TY - GEN
T1 - Empirical analysis of the subjective impressions and objective measures of domain scientists' visual analytic judgments
AU - Dasgupta, Aritra
AU - Burrows, Susannah
AU - Han, Kyungsik
AU - Rasch, Philip J.
N1 - Funding Information:
We thank Daniel Tompkins for his assistance in the metrics calculations and plotting. This research was funded by the Laboratory Directed Research and Development Program (LDRD) at the Pacific Northwest National Laboratory, which is operated by Batelle for the U.S. Department of Energy (DOE) under contract DE-AC05-76RLO01830.
Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists' subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of: i) relationships among scientists' familiarity, their perceived levels of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.
AB - Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists' subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of: i) relationships among scientists' familiarity, their perceived levels of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.
KW - Climate
KW - Familiarity
KW - Information visualization
KW - Preference
KW - Slope plot
KW - Taylor plot
KW - Trust
KW - Visual comparison
UR - http://www.scopus.com/inward/record.url?scp=85044853652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044853652&partnerID=8YFLogxK
U2 - 10.1145/3025453.3025882
DO - 10.1145/3025453.3025882
M3 - Conference contribution
AN - SCOPUS:85044853652
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 1193
EP - 1204
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Y2 - 6 May 2017 through 11 May 2017
ER -