Spatial contextual classification and prediction models for mining geospatial data

Shashi Shekhar, Paul R. Schrater, Ranga R. Vatsavai, Weili Wu, Sanjay Chawla

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries that MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.

Original languageEnglish
Pages (from-to)174-188
Number of pages15
JournalIEEE Transactions on Multimedia
Issue number2
StatePublished - Jun 2002
Externally publishedYes


  • Markov random fields (MRF)
  • Spatial autoregression (SAR)
  • Spatial context
  • Spatial data mining


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