research-article

Multivariate spatial data visualization: a survey

Authors:

Hai LinAuthors Info & Claims

Volume 22, Issue 5

Pages 897 - 912

https://doi.org/10.1007/s12650-019-00584-3

Published: 01 October 2019 Publication History

Abstract

Abstract

Multivariate spatial data play an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis, and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research.

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References

[1]

Akiba H, Ma KL, Chen JH, Hawkes ER (2007) Visualizing multivariate volume data from turbulent combustion simulations. Comput Sci Eng 9(2):76–83

Digital Library

[2]

Akibay H, May KL (2007) A tri-space visualization interface for analyzing time-varying multivariate volume data. In: Proceedings of the 9th Joint Eurographics/IEEE VGTC conference on visualization. Eurographics Association, pp 115–122

[3]

Biswas A, Dutta S, Shen HW, Woodring J (2013) An information-aware framework for exploring multivariate data sets. IEEE Trans Vis Comput Graph 19(12):2683–2692

Digital Library

[4]

Borgo R, Kehrer J, Chung DH, Maguire E, Laramee RS, Hauser H, Ward M, Chen M (2013) Glyph-based visualization: foundations, design guidelines, techniques and applications. In: Eurographics state of the art reports, pp 39–63

[5]

Boyell RL, Ruston H (1963) Hybrid techniques for real-time radar simulation. In: Proceedings of the November 12–14, 1963, fall joint computer conference. ACM, pp 445–458

[6]

Carr H, Duke D (2014) Joint contour nets. IEEE Trans Vis Comput Graph 20(8):1100–1113

Digital Library

[7]

Carr H, Geng Z, Tierny J, Chattopadhyay A, Knoll A (2015) Fiber surfaces: generalizing isosurfaces to bivariate data. Comput Graph Forum 34(3):241–250

[8]

Carr H, Snoeyink J, Axen U (2003) Computing contour trees in all dimensions. Comput Geom 24(2):75–94

Digital Library

[9]

Chuang J, Weiskopf D, Moller T (2009) Hue-preserving color blending. IEEE Trans Vis Comput Graph 15(6):1275–1282

Digital Library

[10]

Crawfis R (1996) Multivariate volume rendering. Technical report, Lawrence Livermore National Lab., CA, USA

[11]

Ding Z, Ding Z, Chen W, Chen H, Tao Y, Li X, Chen W (2016) Visual inspection of multivariate volume data based on multi-class noise sampling. Vis Comput 32(4):465–478

Digital Library

[12]

Duke D, Carr H, Knoll A, Schunck N, Nam HA, Staszczak A (2012) Visualizing nuclear scission through a multifield extension of topological analysis. IEEE Trans Vis Comput Graph 18(12):2033–2040

Digital Library

[13]

Dutta S, Liu X, Biswas A, Shen HW, Chen JP (2017) Pointwise information guided visual analysis of time-varying multi-fields. In: SIGGRAPH Asia 2017 symposium on visualization. ACM, p 17

Digital Library

[14]

Edelsbrunner H, Harer J (2002) Jacobi sets of multiple morse functions. In: Foundations of computational mathematics, Minneapolis, pp 37–57

[15]

Fuchs R, Hauser H (2009) Visualization of multi-variate scientific data. Comput Graph Forum 28(6):1670–1690

[16]

Geng Z, Duke DJ, Carr H, Chattopadhyay A (2014) Visual analysis of hurricane data using joint contour net. In: TPCG, pp 33–40

[17]

Gosink L, Anderson J, Bethel W, Joy K (2007) Variable interactions in query-driven visualization. IEEE Trans Vis Comput Graph 13(6):1400–1407

Digital Library

[18]

Guo H, Hong F, Shu Q, Zhang J, Huang J, Yuan X (2014) Scalable lagrangian-based attribute space projection for multivariate unsteady flow data. In: Visualization symposium (PacificVis), IEEE Pacific. IEEE, pp 33–40

