interpolation

Reconstruction of the underlying continuous field of data from the limited evidence of the control points, called interpolation, is an example of the classic missing data problem in statistics. Whatever type of surface is involved and whatever control points are used, the objective is to produce a field of values to some satisfactory level of accuracy relative to the intended subsequent use of the data (p. 215). Spatial interpolation is the prediction of exact values of attributes at unsampled locations from measurements made at control points

Topic AM6-2. Identify the spatial concepts that are assumed in different interpolation algorithms; Describe how surfaces can be interpolated using splines; Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging; Differentiate between trend surface analysis and deterministic spatial interpolation; Explain why different interpolation algorithms produce different results and suggest ways by which these can contour-type lines from point datasets using proximity polygons, spatial averages, or inverse d

Determine value of two or more location/place-based distributions (p. 92)