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An IconMap-based exploratory analytical approach for multivariate geospatial data

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Abstract

This paper discusses an iconmap-based visualization technique that enables multiple geospatial variables to be illustrated in a single GIS raster layer. This is achieved by extending the conventional pixel-based data structure to an iconic design. In this way, spatial patterns generated by the interaction of geographic variables can be disclosed, and geospatial information mining can be readily achieved. As a case study, a visual analysis of soil organic matter and soil nutrients for Shuangliu County in the city of Chengdu, China, was undertaken using the prototype IconMapper software, developed by the authors. The results show that the static IconMap can accurately exhibit trends in the distribution of organic matter and nutrients in soil. The dynamic iconmap can both reflect interaction patterns between organic matter and the nutrient variables, and display soil fertility levels in a comprehensive way. Thus, the iconmap-based visualization approach is shown to be a non-fused, exploratory analytical approach for multivariate data analysis and as a result is a valuable method for visually analyzing soil fertility conditions.

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References

  1. Mitasova H, Harmon R S, Weaver K J, et al. Scientific visualization of landscapes and landforms. Geomorphology, 2011, doi:10.1016/j.geomorph.2010.09.033

  2. De Chiara D, Fatto V D, Laurini R, et al. A chorem-based approach for visually analyzing spatial data. J Visual Lang Comput, 2011, 22: 173–193

    Article  Google Scholar 

  3. Xu B, Lin H, Chiu L, et al. Collaborative virtual geographic environments: a case study of air pollution simulation. Inform Sciences, 2011, 181: 2231–2246

    Article  Google Scholar 

  4. Kulawiak M, Prospathopoulos A, Perivoliotis L, et al. Interactive visualization of marine pollution monitoring and forecasting data via a Web-based GIS. Comput Geosci-UK, 2010, doi:10.1016/j.cageo.2010.02.008

  5. Ropinski T, Oeltze S, Preim B. Survey of glyph-based visualization techniques for spatial multivariate medical data. Comput Graph, 2011, 35: 392–401

    Article  Google Scholar 

  6. Zhang X, Pazner M. The icon imagemap technique for multivariate geospatial data visualization: approach and software system. Cartogr Geogr Inf Sci, 2004, 31: 29–41

    Article  Google Scholar 

  7. Pazner M, Lafreniere M. GIS icon maps. In: ACSM/ASPRS Annual Convention and Exposition, Technical Papers Volume 5: Auto-Carto 13. Seattle: American Society for Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping, 1997. 126–135

  8. Lorenz H, Dollner J. 3D feature surface properties and their application in geovisualization. Comput Environ Urban Syst, 2010, 34: 476–483

    Article  Google Scholar 

  9. van Lammeren R, Houtkamp J, Colijn S, et al. Affective appraisal of 3D land use visualization. Comput Environ Urban Syst, 2010, 34: 465–475

    Article  Google Scholar 

  10. Brooks S, Whalley J L. Multilayer hybrid visualizations to support 3D GIS. Comput Environ Urban Syst, 2008, 32: 278–292

    Article  Google Scholar 

  11. Ning J. Design and development of a 3D visualization system for gravity field models: WHU-3D gravit. Geomat Inf Sci Wuhan Univ, 2007, 32: 945–949

    Google Scholar 

  12. Hildebrandt D, Dollner J. Service-oriented, standards-based 3D geovisualization: potential and challenges. Comput Environ Urban Syst, 2010, 34: 484–495

    Article  Google Scholar 

  13. Jia Z, Liu Y, Zhang T. Design and development of system based on visual interactive spatial data mining. Geomat Inf Sci Wuhan Univ, 2006, 31: 916–919

    Google Scholar 

  14. Schroeder W, Martin K, Lorensen B. The Visualization Toolkit. 2nd ed. USA: Prentice Hall Inc., 1997

    Google Scholar 

  15. Keim D A. Visualization and data. In: Widom J, ed. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. Montreal: ACM Press, 1996. 543

    Chapter  Google Scholar 

  16. Chan A, MacLean K, McGrenere J. Designing haptic icons to support collaborative turn-taking. Int J Hum-Comput St, 2008, 66: 333–355

    Article  Google Scholar 

  17. Dovey D. Vector plots for irregular grids. In: Nielson G M, Silver D, eds. Proceedings of the 6th IEEE Visualization Conference (Visualization’95). Atlanta: IEEE Computer Society, 1995. 248–253

    Chapter  Google Scholar 

  18. Horton W. The Icon Book: Visual Symbols for Computer Systems and Documentation. New York: John Wiley & Sons Inc., 1994

    Google Scholar 

  19. Yaman M, Härting M, Nsengiyumva S, Britton D T. Scientific visualization: analysis, exploration and presentation of tri-axial stress states of Kr implanted titanium determined by X-ray diffraction. Surface Coat Technol, 2007, 201: 8431–8436

    Article  Google Scholar 

  20. Wood M. The traditional map as a visualization technique. In: Hearbshaw H, Unwin D, eds. Visualization in Geographical Information Systems. England: John Wiley & Sons Ltd., 1997. 9–17

    Google Scholar 

  21. Walsum T V, Post F H, Silver D, et al. Feature extraction and iconic visualization. IEEE Trans Vis Comput Gr, 1997, 2: 111–119

    Article  Google Scholar 

  22. Kolhoff P, Preuß J, Loviscach J. Content-based icons for music files. Comput Graph, 2008, 32: 550–560

    Article  Google Scholar 

  23. Chernoff H, Rizvi M H. Effect on classification error of random permutations of features in representing multivariate data by faces. J Am Stat Assoc, 1975, 70, 548-554

    Google Scholar 

  24. Woodside A G, Cruickshank B F, Ning D. Stories visitors tell about Italian cities as destination icons. Tourism Manage, 2007, 28: 162–174

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Remote Sensing and GIS, Peking University, Beijing, 100871, China

    XianFeng Zhang, ChunHua Liao & Yu Liu

  2. Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Jonathan Li

Authors
  1. XianFeng Zhang
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  2. ChunHua Liao
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  3. Yu Liu
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  4. Jonathan Li
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Correspondence to XianFeng Zhang.

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Zhang, X., Liao, C., Liu, Y. et al. An IconMap-based exploratory analytical approach for multivariate geospatial data. Sci. China Inf. Sci. 56, 1–10 (2013). https://doi.org/10.1007/s11432-012-4692-6

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  • DOI: https://doi.org/10.1007/s11432-012-4692-6

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