[19]

Guo H, Xiao H, Yuan X (2012) Scalable multivariate volume visualization and analysis based on dimension projection and parallel coordinates. IEEE Trans Vis Comput Graph 18(9):1397–1410

Digital Library

[20]

Hadwiger M, Berger C, Hauser H (2003) High-quality two-level volume rendering of segmented data sets on consumer graphics hardware. In: Visualization, 2003, VIS 2003. IEEE, pp 301–308

[21]

Hagh-Shenas H, Kim S, Interrante V, Healey C (2007) Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Trans Vis Comput Graph 13(6):1270–1277

Digital Library

[22]

Haidacher M, Bruckner S, Groller E (2011) Volume analysis using multimodal surface similarity. IEEE Trans Vis Comput Graph 17(12):1969–1978

Digital Library

[23]

Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

Digital Library

[24]

He W, Liu X, Shen HW, Collis SM, Helmus JJ (2017) Range likelihood tree: a compact and effective representation for visual exploration of uncertain data sets. In: Pacific visualization symposium (PacificVis). IEEE, pp 151–160

[25]

He X, Tao Y, Wang Q, Lin H (2018) Biclusters based viusal exploration of multivariate scientific data. In: Visualization, 2018, VIS Short Paper. IEEE

[26]

Hong F, Lai C, Guo H, Shen E, Yuan X, Li S (2014) FLDA: latent dirichlet allocation based unsteady flow analysis. IEEE Trans Vis Comput Graph 20(12):2545–2554

[27]

Huettenberger L, Heine C, Carr H, Scheuermann G, Garth C (2013) Towards multifield scalar topology based on pareto optimality. Comput Graph Forum 32(3pt3):341–350

[28]

Huettenberger L, Heine C, Garth C (2014) Decomposition and simplification of multivariate data using pareto sets. IEEE Trans Vis Comput Graph 20(12):2684–2693

[29]

Ivanovska T, Linsen L (2008) A user-friendly tool for semi-automated segmentation and surface extraction from color volume data using geometric feature-space operations. In: Visualization in medicine and life sciences. Springer, Berlin, pp 153–170

[30]

Johnson C (2004) Top scientific visualization research problems. IEEE Comput Graph Appl 24(4):13–17

Digital Library

[31]

Kehrer J, Hauser H (2013) Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans Vis Comput Graph 19(3):495–513

Digital Library

[32]

Khlebnikov R, Kainz B, Steinberger M, Schmalstieg D (2013) Noise-based volume rendering for the visualization of multivariate volumetric data. IEEE Trans Visu Comput Graph 19(12):2926–2935

Digital Library

[33]

Kirby RM, Marmanis H, Laidlaw DH (1999) Visualizing multivalued data from 2d incompressible flows using concepts from painting. In: Proceedings of the conference on Visualization’99: celebrating ten years. IEEE Computer Society Press, pp 333–340

[34]

Kniss J, Kindlmann G, Hansen C (2002) Multidimensional transfer functions for interactive volume rendering. IEEE Trans Vis Comput Graph 8(3):270–285

Digital Library

[35]

Kreeger KA, Kaufman AE (1999) Mixing translucent polygons with volumes. In: Proceedings of the Visualization ’99. IEEE, pp 191–525

[36]

Kruger J, Schneider J, Westermann R (2006) Clearview: an interactive context preserving hotspot visualization technique. IEEE Trans Visu Comput Graph 12(5):941–948

Digital Library

[37]

Kruskal JB, Wish M (1978) Multidimensional scaling, vol 11. Sage, Thousand Oaks

[38]

Kühne L, Giesen J, Zhang Z, Ha S, Mueller K (2012) A data-driven approach to hue-preserving color-blending. IEEE Trans Vis Comput Graph 18(12):2122–2129

Digital Library

[39]

