Supporting Web-Based Visual Exploration of Large-Scale Raster Geospatial Data Using Binned Min-Max Quadtree

  • Jianting Zhang
  • Simin You
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6187)

Abstract

Traditionally environmental scientists are limited to simple display and animation of large-scale raster geospatial data derived from remote sensing instrumentation and model simulation outputs. Identifying regions that satisfy certain range criteria, e.g., temperature between [t1,t2) and precipitation between [p1,p2), plays an important role in query-driven visualization and visual exploration in general. In this study, we have proposed a Binned Min-Max Quadtree (BMMQ-Tree) to index large-scale numeric raster geospatial data and efficiently process queries on identifying regions of interests by taking advantages of the approximate nature of visualization related queries. We have also developed an end-to-end system that allows users visually and interactively explore large-scale raster geospatial data in a Web-based environment by integrating our query processing backend and a commercial Web-based Geographical Information System (Web-GIS). Experiments using real global environmental data have demonstrated the efficiency of the proposed BMMQ-Tree. Both experiences and lessons learnt from the development of the prototype system and experiments on the real dataset are reported.

Keywords

Binned Min-Max Quadtree raster geospatial data Web-GIS visual exploration 

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References

  1. 1.
    Li, Y.K., Bretschneider, T.R.: Semantic-sensitive satellite image retrieval. IEEE Transactions on Geoscience and Remote Sensing 45(4), 853–860 (2007)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    WRF: Weather research and forecast, http://www.wrf-model.org
  4. 4.
    UCAR Unidata: Unidata integrated data viewer (idv), http://www.unidata.ucar.edu/software/idv/
  5. 5.
  6. 6.
    Kothuri, R.K.V., Ravada, S., Abugov, D.: Quadtree and r-tree indexes in oracle spatial: a comparison using gis data. In: SIGMOD 2002, pp. 546–557 (2002)Google Scholar
  7. 7.
    Fang, Y., Friedman, M., Nair, G., Rys, M., Schmid, A.E.: Spatial indexing in microsoft sql server 2008. In: SIGMOD 2008, pp. 1207–1216 (2008)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    MapServer: Mapserver open source mapping, http://mapserver.org/
  11. 11.
    TileCache: Tilecache - web map tile caching, http://tilecache.org/
  12. 12.
    Wu, K., Koegler, W., Chen, J., Shoshani, A.: Using bitmap index for interactive exploration of large datasets. In: SSDBM 2003, pp. 65–74 (2003)Google Scholar
  13. 13.
    Stockinger, K., Shalf, J., Wu, K., Bethel, E.W.: Query-driven visualization of large data sets. In: IEEE Visualization, p. 22 (2005)Google Scholar
  14. 14.
    Glatter, M., Mollenhour, C., Huang, J., Gao, J.Z.: Scalable data servers for large multivariate volume visualization. IEEE TVCG 12(5), 1291–1298 (2006)Google Scholar
  15. 15.
    Kendall, W., Glatter, M., Huang, J., Peterka, T., Latham, R., Ross, R.: Terascale data organization for discovering multivariate climatic trends. In: Bergel, A., Fabry, J. (eds.) SC 2009. LNCS, vol. 5634, pp. 1–12. Springer, Heidelberg (2009)Google Scholar
  16. 16.
    Fuchs, R., Hauser, H.: Visualization of multi-variate scientific data. Computer Graphics Forum 28(6), 1670–1690 (2009)CrossRefGoogle Scholar
  17. 17.
    Lawrence Berkeley National Laboratory: Fastbit, https://sdm.lbl.gov/fastbit/
  18. 18.
    Sinha, R.R., Winslett, M., Wu, K.: Finding regions of interest in large scientific datasets. In: SSDBM 2009, pp. 130–147 (2009)Google Scholar
  19. 19.
    Maceachren, A.M., Wachowicz, M., Edsall, R., Haug, D., Masters, R.: Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographical Information Science 13(4), 311–334 (1999)CrossRefGoogle Scholar
  20. 20.
    Andrienko, N., Andrienko, G., Gatalsky, P.: Exploratory spatio-temporal visualization: an analytical review. Journal of Visual Languages and Computing 14(6), 503–541 (2003)CrossRefGoogle Scholar
  21. 21.
