Skip to main content
SpringerLink
Log in

Enhancing statistical charts: toward better data visualization and analysis

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

Conventional statistical charts are widely used in visual analysis. With the development of digital techniques, statistical charts are confronted with problems when data grow in scale and complexity. Accordingly, a huge amount of effort has been paid on the enhancement of standard charts, making the design space dramatically increased. It is cumbersome for naive users to choose appropriate design in a specific analysis scenario. In this paper, we survey the enhancement techniques for a compact set of statistical charts, and identify the types and usage scenarios. Motivated by the new problems, such as data volume and complexity, we present a challenge-and-task-driven framework to guide the understanding of the design space and the decision-making process.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Al-Dohuki S, Wu Y, Kamw F, Xin L, Xin L, Ye Z, Ye X, Wei C, Chao M, Fei W (2017) Semantictraj: a new approach to interacting with massive taxi trajectories. IEEE Trans Visual Comput Graph 23(1):11–20

    Article  Google Scholar 

  • Alsallakh B, Aigner W, Miksch S, Groller ME (2012) Reinventing the contingency wheel: scalable visual analytics of large categorical data. IEEE Trans Visual Comput Graph 18(12):2849–58

    Article  Google Scholar 

  • Alsallakh B, Hanbury A, Hauser H, Miksch S, Rauber A (2014) Visual methods for analyzing probabilistic classification data. IEEE Trans Visual Comput Graph 20(12):1703–1712

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Mladenov M, Mock M, Pölitz C (Oct 2010) Discovering bits of place histories from people’s activity traces. In: 2010 IEEE symposium on visual analytics science and technology, pp 59–66. https://doi.org/10.1109/VAST.2010.5652478

  • Andrienko G, Andrienko N, Fuchs G, Garcia JMC (2018) Clustering trajectories by relevant parts for air traffic analysis. IEEE Trans Visual Comput Graph 24(1):34–44. https://doi.org/10.1109/TVCG.2017.2744322

    Article  Google Scholar 

  • Aupetit M, Heulot N, Fekete J (Oct 2014) A multidimensional brush for scatterplot data analytics. In: 2014 IEEE conference on visual analytics science and technology (VAST), pp 221–222. https://doi.org/10.1109/VAST.2014.7042500

  • Bachthaler S, Weiskopf D (2008) Continuous scatterplots. IEEE Trans Visual Comput Graph 14(6):1428

    Article  Google Scholar 

  • Bertini E, Santucci G (2006) Give chance a chance: modeling density to enhance scatter plot quality through random data sampling. Inf Visual 5(2):95–110

    Article  Google Scholar 

  • Brown ET, Liu J, Brodley CE, Chang R (Oct 2012) Dis-function: Learning distance functions interactively. In: 2012 IEEE conference on visual analytics science and technology (VAST), pp 83–92. https://doi.org/10.1109/VAST.2012.6400486

  • Chang R, Wessel G, Kosara R, Sauda E, Ribarsky W (2007) Legible cities: focus-dependent multi-resolution visualization of urban relationships. IEEE Trans Visual Comput Graph 13(6):1169–1175

    Article  Google Scholar 

  • Chen H, Chen W, Mei H, Liu Z (2014) Visual abstraction and exploration of multi-class scatterplots. IEEE Trans Visual Comput Graph 20(12):1683–92

    Article  Google Scholar 

  • Chen H, Zhang S, Chen W, Mei H, Zhang J, Mercer A, Liang R, Qu H (2015a) Uncertainty-aware multidimensional ensemble data visualization and exploration. IEEE Trans Visual Comput Graph 21(9):1072–1086

    Article  Google Scholar 

  • Chen W, Guo F, Wang FY (2015b) A survey of traffic data visualization. IEEE Trans Intell Transp Syst 16(6):2970–2984

    Article  Google Scholar 

  • Chen W, Lao T, Xia J, Huang X, Zhu B, Hu W, Guan H (2016) Gameflow: narrative visualization of NBA basketball games. IEEE Trans Multimed 18(11):2247–2256

