InterpMapper turns scatter data to continuous 2D grids, relying on various interpolation methods (non-parametric, inverse distance weighting and ordinary kriging).

That also lets users to perform data analysis and filtering with following facilities:

– Draw exclusion polygons to evict outliers,

– Define minimum and maximum cut-offs to filter out extreme values,
– Rotate the scatter (especially designed for inverse-distance weighted interpolation to save computation power, and for ordinary kriging to enable handling of anisotropy),
– Select polygons to be used as interpolation barriers when dealing with inverse-distance weighted interpolation.

Ordinary kriging facilities allow users to:

– Compute automatically residues by giving the drift order,
– Choose kriging with external drift,
– Consider geometric anisotropy Vs isotropy with variographic analysis and fit with different kinds of variogram models,
– Consider unique Vs moving neighborhood,
– ...

Post processing (i.e., cropping, 2D-median filtering, …) of interpolation results could then be easily achieved with the Global Viewer.