平行坐标轴动态排列的地理空间多维数据可视分析
|
摘要
目的平行坐标是经典的多维数据可视化方法,但在用于地理空间多维数据分析时,往往存在空间位置信息缺失和空间关联分析不确定等问题。对此,本文设计了一种有效关联平行坐标和地图的地理空间多维数据可视分析方法。方法根据多维属性信息对地理空间位置进行聚类分析,引入Voronoi图和颜色明暗映射对地理空间各类区域进行显著标识,利用平行坐标呈现地理空间多维属性信息,引入互信息度量地理空间聚类与属性类别的相关性,动态地确定平行坐标轴排列顺序,进一步计算属性轴与地图之间数据线的绑定位置,对数据线的布局进行优化处理,降低地图与平行坐标系间数据线分布的紊乱程度。结果有效集成上述可视化设计及数据分析方法,设计与实现一种基于平行坐标轴动态排列的地理空间多维数据可视化分析系统,提供便捷的用户交互模式,通过2组具有明显地理空间多维属性特征的数据进行测试,验证了本文可视分析方法的有效性和实用性。结论本文提出的可视分析方法和工具可以帮助用户快速分析地理空间多维属性存在的空间分布特征及其关联模式,为地理空间多维数据的探索提供了有效手段。
Objective Geospatial multi-dimensional data are mainly composed of spatial location and attribute information,which can effectively record and describe events and phenomena,such as social and economic development,natural environment changes,and human social activities. As a commonly used method for multi-dimensional data visualization,parallel coordinates do not work well for the visual exploration of geospatial multi-dimensional data because of the lack of spatial information and uncertainty of spatial correlation. Therefore,analysis and understanding of geospatial multidimensional data are highly important in establishing an effective association between spatial locations and multiple attributes. Method In this study,we propose a novel geospatial multi-dimensional data visualization method that uses geographical maps to display spatial locations,visualizes multi-dimensional attributes via parallel coordinates,and associates map and parallel coordinates through data lines. We design a corresponding visual analysis system that allows users to explore and analyze the spatial distribution of geospatial multi-dimensional data and its associated feature patterns interactively on the basis of the initial geospatial multidimensional data,including different spatial locations and their corresponding multi-dimensional attribute information. Spatial areas are classified into different clusters according to multi-dimensional attributes and spatial distance,and Voronoi diagrams and color mappings are designed to represent different clusters visually. The attribute information of the geospatial multi-dimensional data is represented by parallel coordinates,and the data on different attribute axes are clustered and analyzed. Mutual information is used to calculate the correlation between the geospatial clustering and attribute categories dynamically,and the ordering of the parallel coordinate plot is adaptively determined. Then,the map is embedded into the parallel coordinates on the basis of the axis alignment results,and the map view and parallel coordinate systems are effectively correlated through data lines. Furthermore,the binding position of the data line between the attribute axis and the map is dynamically calculated according to the geospatial clustering,and the layout of the data line is optimized to reduce the disorder of the data line distribution between the map and parallel coordinate system. We design and implement a geospatial multi-dimensional data visualization analysis system that integrates the above visual designs and data analysis methods. To demonstrate the validity and practicability of the proposed visual analysis system,a convenient user interaction mode is provided,and two case studies are conducted based on the datasets with multi-dimensional geospatial attributes. GDP data containing 11 attributes and 32 spatial locations are visualized using our visual analysis system. Result Comparison of the geospatial clustering and actual urban development in the map view proves that the proposed geospatial clustering algorithm that comprehensively considers data attribute and spatial location information is useful. By observing the arrangement of parallel axes,we confirm that the dynamic arrangement of parallel axes based on mutual information exhibits a certain rationality. In the second case based on geospatial multi-dimensional data,we explore the spatial distribution of attribute information in a certain spatial cluster. When a user clicks on a geospatial clustering of interest,the system rearranges the parallel coordinate axes. Conclusion By comparing the distributions of attributes of the same geospatial clustering at different times,we find that the proposed method is highly sensitive to data. When the data change slightly,the order of the parallel axis changes,making the map embedded in parallel coordinates match the spatial distribution of multidimensional attribute information well. We invite experts from different fields,such as geography and economics,to use and evaluate the system. The validity and practicability of the geospatial multidimensional data visual analysis system are further verified through one-on-one interviews. A set of case studies and expert feedback shows that the visual analysis methods and tools proposed in this study can help users quickly analyze the spatial distribution characteristics and associated patterns of geospatial multi-dimensional attributes and provide domain experts with an effective means of exploring geospatial multi-dimensional data.
