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Interpolation

In this assignment, we learned about four different interpolation methods: Thiessen, IDW, and Spline Regularized/Tension. We used these methods to analyze the Biochemical Oxygen Demand (BOD) in milligrams per Liter in the Tampa Bay of Florida. 

The first method, Thiessen interpolation, is a widely used method mainly because of its ease of use and high accuracy when using a large sampling density. Because this method assigns a value based on the nearest sample point, it does not do well with continuous data. However, because of this same attribute, this method can be highly beneficial when dealing with data that is oddly shaped or ends abruptly.

Model 1: Thiessen

IDW interpolation draws in the theory that points closer to each other are more alike. 

Model 2: IDW

The biggest difference in the Spline layer from the other two methods is it has a much smoother expression. This makes sense as the spline interpolation runs through each data point and aims to smooth out the surface elevation. In this step, I encountered an error in the model. A section of the Tampa Bay model showed a high level of BOD that did not make sense. This issue is discussed further below. 

Model 3: Spline

A high BOD concentration signifies that there is less oxygen in the environment due to decomposing organisms and a sign of low water quality. Because the area noted below is a natural park and wetland area this can make sense; assuming that decaying biological fauna and folia are present in the area and causing the low water quality. However, because there are also plenty of live plants to add oxygen back to the environment, I would have to question the health of this local wetland ecosystem. This would also explain the lack of sampling points if this is a protected area. That being said, because spline interpolation runs through every point of data, aims to create a smooth surface, and there are no sharp slope changes in Tampa Bay it makes sense that it would make this area relatively high (if drawing solely on points nearby) and create a false positive.  



I suspect that the error seen in the Tampa Bay estuary is also due to the fact that points 25 and 29 are very close to each other with significantly different numbers. Spline aims to create a smooth surface and having two very close points with different values [2.2 and 1.16] confused the interpolation. I first decided to delete point 25 [2.2] to get rid of this error. However, when I used the Spline tool it would not run and stated there was not enough points. So, I then changed the value of point 25 to that of 29 [1.16]. This drastically changed the Spline outcome as seen below. 


Moving forward, I would choose the IDW model because it bases the model on the assumption that points closer to each other are more related. This model makes sense in Tampa Bay because there may be many changes due to boat traffic, estuary outputs, and environmental runoff. 


 





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