Survey of the intersection of mental maps and visualization.

IF 6.7 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jie Guo, Huiyuan Shi, Yucheng Kong, Jiazhen Du, Zeyang Liu, Fangfang Zhou, Ying Zhao
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引用次数: 0

Abstract

The intersection between mental maps and visualization has attracted increasing research attention in recent years, as they exhibit a natural functional complementarity. Mental maps research explores how humans construct in-mind representations of the external world through the view of cognitive science, while visualization can transform implicit mental maps into concrete visual forms, thereby facilitating the analysis of their construction mechanisms. Visualization, a branch of computer science, investigates how to design human-friendly visual representations of digital data. Mental maps can provide human-perspective insights to guide visualization designs and improve the effectiveness of data understanding and analysis. This paper presents a literature survey of the recent research at this intersection. Regarding the visualizations of mental maps, the present study classifies relevant research into visualizations to depict structural topographies, attribute information in mental maps, and compare multiple mental maps. In terms of mental maps for visualization, this study categorizes the relevant research into the element structuring, structural mapping, and consistency maintenance mechanisms of mental maps to guide visualization designs. This paper introduces and discusses in detail representative works in these categories, providing future research opportunities at this intersection.

心理地图与可视化交叉的调查。
近年来,心理地图与可视化的交叉研究越来越受到关注,因为它们在功能上具有天然的互补性。心理地图研究通过认知科学的观点探讨人类如何构建外部世界的心理表征,而可视化可以将内隐的心理地图转化为具体的视觉形式,从而便于分析其构建机制。可视化是计算机科学的一个分支,研究如何设计对人类友好的数字数据的可视化表示。心理地图可以提供人类视角的见解来指导可视化设计,并提高数据理解和分析的有效性。本文对这一交叉领域的最新研究进行了文献综述。对于心理地图的可视化,本研究将相关研究分为描绘心理地图中的结构地形、属性信息的可视化,以及多个心理地图的比较可视化。在可视化心理图方面,本研究将相关研究分为心理图的元素构造、结构映射和一致性维护机制,以指导可视化设计。本文详细介绍和讨论了这些范畴的代表性作品,为今后在这一交叉领域的研究提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
5.60
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0.00%
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