Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics
<|reference_start|>Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics: Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke applications that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at https://urbantk.org/curio.<|reference_end|>
@article{moreira2024curio:,
    title={Curio: A Dataflow-Based Framework for Collaborative Urban Visual
  Analytics},
    author={Gustavo Moreira, Maryam Hosseini, Carolina Veiga, Lucas Alexandre,
  Nicola Colaninno, Daniel de Oliveira, Nivan Ferreira, Marcos Lage, Fabio
  Miranda},
    journal={arXiv preprint arXiv:2408.06139},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06139},
    primaryClass={cs.HC cs.CY}
}