Scale Effect & Spatial Data Aggregation

 This week we learned about scale effect and spatial data aggregation. The effect scale has on vector data, how cell resolution affects raster data, and how measuring compactness of congressional districts can identify gerrymandering. Spatial data aggregation is often used in GIS and data analysis and thus we have to contend with the implication of the modifiable areal unit problem (MAUP), where loss of data can occur. The MAUP has two main issues that concern analysts: the scale effect and the zonation effect.

As the scale decreases, the geometric properties for the hydrographic features decreases as well. Vector data is more detailed the larger the scale is and will include more vertices and/or smaller features.

The DEM for a small coastal watershed in California was reclassified multiple times at different resolutions. As the resolution increases, the level of detail in the DEM decreases. The average slope of the DEM also decreases as the resolution increases.

The compactness of an area is one of the guidelines for drawing congressional districts to minimize oddly shaped areas. I calculated the Polsby-Popper score to determine the compactness of the congressional districts. The closer to 1 correlates with being more compact as the closer to 0 correlates with being less compact. Below is a screenshot of the worst offender, Congressional District 12 in North Carolina, with a PP score of 0.29!

Surface Interpolation

 For this week's module, we learned about different methods to use for surface interpolation. The three different methods explored were Thiessen Polygons, IDW, and Spline (Regularized/Tension). Thiessen interpolation method assigns interpolated value equal to the value found at the nearest sample location. Some advantages of the Thiessen interpolation method are that the polygons are only created once and it’s the easiest method to conceptualize and apply. Some disadvantages are that topography is not considered and boundaries are often oddly shaped (not smooth and continuous like spline). IDW interpolation determines values by using a linear-weighted set of sample points. The weight assigned is a function of the distance from the sample point from output cell location. The further away, the less weight that is assigned to the sample point. The spline interpolation estimates values using a mathematical function that minimizes overall surface curvature and shows smooth surfaces that pass through each sample point.

We used the various surface interpolation methods to explore the water quality conditions in Tampa Bay. The dataset of sample points were gathered and the water quality was determined by measuring the Biochemical Oxygen Demand (BOD) of each sample. After analyzing all of the methods, I chose the Spline with Tension Interpolation method to develop a good description of the BOD concentrations in Tampa Bay. Spline interpolation surfaces are smooth and easier to read than IDW. It appears that the sample points were taken in a uniform distance and amount so I feel better about any distortions skewing the surfaces. This is highlighted between the Spline Regular and the Spline with Tension. Once the two data points that were too close together were moved, it depicted the overall data better.