# What is Spatial Interpolation: Inputs to Spatial Interpolation (Especially for GATE-Geospatial 2022)

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Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data: elevation, rainfall, chemical concentrations, noise levels, and so on.

On the left is a point dataset of known values. On the right is a raster interpolated from these points. Unknown values are predicted with a mathematical formula that uses the values of nearby known points.

In this example the input points happen to fall on cell centers. One problem with creating rasters by interpolation is that the original information is degraded to some extent - even when a data point falls within a cell, it is not guaranteed that the cell will have the same value.

Spatial Interpolation is the process of using points with known values to estimate values at other points. Through Spatial Interpolation, we can estimate the precipitation value at a location with no recorded data by using known precipitation readings at nearby weather stations.

## Methods and Inputs of Spatial Interpolation

Spatial Interpolation covers a variety of method including trend surface models, thiessen polygons, kernel density estimation, inverse distance weighted, splines, and Kriging. Spatial Interpolation requires two basic inputs:

- Control Points
- Choice of Spatial Interpolation