系统仿真学报 ›› 2016, Vol. 28 ›› Issue (9): 2035-2041.

• 仿真系统与技术 • 上一篇    下一篇

一种面向层次数据可视化的圆形空间填充算法

侯堃, 李志龙, 冯玉超, 陈谊   

  1. 北京工商大学食品安全大数据技术北京市重点实验室计算机与信息工程学院,北京 100048
  • 收稿日期:2015-05-20 修回日期:2015-07-24 出版日期:2016-09-08 发布日期:2020-08-14
  • 作者简介:侯堃(1985-),男,北京,硕士,实验师,研究方向为信息可视化与可视分析。
  • 基金资助:
    “十二五”国家科技支撑计划(2012BAD29 B01-2),国家科技基础性工作专项(2015FY111200),虚拟现实技术与系统国家重点实验室开放基金(BUAA-VR-14KF-04)

Circular Space-filling Algorithm for Hierarchical Data Visualization

Hou Kun, Li Zhilong, Feng Yuchao, Chen Yi   

  1. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
  • Received:2015-05-20 Revised:2015-07-24 Online:2016-09-08 Published:2020-08-14

摘要: 现有的嵌套圆排列方法主要采用自顶向下的排列方式。排列过程中的多次缩放和平移将导致算法时间复杂度增高、各层节点大小比例不一致以及局部排列不够紧密等问题。为解决上述问题,在总结层次结构中同层兄弟节点圆外切排列算法的基础上,提出了自底向上父子节点的递归排列算法——圆形-矩形中心法CRCA(Circle and Rectangle Center Algorithm),并提出了一种评价父子节点排列紧密性的指标——面积比AR(Area Ratio)。将基于CRCA算法的嵌套圆排列方法应用于各国农药最大残留限量标准数据的可视化中。实验表明,该方法能够保持同层节点的大小比例和更紧密的排列效果,提高空间利用率,在数据展示方面取得良好效果。

关键词: 信息可视化, 层次结构, 嵌套圆, 评价指标

Abstract: The existed Circle Packing methods mainly take the style of top-down to arrange all the nodes. When the nodes of different layer was arranged, the scaling and translation would cause relative high time complexity of the algorithm, and the size proportional discordance of nodes of different father nodes but the same layer, and it also causes the problems like defective tightness between the layouts of father nodes and child nodes. To solve problems above, on the basis of summarizing methods of arranging brother nodes of the same layer, the algorithm——CRCA(Circle and Rectangle Center Algorithm) was proposed, which took the style of down-top. And an indicator evaluating the layout tightness between father nodes and child nodes was presented, which was called AR(Area Ratio). The Circle Packing method based on CRCA would be applied to the visual analysis on the data of Maximum Residue Limits (MRLs) of pesticides in China (GB2763-2014). After visualizing the MRLs data, it is proved efficient to help users to compare the data and also significant to get a better knowledge of the MRLs data.

Key words: information visualization, hierarchical structure, circle packing, evaluation indicator

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