Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 25, Issue 1, Pages 459-469Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2018.2865126
Keywords
Spatial ranking; visualization
Categories
Funding
- National Key R&D Program of China [2018YFB1004300]
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1609217]
- NSFC [61761136020, 61502416]
- Zhejiang Provincial Natural Science Foundation [LR18F020001]
- 100 Talents Program of Zhejiang University
- Alibaba-Zhejiang University Joint Institute of Frontier Technologies
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Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.
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