Hierarchical visualization of geographical areal data with spatial attribute association

Wang, Haoxuan; Ni, Yuna; Sun, Ling; Chen, Yuanyuan; Xu, Ting; Chen, Xiaohui; Su, Weihua; Zhou, Zhiguang

doi:10.1016/j.visinf.2021.09.001

Cited by 11 publications

(1 citation statement)

References 42 publications

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“…Andrienko et al [5] aggregated data to identify spatial similarities in trajectories and abstracted spatio-temporal features to delve into trajectory flows. Wang et al [28] introduced a multi-scale geographical area data visualization method predicated on spatial attribute associations, enhancing visual perception of both global and detailed features. While these methodologies provide visual optimization at the trajectory point and line levels, investing considerable effort in visually exploring trajectory features, they fall short in contrasting spatial trajectory topic information and topic features between temporal slices.…”

Section: Visual Analysis Of Trajectory Topicmentioning

confidence: 99%

Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process

Chen,

Wu,

Tang

et al. 2024

Electronics

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The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.

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Section: Visual Analysis Of Trajectory Topicmentioning

confidence: 99%