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Hierarchical Geospatial Computing Environment for Data-intensive Geographic Process Simulation

  • Mingyuan Hu
  • Hui Lin
  • Bingli Xu
  • Ya Hu
  • Sammy Tang
  • Weitao Che

Abstract

Geographic information system (GIS) professionals recognize that geographic process is essential for understanding what is happening in the world, learning how the environment is changing, modelling how complex systems work and giving context to other types of data. Consequently, geographic process models have been increasingly featured for the next generation geographic information science (system), as a method for phenomena simulation and mechanism analysis of the physical environment and its live activities, thereby driving conventional GIS based on data manipulation into the world of dynamic and computational processes (Goodchild, 2006; Yuan and Hornsby, 2008; Torrens, 2009; CSDGS et al., 2010).

Keywords

Pearl River Delta Plume Model Globus ToolKit Gauss Plume Model Tiled Display 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

The work described in this chapter is supported by the National High Technology Research and Development Program of China (973 program grant no. 2010CB731801), the National High Technology Research and Development Program of China (863 key program grant no. 2010AA122202), HKSAR RGC Project no. 447807, and the Research Fund of State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Sciences.

References

  1. Brimicombe A. GIS, environmental modeling and engineering. London, New York: CRC Press; 2009.CrossRefGoogle Scholar
  2. CSDGS and NRC. Understanding the Changing Planet: Strategic Directions for the Geographical Sciences. Washington DC: The National Academies Press; 2010.Google Scholar
  3. Curran O, Shearer A. A workflow model for heterogeneous computing environments. Future Generation Computer Systems. 2009;25(4):414–425.CrossRefGoogle Scholar
  4. Douglas, C.C. and Coen, J.L. (2010). Computational modeling of large wildfires: A roadmap. In: 2010 Ninth International Symposium on Distributed Computing and Applications to Business Engineering and Science (DCABES) (pp. 113-117). Hong Kong, China.Google Scholar
  5. Dudhia, J., Gill, D., Manning, K., Wang, W. and Bruyere, C. (2005). PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users' Guide (MM5 Modeling System Version 3). Retrieved January 5, 2010, from http://www.mmm.ucar.edu/ mm5/documents/tutorial-v3-notes.html.
  6. Foster I. The grid: A new infrastructure for 21st century science. Physics Today. 2002;55(2):42–47.CrossRefGoogle Scholar
  7. Foster, I. and Kesselman, C. (2004). The Grid2: Blueprint for a New Computing Infrastructure (2nd ed.). Kaufmann Publishers, Morgan.Google Scholar
  8. Globus Toolkit (2010). About the Globus Toolkit. Retrieved January 2, 2010, from http://www.globus.org/toolkit/about.html.
  9. Goodchild MF. Geographical information science: fifteen years later. In: Fisher PF, editor. Classics from IJGIS: Twenty years of the International Journal of Geographical Information Science and Systems. Boca Raton: CRC Press; 2006. p. 199–204.Google Scholar
  10. Goodchild MF. Twenty years of progress: Giscience in 2010. Journal of Spatial Information Science. 2010;1:3–20.Google Scholar
  11. Guan Q, Zhang T, Clarke K. GeoComputation in the Grid Computing Age. Lecture Notes in Computer Science: Web and Wireless Geographical Information Systems. 2006;4295(2006):237–246.CrossRefGoogle Scholar
  12. Guan Q, Clarke K. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model. International Journal of Geographic Information Science. 2010;24(5):695–722.CrossRefGoogle Scholar
  13. Han SH, Heo J, Sohn H, Yu K. Parallel processing method for airborne laser scanning data using a pc cluster and a virtual grid. Sensors. 2009;9(4):2555–2573.CrossRefGoogle Scholar