Liu X, Shen HW (2016) Association analysis for visual exploration of multivariate scientific data sets. IEEE Trans Vis Comput Graph 22(1):955–964

Digital Library

[40]

Ljung P, Krüger J, Groller E, Hadwiger M, Hansen CD, Ynnerman A (2016) State of the art in transfer functions for direct volume rendering. Comput Graph Forum 35(3):669–691

[41]

Lu K, Shen HW (2017) Multivariate volumetric data analysis and visualization through bottom-up subspace exploration. In: Pacific Visualization Symposium (PacificVis), 2017 IEEE. IEEE, pp 141–150

[42]

Maaten LVD, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Research 9:2579–2605

[43]

Nagaraj S, Natarajan V (2011) Relation-aware isosurface extraction in multifield data. IEEE Trans Vis Comput Graph 17(2):182–191

Digital Library

[44]

Nagaraj S, Natarajan V, Nanjundiah RS (2011) A gradient-based comparison measure for visual analysis of multifield data. Comput Graph Forum 30(3):1101–1110

Digital Library

[45]

Rocha A, Alim U, Silva JD, Sousa MC (2017) Decal-maps: real-time layering of decals on surfaces for multivariate visualization. IEEE Trans Vis Comput Graph 23(1):821–830

Digital Library

[46]

Sauber N, Theisel H, Seidel HP (2006) Multifield-graphs: an approach to visualizing correlations in multifield scalar data. IEEE Trans Vis Comput Graph 12(5):917–924

Digital Library

[47]

Schneider D, Heine C, Carr H, Scheuermann G (2013) Interactive comparison of multifield scalar data based on largest contours. Comput Aided Geom Des 30(6):521–528

[48]

Schneider D, Wiebel A, Carr H, Hlawitschka M, Scheuermann G (2008) Interactive comparison of scalar fields based on largest contours with applications to flow visualization. IEEE Trans Vis Comput Graph 14(6):1475–1482

Digital Library

[49]

Schroeder D, Keefe DF (2016) Visualization-by-sketching: an artist’s interface for creating multivariate time-varying data visualizations. IEEE Trans Vis Comput Graph 22(1):877–885

Digital Library

[50]

Soundararajan KP, Schultz T (2015) Learning probabilistic transfer functions: a comparative study of classifiers. Comput Grap Forum 34(3):111–120

Digital Library

[51]

Su Y, Agrawal G, Woodring J, Myers K, Wendelberger J, Ahrens J (2018) Information guided data sampling and recovery using bitmap indexing. In: IEEE Pacific visualization symposium

[52]

Sukharev J, Wang C, Ma KL, Wittenberg AT (2009) Correlation study of time-varying multivariate climate data sets. In: Visualization Symposium, 2009, PacificVis’ 09. IEEE Pacific. IEEE, pp 161–168

[53]

Suthambhara N, Natarajan V (2011) Simplification of jacobi sets. In: Topological methods in data analysis and visualization. Springer, Berlin, pp 91–102

[54]

Tierny J, Carr H (2017) Jacobi fiber surfaces for bivariate reeb space computation. IEEE Trans Vis Comput Graph 23(1):960–969

Digital Library

[55]

Tzeng FY, Ma KL (2004) A cluster-space visual interface for arbitrary dimensional classification of volume data. In: Proceedings of the 6th joint Eurographics-IEEE TCVG conference on visualization. Eurographics Association, pp 17–24

[56]

Urness T, Interrante V, Marusic I, Longmire E, Ganapathisubramani B (2003) Effectively visualizing multi-valued flow data using color and texture. In: Proceedings of the 14th IEEE Visualization 2003 (VIS’03). IEEE Computer Society, p 16

[57]

Van Long T, Linsen L (2009) Multiclustertree: interactive visual exploration of hierarchical clusters in multidimensional multivariate data. Comput Graph Forum 28(3):823–830

[58]

