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

   https://doi.org/10.1109/TVCG.2024.3456353  

   Moreira, Gustavo; Hosseini, Maryam; Veiga, Carolina; Alexandre, Lucas; Colaninno, Nicola; de_Oliveira, Daniel; Ferreira, Nivan; Lage, Marcos; Miranda, Fabio (January 2025, IEEE Transactions on Visualization and Computer Graphics)

   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. 

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2. The State of the Art in Visual Analytics for 3D Urban Data

   https://doi.org/10.1111/cgf.15112  

   Miranda, Fabio; Ortner, Thomas; Moreira, Gustavo; Hosseini, Maryam; Vuckovic, Milena; Biljecki, Filip; Silva, Claudio T; Lage, Marcos; Ferreira, Nivan (June 2024, Computer Graphics Forum)

   Urbanization has amplified the importance of three‐dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions, why, what, and how, considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine‐grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions. 

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3. Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

   https://doi.org/10.1145/3313831.3376399  

   Miranda, Fabio; Hosseini, Maryam; Lage, Marcos; Doraiswamy, Harish; Dove, Graham; Silva, Cláudio T. (April 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic, a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Work- ing in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods, and temporal analysis of unfolding urban development. 

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4. Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics

   Coscia, A; Chau, D H: (January 2020, Workshop on Trust and Expertise in Visual Analytics (TREX))

   null (Ed.)

   Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems. 

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5. GesturAR: An Authoring System for Creating Freehand Interactive Augmented Reality Applications

   https://doi.org/10.1145/3472749.3474769  

   Wang, Tianyi; Qian, Xun; He, Fengming; Hu, Xiyun; Cao, Yuanzhi; Ramani, Karthik (October 2021, The 34th Annual ACM Symposium on User Interface Software and Technology)

   Freehand gesture is an essential input modality for modern Augmented Reality (AR) user experiences. However, developing AR applications with customized hand interactions remains a challenge for end-users. Therefore, we propose GesturAR, an end-to-end authoring tool that supports users to create in-situ freehand AR applications through embodied demonstration and visual programming. During authoring, users can intuitively demonstrate the customized gesture inputs while referring to the spatial and temporal context. Based on the taxonomy of gestures in AR, we proposed a hand interaction model which maps the gesture inputs to the reactions of the AR contents. Thus, users can author comprehensive freehand applications using trigger-action visual programming and instantly experience the results in AR. Further, we demonstrate multiple application scenarios enabled by GesturAR, such as interactive virtual objects, robots, and avatars, room-level interactive AR spaces, embodied AR presentations, etc. Finally, we evaluate the performance and usability of GesturAR through a user study. 

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