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.

GIS 6005 - Module 2

Nevada is my area of Interest. There a three separate state planes for Nevada; east, west, and central. For this reason, the state plane was not the appropriate choice. I decided to use NAD 1983 UTM Zone 11N because Nevada fits entirely within that UTM zone.

GIS 6005 - Module 6

Because the data provided for the above map contained positive and negative values which ArcMap does not support for proportional symbols, the data had to be modified. All states with positive values were exported to a new shapefile and the same was done with states with negative values. This was all that needed done with the positive valued states. However, for the negative valued states a new field needed added to the attribute table and it had to be populated with the absolute value of number of jobs lost to convert them to positive integers. To prepare the data for bivariate visualization classes needed to be made. To do this, three class quantification was applied to each desired variable to divide each into three relatively equal groups. Then all values in the first group of the first variable were classified with an 'A', then the second group was given a 'B', and the third was given a 'C'. the same thing was done with the second variable however 1, 2 ,...

GIS Portfolio - Michael Malone

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