    Guo, D.S., Chen, J., MacEachren, A.M., Liao, K.: A visualization system for space-time and multivariate patterns (vis-stamp). IEEE TVCG 12(6), 1461–1474 (2006)Google Scholar
  22. 22.
    Anselin, L., Syabri, I., Kho, Y.: Geoda: An introduction to spatial data analysis. Geographical Analysis 38(1), 5–22 (2006)CrossRefGoogle Scholar
  23. 23.
    Gahegan, M., Takatsuka, M., Wheeler, M., Hardisty, F.: Introducing geovista studio: an integrated suite of visualization and computational methods for exploration and knowledge construction in geography. Computers, Environment and Urban Systems 26(4), 267–292 (2002)CrossRefGoogle Scholar
  24. 24.
    Rushing, J., Ramachandran, R., Nair, U., Graves, S., Welch, R., Lin, H.: Adam: a data mining toolkit for scientists and engineers. Computers & Geosciences 31(5), 607–618 (2005)CrossRefGoogle Scholar
  25. 25.
    Zhang, J.T., Gruenwald, L., Gertz, M.: Vdm-rs: A visual data mining system for exploring and classifying remotely sensed images. Computers & Geosciences 35(9), 1827–1836 (2009)CrossRefGoogle Scholar
  26. 26.
    Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for gis-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58(3-4), 239–258 (2004)CrossRefGoogle Scholar
  27. 27.
    Liu, Y., Guo, Q.H., Kelly, M.: A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing 63(4), 461–475 (2008)CrossRefGoogle Scholar
  28. 28.
    Goodchild, M.F., Yuan, M., Cova, T.J.: Towards a general theory of geographic representation in gis. Int. J. of Geographical Information Science 21(3), 239–260 (2007)CrossRefGoogle Scholar
  29. 29.
    Cohen, S., Hurley, P., Schulz, K.W., Barth, W.L., Benton, B.: Scientific formats for object-relational database systems: a study of suitability and performance. SIGMOD Rec. 35(2), 10–15 (2006)CrossRefGoogle Scholar
  30. 30.
    Stancu-Mara, S., Baumann, P.: A comparative benchmark of large objects in relational databases. In: IDEAS 2008, pp. 277–284 (2008)Google Scholar
  31. 31.
    Abadi, D.J., Madden, S.R., Hachem, N.: Column-stores vs. row-stores: how different are they really? In: SIGMOD 2008 (2008)Google Scholar
  32. 32.
    Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: SIGMOD 2009, pp. 165–178 (2009)Google Scholar
  33. 33.
  34. 34.
    The HDF Group: Hdf5, http://www.hdfgroup.org/HDF5/
  35. 35.
    Baumann, P., Furtado, P., Ritsch, R., Widmann, N.: The rasdaman approach to multidimensional database management. In: SAC 1997, pp. 166–173 (1997)Google Scholar
  36. 36.
    Marathe, A.P., Salem, K.: Query processing techniques for arrays. The VLDB Journal 11(1), 68–91 (2002)CrossRefGoogle Scholar
  37. 37.
    Baumann, P.: Designing a geo-scientific request language - a database approach. In: SSDBM 2009 (2009)Google Scholar
  38. 38.
    Sarawagi, S., Stonebraker, M.: Efficient organization of large multidimensional arrays. In: ICDE 1994, pp. 328–336 (1994)Google Scholar
  39. 39.
    Otoo, E.J., Rotem, D.: Efficient storage allocation of large-scale extendible multi-dimensional scientific datasets. In: SSDBM 2006, pp. 179–183 (2006)Google Scholar
  40. 40.
    Kim, J., JaJa, J.: Component-based data layout for efficient slicing of very large multidimensional volumetric data. In: SSDBM 2007, p. 8 (2007)Google Scholar
  41. 41.
    Gaede, V., Gunther, O.: Multidimensional access methods. ACM Computing Surveys 30(2), 170–231 (1998)CrossRefGoogle Scholar
  42. 42.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  43. 43.
    Cignoni, P., Marino, P., Montani, C., Puppo, E., Scopigno, R.: Speeding up isosurface extraction using interval trees. IEEE TVCG 3(2), 158–170 (1997)Google Scholar
  44. 44.
    Wilhelms, J., Vangelder, A.: Octrees for faster isosurface generation. ACM Transactions on Graphics 11(3), 201–227 (1992)MATHCrossRefGoogle Scholar
  45. 45.
    Wang, C., Chiang, Y.J.: Isosurface extraction and view-dependent filtering from time-varying fields using persistent time-octree (ptot). IEEE TVCG 15(6), 1367–1374 (2009)Google Scholar
  46. 46.
    Gress, A., Klein, R.: Efficient representation and extraction of 2-manifold isosurfaces using kd-trees. Graphical Models 66(6), 370–397 (2004)CrossRefGoogle Scholar
  47. 47.