    Article  Google Scholar 

  • Chen W, Lu J, Kong D, Liu Z, Shen Y, Chen Y, He J, Liu S, Qi Y, Wu Y (2017) Gamelifevis: visual analysis of behavior evolutions in multiplayer online games. J Visual 20(3):1–15

    Article  Google Scholar 

  • Chen W, Huang Z, Wu F, Zhu M, Guan H, Maciejewski R (2018a) Vaud: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans Visual Comput Graph 24(9):2636–2648. https://doi.org/10.1109/TVCG.2017.2758362

    Article  Google Scholar 

  • Chen W, Xia J, Wang X, Wang Y, Chen J, Chang L (2018b) Relationlines: visual reasoning of egocentric relations from heterogeneous urban data. ACM Trans Intell Syst Technol 10(1):2:1–2:21. https://doi.org/10.1145/3200766

    Article  Google Scholar 

  • Chen W, Guo F, Han D, Pan J, Nie X, Xia J, Zhang X (2019) Structure-based suggestive exploration: a new approach for effective exploration of large networks. IEEE Trans Visual Comput Graph 25(1):555–565. https://doi.org/10.1109/TVCG.2018.2865139

    Article  Google Scholar 

  • Cheng S, Cui P, Mueller K (2016) Extending scatterplots to scalar fields. In: IEEE visualization conference (Scivis poster)

  • Cheng S, Mueller K (2016) The data context map: fusing data and attributes into a unified display. IEEE Trans Visual Comput Graph 22(1):121–130

    Article  Google Scholar 

  • Choo J, Lee C, Kim H, Lee H, Liu Z, Kannan R, Stolper CD, Stasko J, Drake BL, Park H (Oct 2014) Visirr: visual analytics for information retrieval and recommendation with large-scale document data. In: 2014 IEEE conference on visual analytics science and technology (VAST), pp 243–244. https://doi.org/10.1109/VAST.2014.7042511

  • Claessen JH, van Wijk JJ (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Visual Comput Graph 17(12):2310

    Article  Google Scholar 

  • Collins C, Penn G, Carpendale S (2009) Bubble sets: revealing set relations with isocontours over existing visualizations. IEEE Trans Visual Comput Graph 15(6):1009–1016

    Article  Google Scholar 

  • Dang TN, Wilkinson L (March 2014) Scagexplorer: exploring scatterplots by their scagnostics. In: 2014 IEEE Pacific visualization symposium, pp 73–80. https://doi.org/10.1109/PacificVis.2014.42

  • Ellis G, Dix A (2006) Enabling automatic clutter reduction in parallel coordinate plots. IEEE Trans Visual Comput Graph 12(5):717–724

    Article  Google Scholar 

  • Fan X, Peng Y, Zhao Y, Li Y, Meng D, Zhong Z, Zhou F, Lu M (2017) A personal visual analytics on smartphone usage data. J Vis Lang Comput 41:111–120. https://doi.org/10.1016/j.jvlc.2017.03.006

    Article  Google Scholar 

  • Feng D, Kwock L, Lee Y, Taylor R (2010) Matching visual saliency to confidence in plots of uncertain data. IEEE Trans Visual Comput Graph 16(6):980

    Article  Google Scholar 

  • Friendly M (2008) The golden age of statistical graphics. Stat Sci 23(4):502–535

    Article  MathSciNet  MATH  Google Scholar 

  • Geng Z, Peng Z, Laramee RS, Roberts JC, Walker R (2011) Angular histograms: frequency-based visualizations for large, high dimensional data. IEEE Trans Visual Comput Graph 17(12):2572–2580

    Article  Google Scholar 

  • Gleicher M, Correll M, Nothelfer C, Franconeri S (2013) Perception of average value in multiclass scatterplots. IEEE Trans Visual Comput Graph 19(12):2316

    Article  Google Scholar 

  • Graham M, Kennedy J (July 2003) Using curves to enhance parallel coordinate visualisations. In: Proceedings on 7th international conference on information visualization, 2003. IV 2003, pp 10–16. https://doi.org/10.1109/IV.2003.1217950

