Lab 6: M 3.1 Scale & Spatial Data Aggregation

This final lab focused on the way scale and spatial data aggregation can affect the quality of data. The resolution of raster data can greatly affect the variation seen in the final results, reducing the resolution tends to smooth out data resulting in more consistent results through generalization. Altering the scale of vector data can diminish the impact of data depending on the way it's aggregated. Specific data sets that may stand out on their own may be diminished when combined with larger areas making the final impact negligible, this is most famously seen in gerrymandering when political districts are redrawn, sometimes in outlandish fashion, in order to give a specific party a political advantage. This is done by splitting up areas that may contain high density of one party into areas that contain high densities of the opposing party, essentially negating the impact of the opposing votes. The outlandishness of the districts can be quantified using the Polsby-Poplar score which compares the shape of the district to a circle and returns a score between 0 and 1. 0 being the least compact and 1 being the most compact. Below can be seen an example of a district with a low Polsby-Poplar Score.

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Lab 5: M 2.2 Interpolation

This weeks module focused on identifying the best interpolation method for modeling the air quality over Tampa Bay. Four methods were tested using the same set of sample points Thiessen, Inverse Weighted Distance (IDW), Tensioned Spline (seen above), Regularized Spline. Thiessen Interpolation assigns all cells in the raster with the value of the nearest sample point. IDW calculates the value of all cells by considered multiple sample points nearby and giving closer points a higher weight than further points. Both Spline methods create a smooth surface over the sample points but the regularized version creates a smooth curvature regardless of the range of values in the sample meaning cell values can end up both above and below the minimum and maximum values found in the sample. The tension model attempts to fix this by constricted the curvature of values to the ranges found in the sample points.

Module 2 Lab: Land Use / Land Cover Classification, Ground Truthing and Accuracy Assessment

The above map shows digitized land use/land cover (LULC) classifications for Pascagoula, MS. A set of 30 random sampling points were created post creation of the LULC layer and they were verified in google street view to determine accuracy. Based on these points the LULC model was found to be 86.6% accurate.

Applications in GIS Module 2

This weeks module focused on using LiDAR data to determine the height and density of a section of Shenandoah National Forest in Virginia. Mostly this involved converting the LiDAR data to a raster and running various tools to achieve the desired datasets. New tools included LAS Dataset To Raster, LAS to MultiPoint, Con, Plus, Float, and Divide. Additionally a histogram was created to display the heights of cells in the forest. Density and DSM: Height with Graph: LiDAR:

GIS Portfolio - Michael Malone

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