Abstract
Multivariate visualization for atmospheric pollution is a challenging research topic. Appropriate algorithms and data structures based on modern graphics hardware are used to obtain high performance. 3D visualization of the atmospheric wind field and pollutant concentrations can easily result in visual perception problems such as occlusion and cluttering even artifacts. To solve the above issues, a K-means clustering technique is used in combination with a similarity metric between streamlines based on an iterative closest point method to cluster the initial streamlines. A small set of streamlines is then selected to represent the prominent structure of the wind field. The proper illumination model and the depth sorting method reduce the inter-occlusion between streamlines and isosurfaces to show much clearer wind field pattern and important features effectively. The atmospheric pollution data set is employed to evaluate the proposed algorithm framework.
Graphic abstract
Similar content being viewed by others
References
Besl PJ, McKay ND (1992) A method for registration of 3d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Carr H, Geng Z, Tiemy J, et al (2015) Fiber surfaces: generalizing isosurfaces to bivariate data. In: Proceedings of EuroVis, pp 241–250
Cuntz N, Leidl M, Kolb A, et al (2007) GPU-based dynamic flow visualization for climate research applications. In: Proceedings of simulation and visualization, pp 371–384
Dyken C, Ziegler G, Theobalt C et al (2008) High-speed marching cubes using histogram pyramids. Comput Graph Forum 27(8):2028–2039
Edmunds M, McLoughlin T, Laramee RS, et al (2011) Automatic stream surface seeding. In: Proceedings of EuroVis, pp 53–56
Edmunds M, Laramee RS, Chen GN, et al (2012) Advanced, automatic stream surface seeding and filtering. In: Proceedings of theory and practice of computer graphics, pp 53–60
Furuya S, Itoh T (2009) A streamline selection technique for integrated scalar and vector visualization. J Soc Art Sci 8(3):120–129
Han J, Tao J, Wang C (2018) FlowNet: a deep learning framework for clustering and selection of streamlines and stream surfaces. IEEE Trans Vis Comput Graph (Early Access):1–1
Hurter C, Puechmorel S, Nicol F et al (2018) Functional decomposition for bundled simplification of trail sets. IEEE Trans Vis Comput Graph 24(1):500–510
Jobard B, Lefer W (May, 1997) Creating evenly-spaced streamlines of arbitrary density. In: Proceedings of the 8th Eurographics workshop on visualization in scientific computing, pp 43–56
Johansson G, Carr H (2006) Accelerating marching cubes with graphics hardware. In: Proceedings of the conference of the center for advanced studies on collaborative research, pp 39–39
Kehrer J, Hauser H (2013) Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans Vis Comput Graph 19(3):495–513
Koyamada K (1992) Visualization of simulated atmosphericflow in a clean room. In: Proceedings of the 3rd conference on visualization, pp 156–163
Krüger J, Kipfer P, Kondratieva P et al (2005) A particle system for interactive visualization of 3d flows. IEEE Trans Vis Comput Graph 11(6):744–756
Li L, Shen HW (2007) Image-based streamline generation and rendering. IEEE Trans Vis Comput Graph 13(3):630–640
Liu ZP, Robert J, Moorhead II et al (2006) An advanced evenly-spaced streamline placement algorithm. IEEE Trans Vis Comput Graph 12(5):965–972
Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. Comput Graph 21(4):163–169
Lu DY (2017) Information-theoretic exploration for texture-based visualization. J Vis 20(2):393–404
Lu DY, Zhu DM, Wang ZQ (2013) Streamline selection algorithm for tree-dimensional flow fields. J Comput Aided Des Comput Graph 25(5):666–673
Lu DY, Zhu DM, Wang ZQ et al (2016) Efficient level of detail for texture-based flow visualization. Comput Anim Virtual Worlds 27(2):123–140
Lu DY, Zhu DM, Wang ZQ et al (2017a) Enhanced texture advection algorithm. J Comput Aided Des Comput Graph 29(4):670–679
Lu DY, Zhu DM, Wang ZQ (2017b) Optimal viewpoint selection for texture-based flow visualization. J Comput Aided Des Comput Graph 29(12):2281–2287
Mallo O, Peikert R, Sigg C, et al (2005) Illuminated lines revisited. In: Proceedings of the conference on visualization, pp 19–26