  • Gu T, Zhu M, Chen W, Huang Z, Maciejewski R, Chang L (2018) Structuring mobility transition with an adaptive graph representation. IEEE Trans Comput Soc Syst 5(4):1121–1132. https://doi.org/10.1109/TCSS.2018.2858439

    Article  Google Scholar 

  • Guo Z, Ward MO, Rundensteiner EA, Ruiz C (Oct 2011) Pointwise local pattern exploration for sensitivity analysis. In: 2011 IEEE conference on visual analytics science and technology (VAST), pp 131–140. https://doi.org/10.1109/VAST.2011.6102450

  • Guo F, Gu T, Chen W, Wu F, Wang Q, Shi L, Qu H (2019) Visual exploration of air quality data with a time-correlation-partitioning tree based on information theory. ACM Trans Interact Intell Syst 9(1):4:1–4:23. https://doi.org/10.1145/3182187

    Article  Google Scholar 

  • Hajizadeh AH, Tory M, Leung R (2013) Supporting awareness through collaborative brushing and linking of tabular data. IEEE Trans Visual Comput Graph 19(12):2189

    Article  Google Scholar 

  • Hao MC, Janetzko H, Mittelstädt S, Hill W, Dayal U, Keim DA, Marwah M, Sharma RK (2011) A visual analytics approach for peak-preserving prediction of large seasonal time series. Comput Graph Forum 30(3):691–700

    Article  Google Scholar 

  • Heinrich J, Bachthaler S, Weiskopf D (2011) Progressive splatting of continuous scatterplots and parallel coordinates. In: Eurographics/IEEE—vGTC conference on visualization, pp 653–662

  • Holten D, Van Wijk JJ (2010) Evaluation of cluster identification performance for different pcp variants. Comput Graph Forum 29(3):793–802

    Article  Google Scholar 

  • Huang Z, Lu Y, Mack E, Chen W, Maciejewski R (2019) Exploring the sensitivity of choropleths under attribute uncertainty. IEEE Trans Visual Comput Graph. https://doi.org/10.1109/TVCG.2019.2892483

    Article  Google Scholar 

  • Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91

    Article  MathSciNet  MATH  Google Scholar 

  • Kamw F, Al-Dohuki S, Zhao Y, Eynon T, Sheets D, Yang J, Ye X, Chen W (2019) Urban structure accessibility modeling and visualization for joint spatiotemporal constraints. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2018.2888994

    Article  Google Scholar 

  • Kanjanabose R, Abdul-Rahman A, Chen M (2015) A multi-task comparative study on scatter plots and parallel coordinates plots. In: Eurographics conference on visualization, pp 261–270

  • Keim DA, Hao MC, Dayal U, Janetzko H, Bak P (2010) Generalized scatter plots. Inf Visual 9(4):301–311. https://doi.org/10.1057/ivs.2009.34

    Article  Google Scholar 

  • Kim NW, Schweickart E, Liu Z, Dontcheva M, Li W, Popovic J, Pfister H (2017) Data-driven guides: supporting expressive design for information graphics. IEEE Trans Visual Comput Graph 23(1):491–500. https://doi.org/10.1109/TVCG.2016.2598620

    Article  Google Scholar 

  • Kincaid R (2010) Signallens: Focus+Context applied to electronic time series. IEEE Trans Visual Comput Graph 16(6):900

    Article  Google Scholar 

  • Kosara R, Bendix F, Hauser H (2006) Parallel sets: interactive exploration and visual analysis of categorical data. IEEE Trans Visual Comput Graph 12(4):558–568

    Article  Google Scholar 

  • Kwon BC, Kim H, Wall E, Choo J, Park H, Endert A (2017) Axisketcher: interactive nonlinear axis mapping of visualizations through user drawings. IEEE Trans Visual Comput Graph 23(1):221–230

    Article  Google Scholar 

  • Lampe OD, Hauser H (Mar 2011) Interactive visualization of streaming data with kernel density estimation. In: 2011 IEEE Pacific visualization symposium, pp 171–178. https://doi.org/10.1109/PACIFICVIS.2011.5742387

