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A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.
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Enhancing digital twin technology with community-led, science-driven participatory modeling: A case in green infrastructure planning
Recent research, professional, and funding agendas have re-surfaced the importance of knowledge co-production and ethical participation to address urban tensions worldwide: urbanization and rapid climate change, disproportionately impacting socially vulnerable populations. Despite the rise of Digital Twins (DT), buoyed by the growth of computational and data technologies in the past 10 to 15 years, DT have fallen short of their promise to address these tensions. We present a participatory modeling (PM) platform, Fora.ai, to build on existing strengths of DT and overcome the most prevalent limitations of data-driven technologies. This platform (i.e., a set of visualization and simulation tools and facilitation and sense-making approaches) is organized around the iterative steps in PM: problem definition and goal setting, preference elicitation, collaborative scenario-building, simulation, tradeoff deliberation, and solution-building. We demonstrate the platform’s effectiveness when set within a stakeholder-led process that integrates diverse knowledge, data sources, and values in pursuit of equitable green infrastructure (GI) planning to address flooding. The immediate visualization of simulated impacts, followed by reflection on causal and spatial relationships and tradeoffs across diverse priorities, enhanced participants’ collective understanding of how GI interacts with the built environment and physical conditions to inform their intervention scenarios. The facilitated use of Fora.ai enabled a collaborative socio-technical sense-making process, whereby participants transitioned from untested beliefs to designs that were specifically tailored to the problem in the study area and the diversity of values represented, attending to both localized flooding and neighborhood-level impacts. They also derived generalizable design principles that could be applied elsewhere. We show how the combination of specific facilitation practices and platform features leverage the power of data, computational modeling, and social complexity to contribute to collaborative learning and creative and equitable solution-building for urban sustainability and climate resilience.
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- PAR ID:
- 10572605
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- ISSN:
- 2399-8083
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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