ABSTRACT
Spatial information may be viewed in two ways: as coordinates measured with a ruler along imaginary axes; or as the adjacency relationships between objects. The former has been the most common historically, but the latter appears more relevant to the handling of spatially distributed objects in a computer. The interpolation problem, as implemented using weighted-average techniques, depends on the selection of appropriate neighbours to the sample location being estimated. The chapter focuses on weighted-average techniques, as they appear to be the most flexible, and more readily extended to complex situations. The mathematical complexities of achieving this objective with an arbitrary selection of neighbouring data points is considerable. A second moral emerges: selection of the data points and the weighting function are interrelated processes. The insertion of the sample point not only creates a new polygonal region, but it creates this region at the expense of the neighbouring data point regions.