  • Lehmann DJ, Theisel H (2010) Discontinuities in continuous scatter plots. IEEE Trans Visual Comput Graph 16(6):1291–1300. https://doi.org/10.1109/TVCG.2010.146

    Article  Google Scholar 

  • Li D, Mei H, Shen Y, Su S, Zhang W, Wang J, Zu M, Chen W (2018) Echarts: a declarative framework for rapid construction of web-based visualization. Vis Inf 2(2):136–146

    Google Scholar 

  • Li J, van Wijk JJ, Martens J (April 2009) Evaluation of symbol contrast in scatterplots. In: 2009 IEEE Pacific visualization symposium, pp 97–104. https://doi.org/10.1109/PACIFICVIS.2009.4906843

  • Li J, van Wijk JJ, Martens J (March 2010) A model of symbol lightness discrimination in sparse scatterplots. In: 2010 IEEE Pacific visualization symposium (PacificVis), pp 105–112. https://doi.org/10.1109/PACIFICVIS.2010.5429604

  • Liao H, Wu Y, Chen L, Hamill TM, Wang Y, Dai K, Zhang H, Chen W (Oct 2015) A visual voting framework for weather forecast calibration. In: 2015 IEEE scientific visualization conference (SciVis), pp 25–32. https://doi.org/10.1109/SciVis.2015.7429488

  • Liao H, Wu Y, Chen L, Chen W (2018) Cluster-based visual abstraction for multivariate scatterplots. IEEE Trans Visual Comput Graph 24(9):2531–2545. https://doi.org/10.1109/TVCG.2017.2754480

    Article  Google Scholar 

  • Liu S, Chen Y, Wei H, Yang J, Zhou K, Drucker SM (2015) Exploring topical lead-lag across corpora. TKDE 27(1):115–129

    Google Scholar 

  • Liu M, Shi J, Cao K, Zhu J, Liu S (2018a) Analyzing the training processes of deep generative models. IEEE Trans Visual Comput Graph 24(1):77–87. https://doi.org/10.1109/TVCG.2017.2744938

    Article  Google Scholar 

  • Liu S, Xiao J, Liu J, Wang X, Wu J, Zhu J (2018b) Visual diagnosis of tree boosting methods. IEEE Trans Visual Comput Graph 24(1):163–173. https://doi.org/10.1109/TVCG.2017.2744378

    Article  Google Scholar 

  • Ma Y, Lin T, Cao Z, Li C, Wang F, Chen W (2016) Mobility viewer: an Eulerian approach for studying urban crowd flow. IEEE Trans Intell Transp Syst 17(9):2627–2636

    Article  Google Scholar 

  • Ma Y, Chen W, Ma X, Xu J, Huang X, Maciejewski R, Tung AKH (2017) Easysvm: a visual analysis approach for open-box support vector machines. Comput Vis Media 3(2):1–15

    Google Scholar 

  • Ma Y, Tung AKH, Wang W, Gao X, Pan Z, Chen W (2018) Scatternet: a deep subjective similarity model for visual analysis of scatterplots. IEEE Trans Visual Comput Graph. https://doi.org/10.1109/TVCG.2018.2875702

    Article  Google Scholar 

  • Mayorga A, Gleicher M (2013) Splatterplots: overcoming overdraw in scatter plots. IEEE Trans Visual Comput Graph 19(9):1526–1538

    Article  Google Scholar 

  • Mei H, Ma Y, Wei Y, Chen W (2018) The design space of construction tools for information visualization: a survey. J Vis Lang Comput 44:120–132

    Article  Google Scholar 

  • Meuschke M, Voss S, Beuing O, Preim B, Kai L (2017) Combined visualization of vessel deformation and hemodynamics in cerebral aneurysms. IEEE Trans Visual Comput Graph 23(1):761

    Article  Google Scholar 

  • Muelder C, Zhu B, Chen W, Zhang H, Ma KL (2016) Visual analysis of cloud computing performance using behavioral lines. IEEE Trans Visual Comput Graph 22(6):1694–1704

