This lab helped me supplement my understanding on how to perform spatial interpolation analysis and which method to choose depending on the data being used. Although I used the same exact data for both sets of my maps, we can see that the results are different depending on which method was used. I used the Inverse Distance Weight method for the first map set and spline method for the second map set. I used a regularized spline method which uses the first, second and third derivative into its minimization calculations.
Both sets of maps show similar rainfall throughout Los Angeles County. The rainfall pattern mapped shows a continuous distribution of rainfall throughout LA County with the south east part typically receiving the most rainfall. There are positive spatial autocorrelations between both sets of maps, which shows a pattern of similarity. However the methods calculated slightly different values for rainfall in the upper area of LA County near the Little Rock automatic rain gauge. The varying values of rainfall could be attributed to how and when the precipitation is measured. Normal rainfall is the average seasonal rainfall for each automatic rain gauge station but the total rainfall is precipitation measured from 10/1/2010 until 3/1/2011 which is roughly 5 months. Therefore if during those five months, the precipitation did not follow the season normal, the data would be different.
Although kringing is also appropriate for this data type and the method uses statistics for advanced prediction surface modeling, I decided to go with IDW and spline because they are easier to compare against each other. IDW and spline are both deterministic methods that create surfaces from samples based on the extent of similarity or smoothing but are slightly different in that IDW uses inexact interpolation(surface pass through none of points) and spline uses exact interpolation(surface pass through each sample point). For this particular map, I believe that IDW is the best method because we have several data points for measuring precipitation in LA County. The IDW method is effective when interpolating sample points based on their distance values since the weight assigned is a function of the distance of an input point from the output cell location. The IDW has an advantage in this situation because rainfall data tends to follow regional patterns. Although the IDW method was better suited for this data type, the spline method was also effective due to creating exact interpolation which created a continuous surface for precipitation, which is useful because rainfall follows regional trends.
Even though spatial interpolation is important to estimate unknown values with known values, we have to use the correct method depending on what data is available. For example, IDW should be employed when a set of points is dense enough to capture the extent of the local surface variation needed for analysis since the greater the distance, the less influence the cell has on the output value. This lab helped me understand the importance of interpolation since it can be useful when there are time, money, and physical constraints. Also interpolation can be used to estimate values for many useful things besides precipitation such as elevation and temperature.
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