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This chapter focuses upon the potential of co-production in research linked to risk and resilience. Specifically, it proposes the need to see resilience practice as co-production and illustrates the argument with reflections from the Waterproofing Data project, which investigated water-related risks, with a focus on social and cultural aspects of data practices. The chapter describes how the project adopted a dialogic approach and developed innovative methods around data practices into a method that reframed citizen sensing as a critical pedagogical process. Here the engagement of residents of urban poor neighbourhoods in Brazil was central for a process of research co-production together with a multidisciplinary research team and stakeholders of local governmental and non-governmental organisations involved in disaster risk reduction. Ultimately, we argue that co-production is a necessary ingredient in research looking to transform governance and decision-making in how we respond, in a flexible way, to risks and crisis.

This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84% in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums, the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required.

This article investigates the role of digital technologies and data innovations, such as big data and citizen-generated data, to enable transformations to sustainability. We reviewed recent literature in this area and identified that the most prevailing assumption of work is related to the capacity of data to inform decision-making and support transformations. However, there is a lack of critical investigation on the concrete pathways for this to happen. We present a framework that identifies scales and potential pathways on how data generation, circulation and usage can enable transformations to sustainability. This framework expands the perspective on the role and functions of data, and it is used to outline a critical research agenda for future work that fully considers the socio-cultural contexts and practices through which data may effectively support transformative pathways to sustainable development.\ud \ud

This paper presents results from the application of a methodological framework developed as part of an ongoing research project focused on understanding inequalities in the healthcare access of slum residents of cities in four countries: Bangladesh, Kenya, Pakistan and Nigeria. We employ a systematic approach to produce, curate and analyse volunteered geographic information (VGI) on urban communities, based on a combination of collaborative satellite-imagery digitization and participatory mapping, which relies upon geospatial open-source technologies and the collaborative mapping platform OpenStreetMap. Our approach builds upon and extends humanitarian mapping practices, in order to address the twofold challenge of achieving equitable community engagement whilst generating spatial data that adheres quality standards to produce rigorous and trusted evidence for policy and decision making. Findings show that our method generated promising results both in terms of community engagement and the production of high-quality data on communities to analyse urban inequalities.