  14. Harris R, Singleton A, Grose D, Brunsdon C, Longley P. Grid- enabling geographically weighted regression: A case study of participation in higher education in England. Transactions in GIS. 2010;14(1):43–61.CrossRefGoogle Scholar
  15. Huang P, Peng H, Lin P, Li X. Static strategy and dynamic adjustment: An effective method for grid task scheduling. Future Generation Computer Systems. 2009;25(8):884–892.CrossRefGoogle Scholar
  16. Li X, Zhang XH, Yeh A, Liu X. Parallel cellular automata for large- scale urban simulation using load-balancing techniques. International Journal of Geographic Information Science. 2010;24(6):803–820.CrossRefGoogle Scholar
  17. Marín Pérez JM, Bernabé JB, Alcaraz Calero JM, Garcia Clemente FJ, Pérez GM, Gómez Skarmeta AF. Semantic-based authorization architecture for grid. Future Generation Computer Systems. 2011;27(1):40–55.CrossRefGoogle Scholar
  18. Openshaw, S. (2000). Geocomputation. In: Stan Openshaw and R.J. Abrahart (Eds.), Geocomputation (pp. 1-32). Taylor and Francis, London and New York.Google Scholar
  19. Plaza AD, Valencia JP, Martinez P. Commodity cluster-based parallel processing of hyperspectral imagery. Journal of Parallel and Distributed Computing. 2006;66(3):345–358.CrossRefGoogle Scholar
  20. Rocks (2010). Rocks cluster distribution. Retrieved June 2, 2010, from http://www.rocksclusters.org.
  21. SAGE (2010). SAGE introduction. Retrieved January 2, 2010, from http://www.sagecommons.org/.
  22. Sugato, B. (2005). Simulation of grid computing infrastructure: challenges and solutions. In: Proceedings of the 37th conference on Winter Simulation (pp. 1773 London and New York: 1780), Orlando, Florida.Google Scholar
  23. Tony H, Stewart T, Krist T, editors. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research: United States of America; 2009.Google Scholar
  24. Torrens, P.M. (2009). Process Models and Next-Generation Geographic Information Technology. Retrieved February 10, 2010, from http://www.esri.com/news/arcnews/ summer09articles/process-models.html.
  25. Turner, D.B. (1994). Workbook of Atmospheric Dispersion Estimates: An Introduction to Dispersion Modeling (2nd ed.). CRC Press, Boca Raton.Google Scholar
  26. Wang SW, Cowles MK, Armstrong MP. Grid computing of spatial statistics: using the TeraGrid for g(i)*(d) analysis. Concurrency and Computation: Practice and Experience. 2008;20(14):1697–1720.CrossRefGoogle Scholar
  27. Xie JB, Yang C, Zhou B, Huang Q. High-performance computing for the simulation of dust storms. Computers Environment and Urban Systems. 2009;34(4):278–290.CrossRefGoogle Scholar
  28. Xu B, Lin H, Chiu LS, Tang S, Cheung J, Hu Y, Zeng L. Vge- cugrid: An integrated platform for efficient configuration, computation, and visualization of mm5. Environmental Modelling and Software. 2010;25(12):1894–1896.CrossRefGoogle Scholar
  29. Yuan, M. and Hornsby, K.S. (2008). Computation and Visualization for Understanding Dynamics in Geographic Domains: A Research Agenda (1st ed.). CRC Press, New York.Google Scholar
  30. Zhang, J. (2010). Towards personal high-performance geospatial computing (HPC- G): perspectives and a case study. In: Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems (pp. 3-10). San Jose, California.Google Scholar

Copyright information

© Capital Publishing Company 2011

Authors and Affiliations

  • Mingyuan Hu
    • 1
  • Hui Lin
    • 1
  • Bingli Xu
    • 2
  • Ya Hu
    • 3
  • Sammy Tang
    • 4
  • Weitao Che
    • 1
  1. 1.Institute of Space and Earth in Information Sciencethe Chinese University of Hong KongHong KongChina
  2. 2.Department of Information EngineeringThe Academy of Armored Forces EngineeringBeijingChina
  3. 3.Remote Sensing Information Engineering DepartmentFaculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengduChina
  4. 4.Information Technology Services Centerthe Chinese University of Hong KongHong KongChina

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