### 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 language | English |
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Title of host publication | Proceedings of the Joint Conference on Information Sciences |

Editors | J.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar |

Pages | 245-250 |

Number of pages | 6 |

Volume | 6 |

Publication status | Published - 2002 |

Externally published | Yes |

Event | Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 - Research Triange Park, NC Duration: 8 Mar 2002 → 13 Mar 2002 |

### Other

Other | Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 |
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City | Research Triange Park, NC |

Period | 8/3/02 → 13/3/02 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*Proceedings of the Joint Conference on Information Sciences*(Vol. 6, pp. 245-250)

**A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data.** / Chawla, Sanjay; Shekhar, Shashi; Wu, Weili.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the Joint Conference on Information Sciences.*vol. 6, pp. 245-250, Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002, Research Triange Park, NC, 8/3/02.

}

TY - GEN

T1 - A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data

AU - Chawla, Sanjay

AU - Shekhar, Shashi

AU - Wu, Weili

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=1642329906&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1642329906&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:1642329906

SN - 0970789017

VL - 6

SP - 245

EP - 250

BT - Proceedings of the Joint Conference on Information Sciences

A2 - Caulfield, J.H.

A2 - Chen, S.H.

A2 - Cheng, H.D.

A2 - Duro, R.

A2 - Caufield, J.H.

A2 - Chen, S.H.

A2 - Cheng, H.D.

A2 - Duro, R.

A2 - Honavar, V.

ER -