Weight of Evidence in Geospatial Analysis

  • Rajesh Bahadur Thapa


Weight of evidence (WofE) is a quantitative method for combining evidence in support of a hypothesis. An evidence-based approach involves an assessment of the relative values of different pieces of information that have been collected in previous steps. ECHA (2010) defines WofE as “the process of considering the strengths and weaknesses of various pieces of information in reaching and supporting a conclusion.” A representative value needs to be assigned to each piece of information using a formalized weighting procedure. The evidence can be called as a factor, and can often influence the weight given owing to the quality of the data, the consistency of results, the nature and severity of effects, and the relevance of the information.


Landslide Susceptibility Landslide Susceptibility Mapping Land Change Landslide Susceptibility Index Fuzzy Probability 
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.


  1. Almeida CM, Batty M, Monteiro AVM, Camara G, Soares-Filho BS, Ceroueira GC, Pennachin CL (2003) Stochastic cellular automata modelling of urban land use dynamics: empirical development and estimation. Comput Environ Urban Syst 27:481–509CrossRefGoogle Scholar
  2. Aspinall RJ (1992) An inductive modeling procedure based on Bayes’ theorem for analysis of pattern in spatial data. Int J Geogr Inform Syst 6:105–121CrossRefGoogle Scholar
  3. Bonham-Carter G (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon, New YorkGoogle Scholar
  4. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Am Soc Photogram Rem Sens 54:1585–1592Google Scholar
  5. Cheng Q, Agterberg FP (1999) Fuzzy weights of evidence method and its application in mineral potential mapping. Nat Resour Res 8:27–35CrossRefGoogle Scholar
  6. Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324CrossRefGoogle Scholar
  7. Dilts TE, Sibold JS, Biondi F (2009) A weights-of-evidence model for mapping the probability of fire occurrence in lincoln county, Nevada. Ann Assoc Am Geogr 99:712–727CrossRefGoogle Scholar
  8. ECHA (European chemical Agency) (2010) Practical guide 2: how to report weight of evidence. Accessed 4 Mar 2011
  9. Good IJ (1950) Probability and the weighing of evidence. C. Griffin, LondonGoogle Scholar
  10. Good IJ (1979) Studies in the history of probability and statistics: A. M. Turing’s statistical work in World War II. Biometrika 66:393–396CrossRefGoogle Scholar
  11. Kemp LD, Bonham-Carter GF, Raines GL (1999) Arc-WofE: arcview extension for weights of evidence mapping. Accessed 7 Mar 2011
  12. Masetti M, Poli S, Sterlacchini S (2007) The use of the weights-of-evidence modeling technique to estimate the vulnerability of groundwater to nitrate contamination. Nat Resour Res 16:109–119CrossRefGoogle Scholar
  13. Peirce CS (1878) The probability of induction. Popular Science Monthly 12:705–718. Reprinted (1956) in Newman JR (ed) The world of mathematics, vol 2. Simon and Schuster, New York, pp 1341–1354Google Scholar
  14. Pradhan B, Oh H, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1:199–233CrossRefGoogle Scholar
  15. Raines GL, Bonham–Carter G, Kemp L (2000) Predictive probabilistic modeling: using ArcView GIS. ArcUser, AprilJune, pp 45–48.
  16. Romero-Calcerrada R, Luque S (2006) Habitat quality assessment using weights-of-evidence based GIS modelling: the case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest. Ecol Model 196:62–76CrossRefGoogle Scholar
  17. Romero-Calcerrada R, Millington JDA (2007) Spatial analysis of patterns and causes of fire ignition probabilities using logistic regression and weights-of-evidence based GIS modeling. Geophys Res Abstr 9:01337Google Scholar
  18. Soares-Filho BS, Alencar A, Nespad D, Cerqueira GC, Dial M, Del C, Solozarno L, Voll E (2004) Simulating the response of land-cover changes to road paving and governance along a major Amazon Highway: the Santarem–Cuiaba corridor. Glob Chang Biol 10:745–764CrossRefGoogle Scholar
  19. Spiegelhalter DJ (1986) Probabilistic prediction in patient management and clinical trials. Stat Med 5:421–33CrossRefGoogle Scholar
  20. Thapa RB, Murayama Y (2011) Urban growth modeling of Kathmandu metropolitan region, Nepal. Comput Environ Urban Syst 35:25–34CrossRefGoogle Scholar

Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Earth Observation Research Center, Space Applications Mission DirectorateJapan Aerospace Exploration Agency (JAXA)TsukubaJapan
  2. 2.Division of Spatial Information Science, Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

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