Illustrative visualization of time-varying features in spatio-temporal data. (October 2018)

• Record Type: Journal Article
• Authors: Wu, Xiangyang; Chen, Zixi; Gu, Yuhui; Chen, Weiru; Fang, Mei-e
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 157
• Page End: 168
• Publication Date: 2018-10
• Subjects: Illustrative visualization -- Feature enhancement -- Spatio-temporal data; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.08.010
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Identifying and analyzing the time-varying features is important for understanding the spatio-temporal datasets. While there are numerous studies on illustrative visualization, existing solutions can hardly show subtle variations in a temporal dataset. This paper introduces a novel illustrative visualization scheme that employs temporal filtering techniques to disclose desired tiny features, which are further enhanced by an adaptive temporal illustration technique. The unconcerned context can be suppressed in a similar fashion. We develop a visual exploration system that empowers users to interactively manipulate and analyze temporal features. The experimental results on a mobile calling data demonstrate the effectivity and usefulness of our method.