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.
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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
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
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
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
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
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
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
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
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
Chen W, Guo F, Wang FY (2015b) A survey of traffic data visualization. IEEE Trans Intell Transp Syst 16(6):2970–2984
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
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
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
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
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
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
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
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
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
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
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
Friendly M (2008) The golden age of statistical graphics. Stat Sci 23(4):502–535
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
Gleicher M, Correll M, Nothelfer C, Franconeri S (2013) Perception of average value in multiclass scatterplots. IEEE Trans Visual Comput Graph 19(12):2316
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
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
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
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
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
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
Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91
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
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
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
Kincaid R (2010) Signallens: Focus+Context applied to electronic time series. IEEE Trans Visual Comput Graph 16(6):900
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
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
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
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
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
Liu S, Chen Y, Wei H, Yang J, Zhou K, Drucker SM (2015) Exploring topical lead-lag across corpora. TKDE 27(1):115–129
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
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
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
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
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
Mayorga A, Gleicher M (2013) Splatterplots: overcoming overdraw in scatter plots. IEEE Trans Visual Comput Graph 19(9):1526–1538
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
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
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
Munzner T (2014) Visualization analysis and design. AK Peters, Natick
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
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
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
Ren D, Lee B, Höllerer T (2017) Stardust: accessible and transparent GPU support for information visualization rendering. Comput Graph Forum 36(3):179–188
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
Sarikaya A, Gleicher M (2018) Scatterplots: tasks, data, and designs. IEEE Trans Visual Comput Graph 24(1):402–412
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
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
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
Streit M, Gehlenborg N (2014) Bar charts and box plots. Nat Methods 11(2):117
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
Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323
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
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
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
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
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
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
Wickham H, Hofmann H (2011) Product plots. IEEE Trans Visual Comput Graph 17(12):2223–2230
Wilkinson L (1999) Dot plots. Am Stat 53(3):276–281
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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This work is supported by the National Science Foundation of China (Nos. 61872389, 61872314, U1501252, U1811264, U1711263).
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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
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DOI: https://doi.org/10.1007/s12650-019-00569-2
Keywords
- Statistic charts
- Enhancements
- Design space