    Article  Google Scholar 

  • Munzner T (2014) Visualization analysis and design. AK Peters, Natick

    Book  Google Scholar 

  • Pagot C, Osmari D, Sadlo F, Weiskopf D, Ertl T, Comba J (2011) Efficient parallel vectors feature extraction from higher-order data. Comput Graph Forum 30(3):751–760. https://doi.org/10.1111/j.1467-8659.2011.01924.x

    Article  Google Scholar 

  • Palmas G, Bachynskyi M, Oulasvirta A, Seidel HP, Weinkauf T (March 2014) An edge-bundling layout for interactive parallel coordinates. In: 2014 IEEE Pacific visualization symposium, pp 57–64. https://doi.org/10.1109/PacificVis.2014.40

  • Pearson K (1895) Contributions to the mathematical theory of evolution. II. Skew variation in homogeneous material. Philos Trans R Soc A Math Phys Eng Sci 186:343–414

    Article  Google Scholar 

  • Peng W, Ward MO, Rundensteiner EA (2004) Clutter reduction in multi-dimensional data visualization using dimension reordering. In: IEEE Symposium on information visualization, pp 89–96

  • Playfair W, Wainer H, Spence I (2005) The commercial and political atlas and statistical breviary (Original version was published in 1786). Cambridge University Press, Cambridge

    Google Scholar 

  • Ren D, Lee B, Höllerer T (2017) Stardust: accessible and transparent GPU support for information visualization rendering. Comput Graph Forum 36(3):179–188

    Article  Google Scholar 

  • Rodrigues N, Weiskopf D (2018) Nonlinear dot plots. IEEE Trans Visual Comput Graph 24(1):616–625. https://doi.org/10.1109/TVCG.2017.2744018

    Article  Google Scholar 

  • Sarikaya A, Gleicher M (2018) Scatterplots: tasks, data, and designs. IEEE Trans Visual Comput Graph 24(1):402–412

    Article  Google Scholar 

  • Schulz H-J, Nocke T, Heitzler M, Schumann H (2013) A design space of visualization tasks. IEEE Trans Visual Comput Graph 19(12):2366–2375

    Article  Google Scholar 

  • Shi C, Cui W, Liu S, Xu P, Chen W, Qu H (2012) Rankexplorer: visualization of ranking changes in large time series data. IEEE Trans Visual Comput Graph 18(12):2669–2678

    Article  Google Scholar 

  • Shneiderman B (Sep. 1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings 1996 IEEE symposium on visual languages, pp 336–343. https://doi.org/10.1109/VL.1996.545307

  • Staib J, Grottel S, Gumhold S (2016) Enhancing scatterplots with multi-dimensional focal blur. Comput Graph Forum 35(3):11–20

    Article  Google Scholar 

  • Streit M, Gehlenborg N (2014) Bar charts and box plots. Nat Methods 11(2):117

    Article  Google Scholar 

  • Taher F, Jansen Y, Woodruff J, Hardy J, Hornbaek K, Alexander J (2016) Investigating the use of a dynamic physical bar chart for data exploration and presentation. IEEE Trans Visual Comput Graph 23(1):451–460

    Article  Google Scholar 

  • Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  • Unger A, Dräger N, Sips M, Lehmann DJ (2018) Understanding a sequence of sequences: visual exploration of categorical states in lake sediment cores. IEEE Trans Visual Comput Graph 24(1):66–76. https://doi.org/10.1109/TVCG.2017.2744686

    Article  Google Scholar 

  • van den Elzen S, van Wijk JJ (Oct 2011) Baobabview: interactive construction and analysis of decision trees. In: 2011 IEEE conference on visual analytics science and technology (VAST), pp 151–160. https://doi.org/10.1109/VAST.2011.6102453

  • Viau C, McGuffin MJ, Chiricota Y, Jurisica I (2010) The FlowVizMenu and parallel scatterplot matrix: hybrid multidimensional visualizations for network exploration. IEEE Trans Visual Comput Graph 16(6):1100–1108

