%0 eBook
%M Solr-EB000364007
%A Kazar, Baris M.
%I Springer New York
%D 2012
%C New York, NY
%G English
%B SpringerBriefs in Computer Science
%@ 9781461418429
%T Spatial AutoRegression (SAR) Model : Parameter Estimation Techniques
%U https://doi.org/10.1007/978-1-4614-1842-9?nosfx=y
%7 1st ed. 2012
%X Explosive growth in the size of spatial databases has highlighted the need for spatial data mining techniques to mine the interesting but implicit spatial patterns within these large databases. This book explores computational structure of the exact and approximate spatial autoregression (SAR) model solutions. Estimation of the parameters of the SAR model using Maximum Likelihood (ML) theory is computationally very expensive because of the need to compute the logarithm of the determinant (log-det) of a large matrix in the log-likelihood function. The second part of the book introduces theory on SAR model solutions. The third part of the book applies parallel processing techniques to the exact SAR model solutions. Parallel formulations of the SAR model parameter estimation procedure based on ML theory are probed using data parallelism with load-balancing techniques.