ABSTRACT
Prior to introducing interpolation methods, this chapter expounds the behaviour of regionalised variables whose attribute value is suitable for estimation based on spatial proximity. In particular, the behaviour of a regionalized variable is best appreciated from its semi-variogram. After its structure is explained, the chapter elaborates on its modelling using mathematical equations. The actual spatial interpolation starts with the global method of trend surface analysis, followed by two local interpolators of moving averaging and minimum distance. The strengths, limitations, and the best use of each interpolator are comprehensively evaluated. In comparison with these interpolators, a local-scale estimator known as kriging is presented more extensively and at a greater depth. Apart from ordinary kriging, its other forms are also briefly introduced. In explaining an interpolator, examples are provided in most cases to illustrate how it works mathematically, and how different methods of interpolation fare via case studies.
