Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

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 systems 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 urbantk.org/curio.

Authors

Gustavo Moreira, University of Illinois, USA

Maryam Hosseini, University of California, Berkeley, and the Massachusetts Institute of Technology, USA

Carolina Veiga, University of Illinois Urbana-Champaign, USA

Lucas Alexandre, Universidade Federal Fluminense, Brazil

Nicola Colaninno, Politecnico di Milano, Italy

Daniel de Oliveira, Universidade Federal Fluminense, Brazil

Nivan Ferreira, Universidade Federal de Pernambuco, Brazil

Marcos Lage, Universidade Federal Fluminense, Brazil

Fabio Miranda, University of Illinois, USA