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

• URL: http://arxiv.org/abs/2408.06139v1
• Date: Mon, 12 Aug 2024 13:31:40 GMT
• Title: Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics
• Authors: Gustavo Moreira, Maryam Hosseini, Carolina Veiga, Lucas Alexandre, Nicola Colaninno, Daniel de Oliveira, Nivan Ferreira, Marcos Lage, Fabio Miranda,
• Abstract summary: We present Curio, a framework for collaborative urban visual analytics. The framework allows experts to intertwine data preprocessing, management, and visualization stages. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access.
• Score: 4.719274729264413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode [1.0937094979510213]
  Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. We propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization.
  arXiv  Detail & Related papers  (2024-09-29T03:00:16Z)
• The State of the Art in Visual Analytics for 3D Urban Data [5.056350278679641]
  Urbanization has amplified the importance of three-dimensional structures in urban environments. 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. incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities.
  arXiv  Detail & Related papers  (2024-04-24T16:50:42Z)
• MouSi: Poly-Visual-Expert Vision-Language Models [132.58949014605477]
  This paper proposes the use of ensemble experts technique to synergize the capabilities of individual visual encoders. This technique introduces a fusion network to unify the processing of outputs from different visual experts. In our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1.
  arXiv  Detail & Related papers  (2024-01-30T18:09:11Z)
• Charting a Path to Efficient Onboarding: The Role of Software Visualization [49.1574468325115]
  The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
  arXiv  Detail & Related papers  (2024-01-17T21:30:45Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics [5.674216760436341]
  The complex nature of urban issues and the overwhelming amount of available data have posed significant challenges in translating these efforts into actionable insights. When analyzing a feature of interest, an urban expert must transform, integrate, and visualize different thematic (e.g., sunlight access, demographic) and physical (e.g., buildings, street networks) data layers. This makes the entire visual data exploration and system implementation difficult for programmers and also sets a high entry barrier for urban experts outside of computer science.
  arXiv  Detail & Related papers  (2023-08-15T13:43:04Z)
• Generative methods for Urban design and rapid solution space exploration [13.222198221605701]
  This research introduces an implementation of a tensor-field-based generative urban modeling toolkit. Our method encodes contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs.
  arXiv  Detail & Related papers  (2022-12-13T17:58:02Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics [7.157706457130007]
  This paper investigates how effective 3D urban visual analytics are at supportingtemporal analysis on building surfaces. We compare four representative visual designs used to visualize 3Dtemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Our results demonstrate that participants were more accurate using plot-based visualizations but faster using colorcoded visualizations.
  arXiv  Detail & Related papers  (2022-08-10T14:38:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.