Domains of uncertainty visualization research: a visual summary approach. Issue 4 (4th July 2017)

• Record Type: Journal Article
• Authors: Smith Mason, Jennifer; Retchless, David; Klippel, Alexander
• Is Part Of: Cartography and geographic information science. Volume 44:Issue 4(2017)
• Page Start: 296
• Page End: 309
• Publication Date: 2017-07-04
• Subjects: Classification -- uncertainty -- visualization -- visual summary; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2016.1154804
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: The inherent uncertainty of geospatial data has engendered a critical research agenda addressing all facets of uncertainty visualization due to the communicative efficiency of graphical representation. To organize this broad research area, we have reviewed literature on geospatial uncertainty visualization and systematically and iteratively classified research in this field. Upon creating a classification, we developed several visual summaries over time, refining the classification and subsequent graphic as new relevant topics emerged. This visual summary extends current existing approaches to taxonomies by allowing users a quick visual overview of relevant topics in a research area at a glance. For each research paper on uncertainty visualization, this classification can be used to visually represent which domains are covered. In order to ensure that the visual summary approach and the corresponding domains developed in this article can be used reliably, we performed an inter-rater agreement task. The high agreement reveals that the domains in the classification that were identified are intuitive and can lead to objective, reproducible classifications (visual summaries) of research papers. In future research, we plan to refine the visual classification/summary approach by providing guided classification via a web interface to visually classify the entire body of literature on geospatial uncertainty visualization and visually explore any trends in research topics, how they have changed over the years, and identify sparser topics that still need to be addressed.