Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics
Gustavo Moreira, Maryam Hosseini, Carolina Veiga, Lucas Alexandre,, Nicola Colaninno, Daniel de Oliveira, Nivan Ferreira, Marcos Lage, Fabio, Miranda

TL;DR
Curio is a flexible dataflow framework that enhances collaborative urban visual analytics by integrating data preprocessing, visualization, and provenance tracking across diverse urban domains.
Contribution
It introduces a multi-level dataflow model enabling collaboration and interoperability in urban visual analytics tools, addressing limitations of existing siloed approaches.
Findings
Demonstrated flexibility across urban domains like accessibility and microclimate.
Enabled collaboration between urban experts and data scientists.
Facilitated tracking of data and visualization provenance.
Abstract
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…
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Taxonomy
TopicsData Visualization and Analytics
