Literature DB >> 31478857

Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff.

Jagoda Walny, Christian Frisson, Mieka West, Doris Kosminsky, Soren Knudsen, Sheelagh Carpendale, Wesley Willett.   

Abstract

Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.

Entities:  

Year:  2019        PMID: 31478857     DOI: 10.1109/TVCG.2019.2934538

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  2 in total

1.  FAIR and Interactive Data Graphics from a Scientific Knowledge Graph.

Authors:  Michael E Deagen; Jamie P McCusker; Tolulomo Fateye; Samuel Stouffer; L Cate Brinson; Deborah L McGuinness; Linda S Schadler
Journal:  Sci Data       Date:  2022-05-27       Impact factor: 8.501

2.  Political affiliation moderates subjective interpretations of COVID-19 graphs.

Authors:  Jonathan D Ericson; William S Albert; Ja-Nae Duane
Journal:  Big Data Soc       Date:  2022-03-04
  2 in total

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