Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations
2017 (English)In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 44, no 1, p. 1-21Article, review/survey (Refereed) Published
Abstract [en]
For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making.
Place, publisher, year, edition, pages
Taylor & Francis , 2017. Vol. 44, no 1, p. 1-21
Keywords [en]
Uncertainty visualization, literature review, evaluation, user studies, decision-making
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-11557DOI: 10.1080/15230406.2015.1089792ISI: 000388603400001Scopus ID: 2-s2.0-84941686201OAI: oai:DiVA.org:his-11557DiVA, id: diva2:856735
Funder
Knowledge Foundation2015-09-252015-09-252019-11-25Bibliographically approved