Encyclopedia of GIS

2017 Edition
| Editors: Shashi Shekhar, Hui Xiong, Xun Zhou

Bayesian Network Integration with GIS

  • Daniel P. Ames
  • Allen Anselmo
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-17885-1_95



A Bayesian network (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...

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  1. Ames DP, Neilson BT, Stevens DK, Lall U (2005) Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study. J Hydroinform 7:267–282. IWA PublishingGoogle Scholar
  2. Borsuk ME, Reckhow KH (2000) Summary description of the Neuse estuary Bayesian ecological response network (Neu-BERN). http://www2.ncsu.edu/ncsu/CIL/WRRI/neuseltm.html. 26 Dec 2001
  3. Haas TC (1998) Modeling waterbody eutrophication with a Bayesian belief network. Working paper, School of Business Administration, University of Wisconsin, MilwaukeeGoogle Scholar
  4. Heckerman D (1997) Bayesian networks for data mining. Data Mining Knowl Discov 1:79–119. MapWindow Open Source Team (2007). MapWindow GIS 4.3 Open Source Software. Accessed 06 Feb 2007 at the MapWindow Website: http://www.mapwindow.org/
  5. Kuikka S, Varis O (1997) Uncertainties of climate change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environ Res 2:109–128Google Scholar
  6. Lee DC, Bradshaw GA (1998) Making monitoring work for managers: thoughts on a conceptual framework for improved monitoring within broad-scale ecosystem management. http://icebmp.gov/spatial/lee_monitor/preface.html (26 Dec 2001)
  7. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  8. Shachter R, Peot M (1992) Decision making using probabilistic inference methods. In: Proceedings of the eighth conference on uncertainty in artificial intelligence, Stanford, pp 275–283Google Scholar
  9. Stassopoulou A, Petrou M, Kittler J (1998) Application of a Bayesian network in a GIS based decision making system. Int J Geograph Inf Sci 12(1):23–45CrossRefGoogle Scholar
  10. Taylor KJ (2003) Bayesian belief networks: a conceptual approach to assessing risk to habitat. Utah State University, LoganGoogle Scholar
  11. Varis O, Kuikka S (1996) An influence diagram approach to Baltic salmon management. In: Proceedings of the conference on decision analysis for public policy in Europe, INFORMS decision analysis society, AtlantaGoogle Scholar
  12. Walker A, Pham B, Maeder A (2004) A Bayesian framework for automated dataset retrieval. In: Geographic information systems. 10th International Multimedia Modelling Conference (MMM), Brisbane, p 138Google Scholar
  13. Walker A, Pham B, Moody M (2005) Spatial Bayesian learning algorithms for geographic information retrieval. In: Proceedings 13th annual ACM international workshop on geographic information systems, Bremen, pp 105–114Google Scholar

Recommended Reading

  1. Ames DP (2002) Bayesian decision networks for watershed management. Utah State University, LoganGoogle Scholar
  2. Norsys Software Corp (2006) Netica Bayesian belief network software. Acquired from http://www.norsys.com/
  3. Stassopoulou A, Caelli T (2000) Building detection using Bayesian networks. Int J Pattern Recognit Artif Intell 14(6):715–733CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel P. Ames
    • 1
  • Allen Anselmo
    • 1
  1. 1.Department of Geosciences, Geospatial Software LabIdaho State UniversityPocatelloUSA