Digital Library

of the European Council for Modelling and Simulation



An Algorithm For Spatial Data Classification And Automatic Mapping Based On "Spin" Correlations


Milan Žukovič, Dionissios T. Hristopulos

Published in:


ECMS 2008 Proceedings

Edited by: Loucas S. Louca, Yiorgos Chrysanthou, Zuzana Oplatkova, Khalid Al-Begain


ISBN: 978-0-9553018-6-5

Doi: 10.7148/2008


22nd European Conference on Modelling and Simulation,

Nicosia, June 3-6, 2008


Citation format:

Zukovic, M., & Hristopulos, D. T. (2008). An Algorithm For Spatial Data Classification And Automatic Mapping Based On “Spin” Correlations. ECMS 2008 Proceedings edited by: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain (pp. 306-312). European Council for Modeling and Simulation. doi:10.7148/2008-0306



We employ the Ising model from statistical physics in the problem of spatial data classification. We use a multiple- class discretization of the sample values. The proposed algorithm predicts the class identity at unmeasured points based on Monte Carlo simulations that are conditional on the observed data (sample). The algorithm aims to mini- mize the deviation between the normalized correlation en- ergies of the sample and the entire domain. A hierarchi- cal scheme is used, in which points predicted to belong in lower-level classes retain their identity in the inference of the higher-level classes. The method is non-parametric and thus suitable for application to non-Gaussian data. The method is investigated using real data of surface elevation over a large part of the territory of North America. The ef- fects of the ratio of training to prediction points, the number of classes, and the initial conditions are investigated. Po- tential extensions of the model are also discussed.

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