Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of GIS pp 39–45Cite as

Bayesian Network Integration with GIS

  • Reference work entry

Synonyms

Directed acyclic graphs; Probability networks; Influence diagrams; Probabilistic map algebra; Spatial representation of bayesian networks

Definition

A Bayesian Networks (BN) is a graphical-mathematical construct used to probabilistically model processes which include interdependent variables, decisions affecting those variables, and costs associated with the decisions and states of the variables. BNs are inherently system representations and, as such, are often used to model environmental processes. Because of this, there is a natural connection between certain BNs and GIS. BNs are represented as a directed acyclic graph structure with nodes (representing variables, costs, and decisions) and arcs (directed lines representing conditionally probabilistic dependencies between the nodes). A BN can be used for prediction or analysis of real world problems and complex natural systems where statistical correlations can be found between variables or approximated using expert opinion....

This is a preview of subscription content, log in via an institution.

Recommended Reading

  1. Ames, D.P.: Bayesian Decision Networks for Watershed Management. Utah State University, Logan, UT (2002)

    Google Scholar 

  2. Ames, D.P., Neilson, B.T., Stevens, D.K., Lall, U.: Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study. IWA Publishing, J. Hydroinform. (2005)

    Google Scholar 

  3. Borsuk, M.E., Reckhow, K.H.: Summary Description of the Neuse Estuary Bayesian Ecological Response Network (Neu-BERN). (2000) http://www2.ncsu.edu/ncsu/CIL/WRRI/neuseltm.html (Dec. 26, 2001)

  4. Haas, T.C.: Modeling waterbody eutrophication with a Bayesian belief network. School of Business Administration, Working Paper. University of Wisconsin, Milwaukee, Wisconsin (1998)

    Google Scholar 

  5. Heckerman, D.: Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1, 79–119. MapWindow Open Source Team (2007). MapWindow GIS 4.3 Open Source Software, Accessed February 06, 2007 at the MapWindow Website: http://www.mapwindow.org/ (1997)

  6. Kuikka, S., Varis, O.: Uncertainties of climate change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environ. Res. 2, 109–128 (1997)

    Google Scholar 

  7. Lee, D.C., Bradshaw, G.A.: Making Monitoring Work for Managers: Thoughts on a conceptual framework for improved monitoring within broad-scale ecosystem management. (1998) http://icebmp.gov/spatial/lee_monitor/preface.html (Dec. 26, 2001)

  8. Norsys Software Corp.: Netica Bayesian Belief Network Software. Acquired from http://www.norsys.com/ (2006)

  9. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  10. Stassopoulou, A., Petrou, M., Kittler, J.: Application of a Bayesian network in a GIS based decision making system. Int. J. Geograph. Info. Sci. 12(1) 23–45 (1998)

    Article  Google Scholar 

  11. Stassopoulou, A., Caelli, T.: Building detection using Bayesian networks. Int. J. Pattern Recognit. Artif. Intell. 14(6), 715–733 (2000)

    Article  Google Scholar 

  12. Taylor, K.J.: Bayesian Belief Networks: A Conceptual Approach to Assessing Risk to Habitat, Utah State University, Logan, UT (2003)

    Google Scholar 

  13. Varis, O., Kuikka, S.: An influence diagram approach to Baltic salmon management. Proc. Conference on Decision Analysis for Public Policy in Europe, INFORMS Decision Analysis Society, Atlanta, GA (1996)

    Google Scholar 

  14. Walker, A., Pham, B., Maeder, A.: A Bayesian framework for automated dataset retrieval. In: Geographic Information Systems, mmm, p. 138. 10th International Multimedia Modelling Conference (2004)

    Google Scholar 

  15. Walker, A., Pham, B., Moody, M.: Spatial Bayesian learning algorithms for geographic information retrieval. In: Proceedings 13th annual ACM international workshop on Geographic information systems, pp. 105–114. Bremen, Germany (2005)

    Google Scholar 

  16. Shachter, R., Peot, M.: Decision making using probabilistic inference methods. In: Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pp. 275–283 (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geosciences, Geospatial Software Lab, Idaho State University, Pocatello, ID, USA

    Daniel P. Ames & Allen Anselmo

Authors
  1. Daniel P. Ames
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allen Anselmo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Shashi Shekhar (McKnight Distinguished University Professor) (McKnight Distinguished University Professor)

  2. Management Science and Information Systems Department, Rutgers Business School Newark and New Brunswick Rutgers, the State University of New Jersey, Newark, NJ, 07102, USA

    Hui Xiong (Assistant Professor) (Assistant Professor)

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Ames, D., Anselmo, A. (2008). Bayesian Network Integration with GIS. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_95

Download citation

Publish with us

Policies and ethics

Search

Navigation