Mao X, Hatanaka Y, Higashida H, et al (1998) Image-guided streamline placement on curvilinear grid surfaces. In: Proceedings of the conference on visualization, pp 135–142
Marchesin S, Chen CK, Ho C et al (2010) View-dependent streamlines for 3d vector fields. IEEE Trans Vis Comput Graph 16(6):1578–1586
Mattausch O, Theussl T, Hauser H, et al (2003) Strategies for interactive exploration of 3d flow using evenly-spaced illuminated streamlines. In: Proceedings of 19th spring conference on computer graphics, pp 213–222
Neeman H (1990) A decomposition algorithm for visualizing irregular grids. Comput Graph 24(5):49–56
Park S, Budge B C, Linsen L et al (2005) Dense geometric flow visualization. In: Proceedings of Eurographics/IEEE VGTC symposium on visualization, pp 21–28
Ropinski T, Oeltze S, Preim B (2011) Survey of glyph-based visualization techniques for spatial multivariate medical data. Comput Graph 35(2):392–401
Sadarjoen A, Walsum TV, Hin AJS (1994) Particle tracing algorithms for 3d curvilinear grids. In: Proceedings of 5th Eurographics workshop on visualization in scientific computing, pp 311–335
Salzbrunn T, Scheuermann G (2006) Streamline predicates. IEEE Trans Vis Comput Graph 12(6):1601–1612
Schroeder W, Maynard R, Geveci B (2015) Flying edges: a high-performance scalable isocontouring algorithm. In: Proceedings of large data analysis and visualization (LDAV), pp 33–40
Shannon CE (2001) A mathematical theory of communication. Newslett ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55
Stalling D, Zöckler M, Hege HC (1997) Fast display of illuminated field lines. IEEE Trans Vis Comput Graph 3(2):118–128
Tierny J, Carr H (2017) Jacobi fiber surfaces for bivariate Reeb space computation. IEEE Trans Vis Comput Graph 23(1):960–969
Turk G, Banks D (1996) Image-guided streamline placement. In: Proceedings of 23rd international conference on computer graphics, ACM SIGGRAPH, pp 453–460
Tushar A, Elham S, Alireza E (2016) Isosurface visualization of data with nonparametric models for uncertainty. IEEE Trans Vis Comput Graph 22(1):777–786
Ueffinger M, Klein T, Strengert M et al (2008) GPU-based streamlines for surface-guided 3d flow visualization. In: Proceedings of 13th international workshop on vision, modelling, and visualization, pp 90–100
Verma V, Kao D, Pang A (2000) A flow-guided streamline seeding strategy. In: Proceedings of the conference on visualization, pp 163–170
Wilhelms J, Gelder AV (1991) Octree for faster isosurface generation. Comput Graph 25(4):57–65
Wong PC, Bergeron DR (1994) 30 years of multidimensional multivariate visualization. In: Proceedings of scientific visualization, pp 3–33
Wu KQ, Liu ZP, Zhang S et al (2010) Topology-aware evenly spaced streamline placement. IEEE Trans Vis Comput Graph 16(5):791–801
Ye X, Kao D, Pang A (2005) Strategy for seeding 3d streamlines. In: Proceedings of the conference on Visualization, pp 471–478
Zöckler M, Stalling D, Hege HC (1996) Interactive visualization of 3d vector fields using illuminated stream lines. In: Proceedings of the conference on visualization, pp 107–113
Acknowledgements
The authors would like to thank the reviewers for their valuable comments. Thanks go to Z.F. Wang from the Institute of Atmospheric Physics in Chinese Academy of Sciences for providing the air pollution data set in Pearl River Delta region. This work is supported and funded by the National Natural Science Foundation of China (Nos. 61379085, 61601261), funded by Science and Technology Program Foundation for Colleges and Universities in Shandong Province (No. J17KA062), funded by Industry-academy Cooperation and Cooperative Education Project of Ministry of Education (No. 201602028014) and supported by Laboratory Open Foundation of Qufu Normal University (No. sk201723).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (mp4 1220 KB)
Rights and permissions
About this article
Cite this article
Lu, D., Ge, Y., Wang, L. et al. Multivariate visualization for atmospheric pollution. J Vis 22, 1093–1105 (2019). https://doi.org/10.1007/s12650-019-00588-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-019-00588-z