Although many fields have in various ways chipped in the birth of spatial analysis as a modern scientific field, automated cartography and surveying can be deliberated to have influenced the most. Other studies with effective contributions included; ethological investigations of animal whereabouts, landscape ecological research of vegetation cover, research on spatial population dynamics and biogeography studies among many others. The work contained herein presents our study on application of computational methods for analysis of spatial data. A huge number of numerous research fields involved in analysis of spatial data make it pretty difficult to classify and exhaustively enumerate all the techniques in the field. Therefore this study avails a number of selected approaches and techniques that are mostly applied in spatial data analysis and modelling. However, among the many techniques, this paper focuses on spatial autocorrelation, spatial interpolation and spatial regression methods in analysing the influence and degree of reliability among observations in a geographic space. We assess the role of predictive variables while explicitly incorporating spatial autocorrelation in the estimation of parameters and testing of hypotheses. The results accessed inspect the effectiveness of these methods and conclude that they are important in the analysis of spatial data fields.