You would be less certain if it was raining across town and less confident still about the state of the weather in the next county. For instance, if it is raining on one side of the street, you can predict with a high level of confidence that it is raining on the other side of the street. The assumption that makes interpolation a viable option is that spatially distributed objects are spatially correlated in other words, things that are close together tend to have similar characteristics. There are several well-known interpolation techniques, including spline and kriging. Interpolation is commonly a raster operation, but it can also be done in a vector environment using a TIN surface model. It can be used to predict unknown values for any geographic point data elevation, rainfall, temperature, chemical dispersion, noise level or other spatially-based phenomena. ![]() In another words, interpolation predicts values for cells in a raster from a limited number of sample data points. Interpolation is used because field data are expensive to collect, and can’t be collected everywhere. ![]() We can create either a raster or vector interpolated surface. We use mathematical estimation to “guess at” what the values are “in between” those points. Sample points are locations where we collect data on some phenomenon and record the spatial coordinates. ![]() Interpolation is a process of creating a surface based on values at isolated sample points.
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