Wang C, Yu H, Grout RW, Ma KL, Chen JH (2011) Analyzing information transfer in time-varying multivariate data. In: Visualization Symposium (PacificVis), 2011 IEEE Pacific. IEEE, pp 99–106

[59]

Wang Q, Tao Y, Lin H (2018) Featurenet: automatic visual summarization of major features in multivariate volume data. J Vis 21(3):443–455

Digital Library

[60]

Wang Z, Seidel HP, Weinkauf T (2016) Multi-field pattern matching based on sparse feature sampling. IEEE Trans Vis Comput Graph 22(1):807–816

Digital Library

[61]

Wei TH, Chen CM, Biswas A (2015) Efficient local histogram searching via bitmap indexing. In: Computer graphics forum, vol 34. Wiley Online Library, pp 81–90

[62]

Wei TH, Chen CM, Woodring J, Zhang H, Shen HW (2017) Efficient distribution-based feature search in multi-field datasets. In: Pacific visualization symposium (PacificVis), 2017 IEEE, pp 121–130. IEEE

[63]

Wu F, Chen G, Huang J, Tao Y, Chen W (2015) Easyxplorer: a flexible visual exploration approach for multivariate spatial data. Comput Graph Forum 34(7):163–172

Digital Library

[64]

Wu K, Knoll A, Isaac BJ, Carr H, Pascucci V (2017) Direct multifield volume ray casting of fiber surfaces. IEEE Trans Vis Comput Graph 23(1):941–949

Digital Library

[65]

Zhao X, Kaufman A (2010) Multi-dimensional reduction and transfer function design using parallel coordinates. In: Volume graphics, international symposium on volume graphics. NIH Public Access, p 69

[66]

Zhou L, Hansen C (2014) Guideme: slice-guided semiautomatic multivariate exploration of volumes. Comput Graph Forum 33(3):151–160

[67]

Zhou L, Schott M, Hansen C (2012) Transfer function combinations. Comput Graph 36(6):596–606

Digital Library

[68]

Zhou L, Weiskopf D (2017) Indexed-points parallel coordinates visualization of multivariate correlations. IEEE Trans Vis Comput Graph 24:1997–2010

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Multivariate spatial data visualization: a survey

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Information

Published In

cover image Journal of Visualization

Journal of Visualization Volume 22, Issue 5

Oct 2019

198 pages

ISSN:1343-8875

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Copyright © 2019 The Visualization Society of Japan.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2019

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Cited By

Li JLai CZhang HYuan X(2024)PM-Vis: A Visual Analytics System for Tracing and Analyzing the Evolution of Pottery MotifsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.338852530:6(3022-3034)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3388525
Neuhauser CStumpfegger JWestermann R(2024)Adaptive Sampling of 3D Spatial Correlations for Focus+Context VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332685530:2(1608-1623)Online publication date: 1-Feb-2024
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He XTao YYang SDai HLin H(2023)voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318909429:10(4296-4311)Online publication date: 1-Oct-2023
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Baharum AIsmail RIsmail IMat Noor NDeris FHalamy S(2023)Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and TechniquesAdvances in Visual Informatics10.1007/978-981-99-7339-2_39(468-477)Online publication date: 15-Nov-2023
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He XYang STao YDai HLin H(2022)Graph convolutional network-based semi-supervised feature classification of volumesJournal of Visualization10.1007/s12650-021-00787-725:2(379-393)Online publication date: 1-Apr-2022
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He XTao YYang SChen CLin H(2022)ScalarGCN: scalar-value association analysis of volumes based on graph convolutional networkJournal of Visualization10.1007/s12650-021-00779-725:1(77-93)Online publication date: 1-Feb-2022
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Bai ZTao YLin H(2020)Time-varying volume visualization: a surveyJournal of Visualization10.1007/s12650-020-00654-x23:5(745-761)Online publication date: 19-Jun-2020
https://dl.acm.org/doi/10.1007/s12650-020-00654-x

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