    Article  Google Scholar 

  • Wan Y, Hansen C (2017) Uncertainty footprint: visualization of nonuniform behavior of iterative algorithms applied to 4D cell tracking. Comput Graph Forum 36(3):479–489

    Article  Google Scholar 

  • Wang F, Chen W, Wu F, Zhao Y, Hong H, Gu T, Wang L, Liang R, Bao H (2014) A visual reasoning approach for data-driven transport assessment on urban roads. In: 2014 IEEE conference on visual analytics science and technology (VAST). IEEE, New York, pp 103–112

  • Wang X, Chou J, Chen W, Guan H, Chen W, Lao T, Ma K (2018a) A utility-aware visual approach for anonymizing multi-attribute tabular data. IEEE Trans Visual Comput Graph 24(1):351–360. https://doi.org/10.1109/TVCG.2017.2745139

    Article  Google Scholar 

  • Wang X, Gu T, Luo X, Cai X, Lao T, Chen W, Wu Y, Yu J, Chen W (2018b) A user study on the capability of three geo-based features in analyzing and locating trajectories. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2018.2875021

    Article  Google Scholar 

  • Wang X, Chen W, Chou J, Bryan C, Guan H, Chen W, Pan R, Ma K (2019) Graphprotector: a visual interface for employing and assessing multiple privacy preserving graph algorithms. IEEE Trans Visual Comput Graph 25(1):193–203. https://doi.org/10.1109/TVCG.2018.2865021

    Article  Google Scholar 

  • Wickham H, Hofmann H (2011) Product plots. IEEE Trans Visual Comput Graph 17(12):2223–2230

    Article  Google Scholar 

  • Wilkinson L (1999) Dot plots. Am Stat 53(3):276–281

    Google Scholar 

  • Wu W, Zheng Y, Qu H, Chen W, Groller E, Ni LM (Oct 2015) Boundaryseer: visual analysis of 2D boundary changes. In: 2014 IEEE conference on visual analytics science and technology (VAST), pp 143–152. https://doi.org/10.1109/VAST.2014.7042490

  • Wu F, Zhu M, Wang Q, Zhao X, Chen W, Maciejewski R (2017) Spatialctemporal visualization of city-wide crowd movement. J Visual 20(2):183–194

    Article  Google Scholar 

  • Wu X, Chen Z, Gu Y, Chen W, Me Fang (2018) Illustrative visualization of time-varying features in spatio-temporal data. J Vis Lang Comput 48:157–168. https://doi.org/10.1016/j.jvlc.2018.08.010

    Article  Google Scholar 

  • Wu Y, Xie X, Wang J, Deng D, Liang H, Zhang H, Cheng S, Chen W (2019) Forvizor: visualizing spatio-temporal team formations in soccer. IEEE Trans Visual Comput Graph 25(1):65–75. https://doi.org/10.1109/TVCG.2018.2865041

    Article  Google Scholar 

  • Xie C, Chen W, Huang X, Hu Y, Barlowe S, Yang J (2014) Vaet: a visual analytics approach for e-transactions time-series. IEEE Trans Visual Comput Graph 20(12):1743–1752. https://doi.org/10.1109/TVCG.2014.2346913

    Article  Google Scholar 

  • Xia J, Jiang G, Zhang Y, Li R, Chen W (2017) Visual subspace clustering based on dimension relevance. J Vis Lang Comput 41:79–88. https://doi.org/10.1016/j.jvlc.2017.05.003

    Article  Google Scholar 

  • Xia J, Gao L, Kong K, Zhao Y, Chen Y, Kui X, Liang Y (2018a) Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets. J Vis Lang Comput 48:52–60. https://doi.org/10.1016/j.jvlc.2018.08.003

    Article  Google Scholar 

  • Xia J, Ye F, Chen W, Wang Y, Chen W, Ma Y, Tung AKH (2018b) LDSScanner: exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Trans Visual Comput Graph 24(1):236–245. https://doi.org/10.1109/TVCG.2017.2744098

    Article  Google Scholar 

  • Yuan X, Guo P, Xiao H, Zhou H, Qu H (2009) Scattering points in parallel coordinates. IEEE Trans Visual Comput Graph 15(6):1001–1008

