight metrics related to socio-demographic characteristics: - Number of workers unemployed, - Number of children with no Access to education - Number of households with no Access to hospital - Number of individuals with no Access to hospital - Number of households displaced - Number of homeless individuals - Population displacement - Number of casualties Details on the computation of each metric is provided in the readme file. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1). The dataset was generated using the Tomorrow's Cities Decision Support Environment (TCDSE) workflow, designed to assess disaster risks in future urban planning scenarios. The process involved the integration of three primary components: 1. Hazard Models: Physics-based simulations or probabilistic models for relevant hazards (e.g., earthquakes, floods, landslides) specific to the project location. 2. Future Exposure Scenarios: Spatially explicit projections of future urban growth, land use, and building typologies, co-developed with local stakeholders and planning authorities. 3. Vulnerability Functions: Engineering-based curves defining the susceptibility of proposed infrastructure to specific hazard intensities. These components were processed through a computational risk engine (served under webapp.tomorrowscities.org) to calculate potential impacts (e.g., physical damage, displacement, economic loss) for various future development trajectories. Quality Assurance (QA) Steps: 1. Input Validation: Hazard models and exposure data were validated by local domain experts and compared against historical baseline data where applicable. 2. Internal Consistency Checks: The computational workflow underwent code reviews and unit testing to ensure data integrity during the integration of hazard and exposure layers. 3. Output Verification: Preliminary risk results were reviewed in stakeholder workshops to ensure plausibility and alignment with local knowledge before finalization.