Understanding Risks and Building Enhanced Capabilities in Latin American Cities: Vulnerability and Risk Perception Data - Morro do Preventorio Case Study, 2019-2022

This dataset includes information about the physical vulnerability and risk perception in the favela Morro do Preventório, URBE Latam's case study neighbourhood in Brazil. The GIS data results were created through participatory data collection with the local communities.

Community leaders and volunteers collaborated to identify factors of physical vulnerability in the neighbourhood, drawing on the expertise of project team members from the British Geological Survey.

The community risk perception data represents the favela community’s perception regarding several types of geohazards and memories of past hazard events in their neighbourhood. The structured risk perception questionnaire (in Excel format) complements the co-produced risk perception GIS data and was co-designed by the community and the project team. Topics covered include socio-demographics and household composition and occupants’ perceptions of the building conditions, as well as memories of past disaster events and damages to the buildings. These datasets result from multiple interactions between the local project team and the community leaders, one of whom was a project team member.

The co-production of the physical vulnerability and risk perception data and risk perception survey led to two outcomes:

1) Fine-grained and richer data on factors of vulnerability and risk in the neighbourhood. Prior to the project, the neighbourhood had been unmapped. The project produced detailed and geospatial risk data.
2) The co-productive activity further strengthened the community’s ability for self-empowerment for risk reduction. Due to the co-productive nature of the data creation process, the data was directly relevant for community initiatives, such as the community bank’s micro-credits, and enabled the community to proactively engage with the municipal government to negotiate policy interventions for risk reduction. The data deposited here are thus a result of a novel collection protocol to generate trusted data for municipal disaster risk reduction policy-making.