    Article  Google Scholar 

  • Zhang T, Wang X, Li Z, Guo F, Ma Y, Chen W (2017) A survey of network anomaly visualization. Sci China (Inf Sci) 60(12):121101

    Article  Google Scholar 

  • Zhao J, Chevalier F, Pietriga E, Balakrishnan R (2011) Exploratory analysis of time-series with chronolenses. IEEE Trans Visual Comput Graph 17(12):2422–31

    Article  Google Scholar 

  • Zhao X, Wu Y, Cui W, Du X, Chen Y, Wang Y, Lee DL, Qu H (2018a) Skylens: visual analysis of skyline on multi-dimensional data. IEEE Trans Visual Comput Graph 24(1):246–255

    Article  Google Scholar 

  • Zhao Y, She Y, Chen W, Lu Y, Xia J, Chen W, Liu J, Zhou F (2018b) Eod edge sampling for visualizing dynamic network via massive sequence view. IEEE Access 6:53006–53018. https://doi.org/10.1109/ACCESS.2018.2870684

    Article  Google Scholar 

  • Zhao Y, Luo F, Chen M, Wang Y, Xia J, Zhou F, Wang Y, Chen Y, Chen W (2019) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Visual Comput Graph 25(1):12–21. https://doi.org/10.1109/TVCG.2018.2865020

    Article  Google Scholar 

  • Zhou Z, Li H, Liu F, Liu Y, Huang C, Tao Y, Lin H, Su W (2018a) Visual analytics of economic features for multivariate spatio-temporal GDP data. J Visual 21(2):337–350

    Article  Google Scholar 

  • Zhou Z, Shi C, Hu M, Liu Y (2018b) Visual ranking of academic influence via paper citation. J Vis Lang Comput 48:134–143. https://doi.org/10.1016/j.jvlc.2018.08.007

    Article  Google Scholar 

  • Zhou Z, Ye Z, Yu J, Chen W (2018c) Cluster-aware arrangement of the parallel coordinate plots. J Vis Lang Comput 46:43–52. https://doi.org/10.1016/j.jvlc.2017.10.003

    Article  Google Scholar 

  • Zhou Z, Yu J, Guo Z, Liu Y (2018d) Visual exploration of urban functions via spatio-temporal taxi OD data. J Vis Lang Comput 48:169–177. https://doi.org/10.1016/j.jvlc.2018.08.009

    Article  Google Scholar 

  • Zhou Z, Zhu X, Liu Y, Ren Q, Wang C, Gu T (2018e) Visupi: visual analytics for university personality inventory data. J Visual 21(5):885–901. https://doi.org/10.1007/s12650-018-0499-x

    Article  Google Scholar 

  • Zhou Z, Meng L, Tang C, Zhao Y, Guo Z, Hu M, Chen W (2019) Visual abstraction of large scale geospatial origin-destination movement data. IEEE Trans Visual Comput Graph 25(1):43–53

    Article  Google Scholar 

  • Zhu M, Chen W, Xia J, Ma Y, Zhang Y, Luo Y, Huang Z, Liu L (2019) Location2vec: a situation-aware representation for visual exploration of urban locations. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2019.2901117

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Science Foundation of China (Nos. 61872389, 61872314, U1501252, U1811264, U1711263).

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, China

    Xiaonan Luo & Yuan Yuan

  2. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China

    Kaiyuan Zhang

  3. School of Computer Science and Engineering, Central South University, Changsha, Hunan, China

    Jiazhi Xia

  4. Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China

    Zhiguang Zhou

  5. School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, Guangxi, China

    Liang Chang

  6. Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi, China

    Tianlong Gu

Authors
  1. Xiaonan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaiyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiazhi Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiguang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tianlong Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Chang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, X., Yuan, Y., Zhang, K. et al. Enhancing statistical charts: toward better data visualization and analysis. J Vis 22, 819–832 (2019). https://doi.org/10.1007/s12650-019-00569-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-019-00569-2

Keywords

Search

Navigation