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金思辰, 陶煜波, 严宇宇, 戴浩然. 基于多维时空数据可视化的传染病模式分析[J]. 计算机辅助设计与图形学学报, 2019, 31(2): 241-255. DOI: 10.3724/SP.J.1089.2019.17653
引用本文: 金思辰, 陶煜波, 严宇宇, 戴浩然. 基于多维时空数据可视化的传染病模式分析[J]. 计算机辅助设计与图形学学报, 2019, 31(2): 241-255. DOI: 10.3724/SP.J.1089.2019.17653
Jin Sichen, Tao Yubo, Yan Yuyu, Dai Haoran. Infectious Disease Patterns Analysis Based on Visualization of Multidimensional Space-Time Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(2): 241-255. DOI: 10.3724/SP.J.1089.2019.17653
Citation: Jin Sichen, Tao Yubo, Yan Yuyu, Dai Haoran. Infectious Disease Patterns Analysis Based on Visualization of Multidimensional Space-Time Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(2): 241-255. DOI: 10.3724/SP.J.1089.2019.17653

基于多维时空数据可视化的传染病模式分析

Infectious Disease Patterns Analysis Based on Visualization of Multidimensional Space-Time Data

  • 摘要: 近年来,诸如非典型性肺炎、甲型H1N1、手足口病等传染病在社会各界引起了广泛关注,传染病的暴发往往具有季节性、空间性和相关性.为此提出了传染病可视分析系统,以直观分析传染病的时空模式,交互挖掘不同疾病、地区之间的关联性和相似性.时间模式通过时序折线图、季节变化堆叠图可视化不同传染病的长期趋势、季节消长规律,同时通过构建时序平行坐标系,交互利用地区分布对比图进行异常值分析.空间模式通过地图分类图以及交叉对比热力图来反应疾病在不同省份的空间分布规律、空间聚类结果以及相似性分析.通过对39种法定传染病的整体、个别趋势以及其中异常值的分析结果,表明所提系统能够综合考虑传染病数据的多维时空特性,可有效帮助用户挖掘传染病传播的时空模式,快速寻找传染病暴发时间节点和空间分布转移事件,从而更好地进行预防、把控和分析.

     

    Abstract: In recent years,the outbreak of infectious diseases,such as SARS(severe acute respiratory syndromes),influenza A(H1N1),and hand-foot-and-mouth disease,has caused a widespread concern in our society.The outbreak of infectious diseases is often seasonal,spatial,and related.To intuitively analyze the spatial-temporal patterns of infectious diseases,interactively mining regional associations and similarities between different diseases,this paper presents a visual analysis system for infectious diseases.The seasonal and annual patterns of infectious diseases are visualized by stack graphs and line charts,and abnormal events can be extracted from a temporal parallel coordinate and regional distribution comparison bars.The spatial distribution patterns of diseases are encoded in the choropleth map to analyze the spatial clustering results and their similarities.According to the analysis of the overall,individual trends and outliers of 39 statutory infectious diseases,it is shown that the system can comprehensively explore the multi-dimensional temporal and spatial characteristics of infectious diseases and effectively help users to discover the implicit temporal and spatial patterns,and thus better prevent,control and analyze the infectious diseases.

     

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