Quality Metrics for Information Visualization. (10th July 2018)

• Record Type: Journal Article
• Authors: Behrisch, M.; Blumenschein, M.; Kim, N. W.; Shao, L.; El‐Assady, M.; Fuchs, J.; Seebacher, D.; Diehl, A.; Brandes, U.; Pfister, H.; Schreck, T.; Weiskopf, D.; Keim, D. A.
• Is Part Of: Computer graphics forum. Volume 37:Number 3(2018)
• Page Start: 625
• Page End: 662
• Publication Date: 2018-07-10
• Subjects: Categories and Subject Descriptors (according to ACM CCS) -- Visualization [Human‐centered computing]: Visualization Techniques—; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13446
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.