The influence of domain expertise on visual overviews of spatiotemporal data. Issue 2 (3rd July 2017)

• Record Type: Journal Article
• Authors: Bleisch, Susanne; Duckham, Matt; Pettit, Chris
• Is Part Of: International journal of cartography. Volume 3:Issue 2(2017)
• Page Start: 166
• Page End: 186
• Publication Date: 2017-07-03
• Subjects: Visual overviews -- exploratory data analysis -- evaluation -- usefulness -- domain knowledge -- spatiotemporal data -- movement analysis; Cartography -- Periodicals; 526.05
• DOI: 10.1080/23729333.2017.1294820
• Languages: English
• ISSNs: 2372-9333

Abstract

ABSTRACT: Overviews of spatiotemporal data are acknowledged to play an important role in visualization in initiating and supporting geovisualization and exploratory data analysis (EDA). However, relatively little research has focused on the visual overviews themselves, and their potential impacts on EDA outcomes. In a user study, we evaluated the influence of different levels of domain knowledge on the usefulness of four distinct types of static visual overview of spatiotemporal data. Beyond simply orienting users, our results indicate that visual overviews can be important in gaining insights into a data set, for example, in learning about metadata. Although subjects without domain knowledge struggled to judge the quality of their findings, they were as successful at identifying interesting patterns in the data as those with domain expertise. Our results suggest that detailed background knowledge of a data set can actively hinder EDA. Being already familiar with their own data sets, our results highlight the tendency of data experts to disregard findings that do not match their pre-existing domain knowledge. Based on these findings, our conclusions identify a range of potential avenues for future work, including the use of visual overviews that deliberately do not, from first view, reveal the context of the data they show. This later approach could help in cases where domain experts need to see their data with 'fresh eyes', and detect interesting patterns in spatiotemporal data before relating the findings to specific knowledge about the data sets and the domain.