GIS5935 Module 2.2: Surface Interpolation

    In this lab, we sought to use different interpolation methods to determine which would be the best to most accurately depict the BOD (Biochemical Oxygen Demand) within the Tampa Bay area in Florida. We explored Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension) methods.

Thiessen interpolation advantages include the interpolated surface equaling the values of each given sample point, which means there isn’t a difference between the true and interpolated values. Disadvantages include the abrupt change of between values at the edges of the polygons this interpolation method outputs. While Thiessen interpolation is good for sampled locations, it may not be as accurate around unsampled locations.

IDW averages values and determines the weight based off point cell proximity. This method assumes points closer to one another are more alike and is good for more evenly distributed, uniform, dense points.

Spline interpolation will estimate values that passes through input points and allows for an overall smooth curved surface result.  

              Regularized: High weight values = smoother surface

              Tension: High weight values = coarser surface, with closer conformity to control points

 

 

    If I had to pick one technique to develop a good description of BOD concentrations of Tampa Bay, Florida, I would pick the Spline Tension method depicted above. Points are not evenly distributed at the bottom of the study area, otherwise the Inverse Distance Weighted (IDW) technique may have been more appropriate. Spline Tension appears to reflect the water quality of the area more accurately as it keeps values closer to the data points, as well as maintains a relatively smooth curved surfaces that one may expect from water. 

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