Interpolation is a very important function with ArcGis. It enables to prediction of values throughout a spatial area without having all of the values for that area. It takes points and estimates based on various mathematical and spatial functions what the values of the surrounding point will be. For example, rainfall in Los Angeles county is recorded by 62 different precipitation stations spread throughout the county. ArcGis is able to predict what the rest of rainfall values are by using the various interpolation methods of kriging, Inverse distance weighting, or splining.
In this example, Los Angeles county recent rainfall seems to be higher than the normal county rainfall of Los Angeles county reported over the last few years. However, it is difficult to determine the true difference between recent and normal rainfall based on the maps because each interpolation method gives different highs and lows. Additionally, it could be the case that some places have received more rainfall and some places have received less rainfall on average this season than the normal season rainfall and it is difficult to determine accurately where these places are with the interpolation method although a change can be calculated by subtracting the season total from the season normal. Accuracy needs to be called into question when assessing, however it is important to keep in mind that the interpolation is the only way to produce information for the whole county.
In this example, Los Angeles county recent rainfall seems to be higher than the normal county rainfall of Los Angeles county reported over the last few years. However, it is difficult to determine the true difference between recent and normal rainfall based on the maps because each interpolation method gives different highs and lows. Additionally, it could be the case that some places have received more rainfall and some places have received less rainfall on average this season than the normal season rainfall and it is difficult to determine accurately where these places are with the interpolation method although a change can be calculated by subtracting the season total from the season normal. Accuracy needs to be called into question when assessing, however it is important to keep in mind that the interpolation is the only way to produce information for the whole county.
The methods that I employed to create the three rainfall maps of Los Angeles County were kriging, splining, and inverse distance weighting. The method that I think represented the data the best is the splining method. Splining "estimates values using a mathematical function that minimizes overall surface curvature. This results in a smooth surface that passes exactly through the input points while minimizing the total curvature of the surface. It can predict ridges and valleys in the data" and because of this it is best for representing the smoothly varying surfaces, like temperature (ArcUser, esri.com 2004: 35).
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