基于多维时空数据可视化的传染病模式分析
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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.
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.
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.
引文
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[3]Keim D A,Hao M C,Dayal U.Hierarchical pixel bar charts[J].IEEE Transactions on Visualization and Computer Graphics,2002,8(3):255-269
[4]Zhou L,Weiskopf D.Indexed-points parallel coordinates visualization of multivariate correlations[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(6):1997-2010
[5]Jolliffe I.International encyclopedia of statistical science[M].Heidelberg:Springer,2011:1094-1096
[6]Borg I,Groenen P.Modern multidimensional scaling:theory and applications[J].Journal of Educational Measurement,2003,40(3):277-280
[7]Yuan X R,Ren D H,Wang Z C,et al.Dimension projection matrix/tree:Interactive subspace visual exploration and analysis of high dimensional data[J].IEEE Transactions on Visualization and Computer Graphics,2013(12):2625-2633
[8]Cao N,Lin Y R,Gotz D,et al.Z-Glyph:Visualizing outliers in multivariate data[J].Information Visualization,2018,17(1):22-40
[9]Liao H S,Wu Y C,Chen L,et al.Cluster-based visual abstraction for multivariate scatterplots[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(9):2531-2545
[10]Kwan M P.Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems:a methodological exploration with a large data set[J].Transportation Research Part C:Emerging Technologies,2000,8(1-6):185-203
[11]Guo D S,Chen J,MacEachren A M,et al.A visualization system for space-time and multivariate patterns(vis-stamp)[J].IEEE Transactions on Visualization and Computer Graphics,2006,12(6):1461-1474
[12]Zhou Zhiguang,Sun Chang,Le Dandan,et al.Collaborative visual analytics of multi-dimensional and spatio-temporal data[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(12):2245-2255(in Chinese)(周志光,孙畅,乐丹丹,等.多维时空数据协同可视分析方法[J].计算机辅助设计与图形学学报,2017,29(12):2245-2255)
[13]Plaisant C,Mushlin R,Snyder A,et al.The craft of information visualization[M].San Francisco:Morgan Kaufmann,2003:308-312
[14]Rind A,Aigner W,Miksch S,et al.Visual exploration of time-oriented patient data for chronic diseases:Design study and evaluation[C]//Proceedings of Symposium of the Austrian HCI and Usability Engineering Group.Heidelberg:Springer,2011:301-320
[15]Fails J A,Karlson A,Shahamat L,et al.A visual interface for multivariate temporal data:finding patterns of events across multiple histories[C]//Proceedings of the IEEE Symposium on Visual Analytics Science And Technology.Los Alamitos:IEEEComputer Society Press,2006:167-174
[16]Wang T D,Plaisant C,Shneiderman B,et al.Temporal summaries:Supporting temporal categorical searching,aggregation and comparison[J].IEEE Transactions on Visualization and Computer Graphics,2009,15(6):1049-1056
[17]Wongsuphasawat K,Guerra Gómez J A,Plaisant C,et al.LifeFlow:visualizing an overview of event sequences[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.New York:ACM Press,2011:1747-1756
[18]Qu Yubing,Li Rendong,Zhuang Dafang,et al.Application of space information technology and big data technology in dengue fever risk assessment system[J].Chinese Journal of Disease Control&Prevention,2017,21(11):1165-1169+1174(in Chinese)(曲玉冰,李仁东,庄大方,等.空间信息技术和大数据技术在登革热风险评估系统中的应用[J].中华疾病控制杂志,2017,21(11):1165-1169+1174)
[19]van der Maaten L,Hinton G.Visualizing data using t-SNE[J].Journal of Machine Learning Research,2008,9:2579-2605