A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data

Sanjay Chawla, Shashi Shekhar, Weili Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A spatial metric, which can be used to systematically calculate the regularization parameter in an MRF formulation of a spatial classification problem was proposed. The standard way to measure classification accuracy is to calculate the percentage of correctly classified objects. Spatial accuracy achieved by classical regression, Spatial Autoregressive Regression (SAR), and Markov Random Field (MRF_hMETIS) was compared using Average Distance to Nearest Prediction (ANDP). It was observed that spatial regression takes two orders of magnitude more computation time relative to MRF_hMETIS approach using the public domain code.

Original languageEnglish
Title of host publicationProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
EditorsJ.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar
Pages245-250
Number of pages6
Publication statusPublished - 1 Dec 2002
EventProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 - Research Triange Park, NC, United States
Duration: 8 Mar 200213 Mar 2002

Publication series

NameProceedings of the Joint Conference on Information Sciences
Volume6

Conference

ConferenceProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
CountryUnited States
CityResearch Triange Park, NC
Period8/3/0213/3/02

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Chawla, S., Shekhar, S., & Wu, W. (2002). A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data. In J. H. Caulfield, S. H. Chen, H. D. Cheng, R. Duro, J. H. Caufield, S. H. Chen, H. D. Cheng, R. Duro, & V. Honavar (Eds.), Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 (pp. 245-250). (Proceedings of the Joint Conference on Information Sciences; Vol. 6).