Objective Geospatial multi-dimensional data are mainly composed of spatial location and attribute information,which can effectively record and describe events and phenomena,such as social and economic development,natural environment changes,and human social activities. As a commonly used method for multi-dimensional data visualization,parallel coordinates do not work well for the visual exploration of geospatial multi-dimensional data because of the lack of spatial information and uncertainty of spatial correlation. Therefore,analysis and understanding of geospatial multidimensional data are highly important in establishing an effective association between spatial locations and multiple attributes. Method In this study,we propose a novel geospatial multi-dimensional data visualization method that uses geographical maps to display spatial locations,visualizes multi-dimensional attributes via parallel coordinates,and associates map and parallel coordinates through data lines. We design a corresponding visual analysis system that allows users to explore and analyze the spatial distribution of geospatial multi-dimensional data and its associated feature patterns interactively on the basis of the initial geospatial multidimensional data,including different spatial locations and their corresponding multi-dimensional attribute information. Spatial areas are classified into different clusters according to multi-dimensional attributes and spatial distance,and Voronoi diagrams and color mappings are designed to represent different clusters visually. The attribute information of the geospatial multi-dimensional data is represented by parallel coordinates,and the data on different attribute axes are clustered and analyzed. Mutual information is used to calculate the correlation between the geospatial clustering and attribute categories dynamically,and the ordering of the parallel coordinate plot is adaptively determined. Then,the map is embedded into the parallel coordinates on the basis of the axis alignment results,and the map view and parallel coordinate systems are effectively correlated through data lines. Furthermore,the binding position of the data line between the attribute axis and the map is dynamically calculated according to the geospatial clustering,and the layout of the data line is optimized to reduce the disorder of the data line distribution between the map and parallel coordinate system. We design and implement a geospatial multi-dimensional data visualization analysis system that integrates the above visual designs and data analysis methods. To demonstrate the validity and practicability of the proposed visual analysis system,a convenient user interaction mode is provided,and two case studies are conducted based on the datasets with multi-dimensional geospatial attributes. GDP data containing 11 attributes and 32 spatial locations are visualized using our visual analysis system. Result Comparison of the geospatial clustering and actual urban development in the map view proves that the proposed geospatial clustering algorithm that comprehensively considers data attribute and spatial location information is useful. By observing the arrangement of parallel axes,we confirm that the dynamic arrangement of parallel axes based on mutual information exhibits a certain rationality. In the second case based on geospatial multi-dimensional data,we explore the spatial distribution of attribute information in a certain spatial cluster. When a user clicks on a geospatial clustering of interest,the system rearranges the parallel coordinate axes. Conclusion By comparing the distributions of attributes of the same geospatial clustering at different times,we find that the proposed method is highly sensitive to data. When the data change slightly,the order of the parallel axis changes,making the map embedded in parallel coordinates match the spatial distribution of multidimensional attribute information well. We invite experts from different fields,such as geography and economics,to use and evaluate the system. The validity and practicability of the geospatial multidimensional data visual analysis system are further verified through one-on-one interviews. A set of case studies and expert feedback shows that the visual analysis methods and tools proposed in this study can help users quickly analyze the spatial distribution characteristics and associated patterns of geospatial multi-dimensional attributes and provide domain experts with an effective means of exploring geospatial multi-dimensional data.
引文
[1]Wang M X,Zhang Z H,Yang W F.Space Geographic Data Visualization[M].Beijing:Science Press,2012:55-67.[王明孝,张志华,杨维芳.地理空间数据可视化[M].北京:科学出版社,2012:55-67.]
[2]Zhou Z G,Shi C,Shi L S,et al.A survey on the visual analytics of geospatial data[J].Journal of Computer-Aided Design&Computer Graphics,2018,30(5):747-763.[周志光,石晨,史林松,等.地理空间数据可视分析综述[J].计算机辅助设计与图形学学报,2018,30(5):747-763.][DOI:10.3724/SP.J.1089.2018.17127]
[3]Becker R A,Cleveland W S.Brushing scatterplots[J].Technometrics,1987,29(2):127-142.[DOI:10.2307/1269768]
[4]Tang J,Liu J Z,Zhang M,et al.Visualizing large-scale and high-dimensional data[C]//Proceedings of the 25th International Conference on World Wide Web.Montréal,Québec,Canada:International World Wide Web Conferences Steering Committee,2016:287-297.[DOI:10.1145/2872427.2883041]
[5]Heinrich J,Weiskopf D.State of the Art of Parallel Coordinates[R].Girona,Spain:The Eurographics Association,2013:95-116.
