Empirical Bayesian Kriging (EBK) Interpolation
Kriging is a special form of interpolation in which one knows about a directional bias within the data and finds spatial correlations. This type of kriging specifically takes error into account more than other kriging methods and is therefore more accurate.
The image itself uses CA point data to interpolate temperatures across the entire state allowing us to find the highest and lowest average temperatures for the given month. It furthermore highlights large cities of interest and their respective temperatures.
Inverse Distance Weighted (IDW) Interpolation
Inverse Distance Weighted (IDW) interpolation differs from kriging in that it uses known points to calculate unknown points rather than finding correlations. It assumes correlations are proportional to distances.
The image itself is similar to the one above in that it interpolates temperature data and highlights specific cities of interest, however this was done for a different month of the same year.