Bivariate interpolation is the process of finding a surface representation based on values measured at 2D points. Its main applications include converting scattered points, profiles, or isolines into a raster format, computing a denser or smoother Triangulated Irregular Network (TIN), and resampling continuous-data rasters, such as elevation, to a higher resolution.
Conditions: Input values are measured at 2D points.; Output is a surface representation, raster, or denser/smoothed TIN.
Bivariate interpolation is the process of finding a surface representation based on values measured at 2D points. Its main applications include converting scattered points, profiles, or isolines into a raster format, computing a denser or smoother Triangulated Irregular Network (TIN), and resampling continuous-data rasters, such as elevation, to a higher resolution.
Conditions: Input values are measured at 2D points.; Output is a surface representation, raster, or denser/smoothed TIN.
Trivariate interpolation differs from bivariate interpolation by operating on values measured at 3D points to produce a 3D raster or volume model, whereas bivariate interpolation uses 2D points to produce a 2D surface. In trivariate interpolation, the sampling may be only partly regular horizontally but scattered vertically, requiring the construction of a continuous volume model from discrete 3D samples.
Conditions: Input values are measured at 3D points.; Output is a 3D raster or volume model.
Trivariate interpolation differs from bivariate interpolation by operating on values measured at 3D points to produce a 3D raster or volume model, whereas bivariate interpolation uses 2D points to produce a 2D surface. In trivariate interpolation, the sampling may be only partly regular horizontally but scattered vertically, requiring the construction of a continuous volume model from discrete 3D samples.
Conditions: Input values are measured at 3D points.; Output is a 3D raster or volume model.