[6]Matejka J,Anderson F,Fitzmaurice G.Dynamic opacity optimization for scatter plots[C]//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems.Seoul,Republic of Korea:ACM,2015:2707-2710.[DOI:10.1145/2702123.2702585]
[7]Goodwin S,Dykes J,Slingsby A,et al.Visualizing multiple variables across scale and geography[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):599-608.[DOI:10.1109/TVCG.2015.2467199]
[8]Jolliffe I T.Principal Component Analysis[M].New York,NY:Springer,2002:513.
[9]Kruskal J B,Wish M,Uslaner E M.Multidimensional scaling[M]//MULTIDIMENSIONAL SCALING.BOOK ON DEMANDPOD,19788,45(2):875-878.
[10]Kohonen T.Self-organized formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.[DOI:10.1007/BF00337288]
[11]Turkay C,Lundervold A,Lundervold A J,et al.Representative factor generation for the interactive visual analysis of high-dimensional data[J].IEEE Transactions on Visualization and Computer Graphics,2012,18(12):2621-2630.[DOI:10.1109/TVCG.2012.256]
[12]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,2013,19(12):2625-2633.[DOI:10.1109/TVCG.2013.150]
[13]Sacha D,Kraus M,Bernard J,et al.SOMFlow:guided exploratory cluster analysis with self-organizing maps and analytic provenance[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(1):120-130.[DOI:10.1109/TVCG.2017.2744805]
[14]Inselberg A.Parallel Coordinates:Visual Multidimensional Geometry and Its Applications[M].New York:Springer,2009:98-101.
[15]Wu W C,Xu J Y,Zeng H P,et al.Tel CoVis:visual exploration of co-occurrence in urban human mobility based on telco data[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):935-944.[DOI:10.1109/TVCG.2015.2467194]
[16]Nguyen H,Rosen P.DSPCP:a data scalable approach for identifying relationships in parallel coordinates[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(3):1301-1315.[DOI:10.1109/TVCG.2017.2661309]
[17]Wang J P,Liu X T,Shen H W,et al.Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(1):81-90.[DOI:10.1109/TVCG.2016.2598830]
[18]Qin H X,Wei X S.A study on edge bundling technology in parallel coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(7):1235-1244.[秦红星,卫学仕.平行坐标中的边捆绑算法[J].计算机辅助设计与图形学学报,2017,29(7):1235-1244.]
[19]Wu B,Cao W Q.A force-directed skeleton-based bundling with clustering in parallel coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(10):1807-1815.[巫滨,曹卫群.平行坐标系聚类数据的力导向分段骨骼绑定绘制[J].计算机辅助设计与图形学学报,2017,29(10):1807-1815.][DOI:10.3969/j.issn.1003-9775.2017.10.006]
[20]Wegman E J.Hyperdimensional data analysis using parallel coordinates[J].Journal of the American Statistical Association,1990,85(411):664-675.[DOI:10.2307/2290001]
[21]Qu H M,Chan W Y,Xu A,et al.Visual analysis of the air pollution problem in Hong Kong[J].IEEE Transactions on Visualization and Computer Graphics,2007,13(6):1408-1415.[DOI:10.1109/TVCG.2007.70523]
[22]Zhang Z Y,Mc Donnell K T,Mueller K.A network-based interface for the exploration of high-dimensional data spaces[C]//IEEE Pacific Visualization Symposium.Songdo,South Korea:IEEE,2012:17-24.[DOI:10.1109/Pacific Vis.2012.6183569]
[23]Zhen L L,Li M Q,Cheng R,et al.Adjusting Parallel Coordinates for Investigating Multi-objective Search[M]//Shi Y H,Tan K C,Zhang M J,et al.Simulated Evolution and Learning.Cham:Springer,2017:224-235.[DOI:10.1007/978-3-319-68759-9_19]
[24]Zhou Z G,Ye Z F,Yu J J,et al.Cluster-aware arrangement of the parallel coordinate plots[J].Journal of Visual Languages&Computing,2018,46:43-52.[DOI:10.1016/j.jvlc.2017.10.003]
