Explosive growth in the size of spatial databases has highlighted the need for spatial data mining techniques to uncover interesting but implicit spatial patterns within these large databases. This book explores the computational structure of the exact and approximate spatial autoregression (SAR) model solutions. Estimating the parameters of the SAR model using Maximum Likelihood (ML) theory is computationally expensive due to the need to compute the logarithm of the determinant of a large matrix in the log-likelihood function. The second part of the book introduces theory on SAR model solutions. The third part applies parallel processing techniques to the exact SAR model solutions. Parallel formulations of the SAR model parameter estimation procedure based on ML theory are examined using data parallelism with load-balancing techniques.