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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Module 5 - Unsupervised and Supervised Image Classification

This weeks module focused on classifying images using multispectral signatures. Above you can see the completed classified land cover of Germantown, Maryland. To create this image above signatures were collected that correlated to each required feature. Then bands were chosen (R:4 G:6 B:5) that contained the largest separation amongst features to minimize spectral confusion. In the above image roads and urban areas were often confused leading to a much larger area of roads than actually exist. The inset map contains a classification distance map which displays the distance each cell is (spectrally) from the sample points with brighter pixels being further than darker pixels. This indicates that brighter areas have a higher chance of error.

Lab 3: M 1.3 Assessment

Lab 2: M1.2 Standards

The task for this module was to perform an accuracy assessment on two versions of the same road network in Albuquerque. One was mapped by the city of Albuquerque (yellow) and one by Street Map USA (red). In order to perform the accuracy assessment twenty points per network needed created at matching intersections on each network. Then a reference point was digitized based on satellite imagery to represent the "actual" location for each intersection. XY coordinates were assigned to all sixty of the created points and the values were exported to an excel spreadsheet. From there the RMSE for each of the networks were calculated using the reference points. With this the accuracy assessment was completed resulting in the following: ABQ_Streets Positional Accuracy: Tested 14.84 feet horizontal accuracy at 95% confidence level. StreetMapUSA Positional Accuracy: Tested 158.56 feet horizontal accuracy at 95% confidence level.

GIS Portfolio - Michael Malone

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