[25]Chen W,Zhu B,Zhang H X.BN-Mapping:visual analysis of geospatial data with bayesian network[J].Chinese Journal of Computers,2016,39(7):1281-1293.[陈为,朱标,张宏鑫.BN-Mapping:基于贝叶斯网络的地理空间数据可视分析[J].计算机学报,2016,39(7):1281-1293.][DOI:10.11897/SP.J.1016.2016.01281]
[26]Turkay C,Slingsby A,Hauser H,et al.Attribute signatures:dynamic visual summaries for analyzing multivariate geographical data[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):2033-2042.[DOI:10.1109/TVCG.2014.2346265]
[27]Lu M,Wang Z C,Liang J,et al.OD-wheel:visual design to explore OD patterns of a central region[C]//2015 IEEE Pacific Visualization Symposium.Hangzhou,China:IEEE,2015:87-91.[DOI:10.1109/PACIFICVIS.2015.7156361]
[28]Chen Y,Lin X L,Zhao Y F,et al.SunMap:an associated hierarchical data visualization method based on Heatmap and sunburst[J].Journal of Computer-Aided Design&Computer Graphics,2016,28(7):1075-1083.[陈谊,林晓蕾,赵云芳,等.SunMap:一种基于热图和放射环的关联层次数据可视化方法[J].计算机辅助设计与图形学学报,2016,28(7):1075-1083.][DOI:10.3969/j.issn.1003-9775.2016.07.006]
[29]Yang Y L,Dwyer T,Goodwin S,et al.Many-to-many geographically-embedded flow visualisation:an evaluation[J].IEEETransactions on Visualization and Computer Graphics,2017,23(1):411-420.[DOI:10.1109/TVCG.2016.2598885]
[30]Li G Q,Deng M,Cheng T,et al.A dual distance based spatial clustering method[J].Acta Geodaetica et Cartographica Sinica,2008,37(4):482-488.[李光强,邓敏,程涛,等.一种基于双重距离的空间聚类方法[J].测绘学报,2008,37(4):482-488.][DOI:10.3321/j.issn:1001-1595.2008.04.014]
[2]Zhou Z G,Shi C,Shi L S,et al.A survey on the visual analytics of geospatial data[J].Journal of Computer-Aided Design&Computer Graphics,2018,30(5):747-763.[周志光,石晨,史林松,等.地理空间数据可视分析综述[J].计算机辅助设计与图形学学报,2018,30(5):747-763.][DOI:10.3724/SP.J.1089.2018.17127]
[3]Becker R A,Cleveland W S.Brushing scatterplots[J].Technometrics,1987,29(2):127-142.[DOI:10.2307/1269768]
[4]Tang J,Liu J Z,Zhang M,et al.Visualizing large-scale and high-dimensional data[C]//Proceedings of the 25th International Conference on World Wide Web.Montréal,Québec,Canada:International World Wide Web Conferences Steering Committee,2016:287-297.[DOI:10.1145/2872427.2883041]
[5]Heinrich J,Weiskopf D.State of the Art of Parallel Coordinates[R].Girona,Spain:The Eurographics Association,2013:95-116.
[6]Matejka J,Anderson F,Fitzmaurice G.Dynamic opacity optimization for scatter plots[C]//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems.Seoul,Republic of Korea:ACM,2015:2707-2710.[DOI:10.1145/2702123.2702585]
[7]Goodwin S,Dykes J,Slingsby A,et al.Visualizing multiple variables across scale and geography[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):599-608.[DOI:10.1109/TVCG.2015.2467199]
[8]Jolliffe I T.Principal Component Analysis[M].New York,NY:Springer,2002:513.
[9]Kruskal J B,Wish M,Uslaner E M.Multidimensional scaling[M]//MULTIDIMENSIONAL SCALING.BOOK ON DEMANDPOD,19788,45(2):875-878.
[10]Kohonen T.Self-organized formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.[DOI:10.1007/BF00337288]
[11]Turkay C,Lundervold A,Lundervold A J,et al.Representative factor generation for the interactive visual analysis of high-dimensional data[J].IEEE Transactions on Visualization and Computer Graphics,2012,18(12):2621-2630.[DOI:10.1109/TVCG.2012.256]
[12]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,2013,19(12):2625-2633.[DOI:10.1109/TVCG.2013.150]
[13]Sacha D,Kraus M,Bernard J,et al.SOMFlow:guided exploratory cluster analysis with self-organizing maps and analytic provenance[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(1):120-130.[DOI:10.1109/TVCG.2017.2744805]
[14]Inselberg A.Parallel Coordinates:Visual Multidimensional Geometry and Its Applications[M].New York:Springer,2009:98-101.
