Earth Science Informatics

, Volume 6, Issue 4, pp 199–207 | Cite as

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

  • Andrés García García
  • Carolina Perpiñá
  • Carlos de Alfonso
  • Vicente Hernández
Research Article


This paper presents the integration of desktop grid infrastructure with GIS technologies, by proposing a parallel resolution method in a generic distributed environment. A case study focused on a discrete facility location problem, in the biomass area, exemplifies the high amount of computing resources (CPU, memory, HDD) required to solve the spatial problem. A comprehensive analysis is undertaken in order to analyse the behaviour of the grid-enabled GIS system. This analysis, consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87 % compared with the sequential procedure.


Biomass Desktop grid Geographical information system 


  1. Anderson D (2004) Boinc: a system for public-resource computing and storage. Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing. IEEE Computer Society, Washington DC, pp 4–10Google Scholar
  2. Available scripts webpage:
  3. Campos I et al (2012) Modelling of a watershed: a distributed parallel application in a grid framework. Comput Informat 27(2):285–296Google Scholar
  4. Church RL (2002) Geographical information systems and location science. Comput Oper Res 29:541–562CrossRefGoogle Scholar
  5. Clarke KC (1986) Advances in geographic information systems, computers. Environ Urban Syst 10:175–184CrossRefGoogle Scholar
  6. Dowers S, Gittings BM, Mineter MJ (2000) Towards a framework for high-performance geocomputation: handling vector-topology within a distributed service environment. Comput Environ Urban Syst 24:471–486CrossRefGoogle Scholar
  7. Geograma SL (2009). Teleatlas. Accessed September 2009
  8. GRASS Development Team (2012) GRASS GIS.
  9. Hoekstra AG, Sloot PMA (2005) Introducing grid speedup: a scalability metric for parallel applications on the grid, EGC 2005, LNCS 3470, pp. 245–254Google Scholar
  10. Hu Y et al. (2004) Feasibility study of geo-spatial analysis using grid computing. Computational Science-ICCS. Springer Berlin Heidelberg, 956–963Google Scholar
  11. Huang Z et al (2009) Geobarn: a practical grid geospatial database system. Adv Electr Comput Eng 9:7–11CrossRefGoogle Scholar
  12. Huang F et al (2011) Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Comput Geosci 37:426–434CrossRefGoogle Scholar
  13. Laure E et al (2006) Programming the grid with gLite. CMST 12(1):33–45Google Scholar
  14. Li WJ et al (2005) The Design and Implementation of GIS Grid Services. In: Zhuge H, Fox G (eds) Grid and Cooperative Computing. Vol. 3795 of Lecture Notes in Computer Science 10. Springer, Berlin, pp 220–225Google Scholar
  15. National Geographic Institute (2010) BCN25: numerical cartographic database. Accessed April 2010
  16. Open Geospatial Consortium, Inc (2012) Open GIS Specification Model,
  17. Openshaw S, Turton I (1996) A parallel Kohonen algorithm for the classification of large spatial datasets. Comput Geosci 22:1019–1026CrossRefGoogle Scholar
  18. Perpiñá C, Alfonso D, Pérez-Navarro A (2007) BIODER project: biomass distributed energy resources assessment and logistic strategies for sitting biomass plants in the Valencia province (Spain), 17th European Biomass Conference and Exhibition Proceedings, Hamburg, Germany, pp. 387–393Google Scholar
  19. Perpiñá C et al (2008) Methodology based on Geographic Information Systems for biomass logistics and transport optimization. Renew Energ 34:555–565CrossRefGoogle Scholar
  20. Shen Z et al (2007) Distributed computing model for processing remotely sensed images based on grid computing. Inf Sci 177:504–518CrossRefGoogle Scholar
  21. Spanish Ministry of Agriculture, fisheries and food (2009). Accessed March 2009
  22. Spanish Ministry of Environment (2008). Accessed May 2008
  23. University of California. List of BOINC projects.
  24. Xiao N, Fu W (2003) SDPG: Spatial data processing grid. J Comput Sci Technol 18:523–530CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrés García García
    • 1
  • Carolina Perpiñá
    • 2
  • Carlos de Alfonso
    • 1
  • Vicente Hernández
    • 1
  1. 1.Instituto de Instrumentación para Imagen Molecular (I3M)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Instituto de Ingeniería EnergéticaUniversitat Politècnica de ValènciaValenciaSpain

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