    Hughes, D.M., Lim, I.S.: Kd-jump: a path-preserving stackless traversal for faster isosurface raytracing on gpus. IEEE TVCG 15(6), 1555–1562 (2009)Google Scholar
  48. 48.
    Lin, T.W.: Compressed quadtree representations for storing similar images. Image and Vision Computing 15(11), 833–843 (1997)CrossRefGoogle Scholar
  49. 49.
    Chan, Y.K., Chang, C.C.: Block image retrieval based on a compressed linear quadtree. Image and Vision Computing 22(5), 391–397 (2004)CrossRefGoogle Scholar
  50. 50.
    Chung, K.L., Liu, Y.W., Yan, W.M.: A hybrid gray image representation using spatial- and dct-based approach with application to moment computation. Journal of Visual Communication and Image Representation 17(6), 1209–1226 (2006)CrossRefGoogle Scholar
  51. 51.
    Vassilakopoulos, M., Manolopoulos, Y., Economou, K.: Overlapping quadtrees for the representation of similar images. Image and Vision Computing 11(5), 257–262 (1993)CrossRefGoogle Scholar
  52. 52.
    Manolopoulos, Y., Nardelli, E., Papadopoulos, A., Proietti, G.: Mof-tree: a spatial access method to manipulate multiple overlapping features. Inf. Syst. 22(9), 465–481 (1997)CrossRefGoogle Scholar
  53. 53.
    Nardelli, E., Proietti, G.: An efficient spatial access method for spatial images containing multiple non-overlapping features. Information Systems 25(8), 553–568 (2000)MATHCrossRefGoogle Scholar
  54. 54.
    Tzouramanis, T., Vassilakopoulos, M., Manolopoulos, Y.: On the generation of time-evolving regional data. Geoinformatica 6(3), 207–231 (2002)MATHCrossRefGoogle Scholar
  55. 55.
    Manolopoulos, Y., Nardelli, E., Proietti, G., Tousidou, E.: A generalized comparison of linear representations of thematic layers. Data & Knowledge Engineering 37(1), 1–23 (2001)MATHCrossRefGoogle Scholar
  56. 56.
    Manouvrier, M., Rukoz, M., Jomier, G.: Quadtree representations for storage and manipulation of clusters of images. Image and Vision Computing 20(7), 513–527 (2002)CrossRefGoogle Scholar
  57. 57.
    Wu, K., Stockinger, K., Shoshani, A.: Breaking the curse of cardinality on bitmap indexes. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 348–365. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  58. 58.
    Sinha, R.R., Winslett, M.: Multi-resolution bitmap indexes for scientific data. ACM Trans. Database Syst. 32(3), 16 (2007)CrossRefGoogle Scholar
  59. 59.
    Rubel, O., Wu, K., et al.: High performance multivariate visual data exploration for extremely large data. In: Pautasso, C., Tanter, É. (eds.) SC 2008. LNCS, vol. 4954, pp. 1–12. Springer, Heidelberg (2008)Google Scholar
  60. 60.
    Gosink, L.J., Anderson, J.C., Bethel, E.W., Joy, K.I.: Query-driven visualization of time-varying adaptive mesh refinement data. IEEE TVCG 14(6), 1715–1722 (2008)Google Scholar
  61. 61.
    Wood, J., Dykes, J., Slingsby, A., Clarke, K.: Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup. IEEE TVCG 13(6), 1176–1183 (2007)Google Scholar
  62. 62.
    Dork, M., Carpendale, S., Collins, C., Williamson, C.: Visgets: Coordinated visualizations for web-based information exploration and discovery. IEEE TVCG 14(6), 1205–1212 (2008)Google Scholar
  63. 63.
    Tobler, W.: A computer model simulating urban growth in the detroit region. Economic Geography 46(2), 234–240 (1970)CrossRefGoogle Scholar
  64. 64.
    GDAL: Geospatial data abstraction library, http://www.gdal.org/
  65. 65.
    Adobe: Adobe flex api, http://www.adobe.com/products/flex/
  66. 66.
    ESRI: Arcgis flex api, http://www.adobe.com/products/flex/
  67. 67.
    University of Maryland: Global land cover facility (glcf), ftp://ftp.glcf.umiacs.umd.edu/modis/500m/
  68. 68.
    Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25(15), 1965–1978 (2005)CrossRefGoogle Scholar
  69. 69.
    WorldClim: Worldclim current conditions data 1950-2000, http://www.worldclim.org/current

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jianting Zhang
    • 1
    • 2
  • Simin You
    • 2
  1. 1.Department of Computer ScienceThe City College of the City University of New YorkNew York
  2. 2.Department of Computer ScienceThe Graduate Center of the City University of New YorkNew York

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