[15]Wu W C,Xu J Y,Zeng H P,et al.Tel CoVis:visual exploration of co-occurrence in urban human mobility based on telco data[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):935-944.[DOI:10.1109/TVCG.2015.2467194]
[16]Nguyen H,Rosen P.DSPCP:a data scalable approach for identifying relationships in parallel coordinates[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(3):1301-1315.[DOI:10.1109/TVCG.2017.2661309]
[17]Wang J P,Liu X T,Shen H W,et al.Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(1):81-90.[DOI:10.1109/TVCG.2016.2598830]
[18]Qin H X,Wei X S.A study on edge bundling technology in parallel coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(7):1235-1244.[秦红星,卫学仕.平行坐标中的边捆绑算法[J].计算机辅助设计与图形学学报,2017,29(7):1235-1244.]
[19]Wu B,Cao W Q.A force-directed skeleton-based bundling with clustering in parallel coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(10):1807-1815.[巫滨,曹卫群.平行坐标系聚类数据的力导向分段骨骼绑定绘制[J].计算机辅助设计与图形学学报,2017,29(10):1807-1815.][DOI:10.3969/j.issn.1003-9775.2017.10.006]
[20]Wegman E J.Hyperdimensional data analysis using parallel coordinates[J].Journal of the American Statistical Association,1990,85(411):664-675.[DOI:10.2307/2290001]
[21]Qu H M,Chan W Y,Xu A,et al.Visual analysis of the air pollution problem in Hong Kong[J].IEEE Transactions on Visualization and Computer Graphics,2007,13(6):1408-1415.[DOI:10.1109/TVCG.2007.70523]
[22]Zhang Z Y,Mc Donnell K T,Mueller K.A network-based interface for the exploration of high-dimensional data spaces[C]//IEEE Pacific Visualization Symposium.Songdo,South Korea:IEEE,2012:17-24.[DOI:10.1109/Pacific Vis.2012.6183569]
[23]Zhen L L,Li M Q,Cheng R,et al.Adjusting Parallel Coordinates for Investigating Multi-objective Search[M]//Shi Y H,Tan K C,Zhang M J,et al.Simulated Evolution and Learning.Cham:Springer,2017:224-235.[DOI:10.1007/978-3-319-68759-9_19]
[24]Zhou Z G,Ye Z F,Yu J J,et al.Cluster-aware arrangement of the parallel coordinate plots[J].Journal of Visual Languages&Computing,2018,46:43-52.[DOI:10.1016/j.jvlc.2017.10.003]
[25]Chen W,Zhu B,Zhang H X.BN-Mapping:visual analysis of geospatial data with bayesian network[J].Chinese Journal of Computers,2016,39(7):1281-1293.[陈为,朱标,张宏鑫.BN-Mapping:基于贝叶斯网络的地理空间数据可视分析[J].计算机学报,2016,39(7):1281-1293.][DOI:10.11897/SP.J.1016.2016.01281]
[26]Turkay C,Slingsby A,Hauser H,et al.Attribute signatures:dynamic visual summaries for analyzing multivariate geographical data[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):2033-2042.[DOI:10.1109/TVCG.2014.2346265]
[27]Lu M,Wang Z C,Liang J,et al.OD-wheel:visual design to explore OD patterns of a central region[C]//2015 IEEE Pacific Visualization Symposium.Hangzhou,China:IEEE,2015:87-91.[DOI:10.1109/PACIFICVIS.2015.7156361]
[28]Chen Y,Lin X L,Zhao Y F,et al.SunMap:an associated hierarchical data visualization method based on Heatmap and sunburst[J].Journal of Computer-Aided Design&Computer Graphics,2016,28(7):1075-1083.[陈谊,林晓蕾,赵云芳,等.SunMap:一种基于热图和放射环的关联层次数据可视化方法[J].计算机辅助设计与图形学学报,2016,28(7):1075-1083.][DOI:10.3969/j.issn.1003-9775.2016.07.006]
[29]Yang Y L,Dwyer T,Goodwin S,et al.Many-to-many geographically-embedded flow visualisation:an evaluation[J].IEEETransactions on Visualization and Computer Graphics,2017,23(1):411-420.[DOI:10.1109/TVCG.2016.2598885]
[30]Li G Q,Deng M,Cheng T,et al.A dual distance based spatial clustering method[J].Acta Geodaetica et Cartographica Sinica,2008,37(4):482-488.[李光强,邓敏,程涛,等.一种基于双重距离的空间聚类方法[J].测绘学报,2008,37(4):482-488.][DOI:10.3321/j.issn:1001-1595.2008.04.014]
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |