GIS Programming Module 1

Above are the results of the code created in module. The assignment was a combination of writing new script and repairing pre-existing scrip. The first step was to create a list from name and print my last name only using its index number. Following last name you will see a series with the following pattern "X roles a # out of #, X wins" This was the pre-existing code that needed repairs. Once fixed it generates a random number between 0 and a number equal to double the number of characters in each persons name. If they role higher than the length of their name it tells you they won, otherwise it tells you they lose. Next, a list with 20 random numbers is created. Then a number of our choosing, I chose seven, is declared the unlucky number. If that number does not occur in the list it the script tells you so, if the number does occur in the list then all instances of the unlucky number are removed and the program tells you how many were removed. The final step is to print out the list of numbers after the unlucky number has been removed.

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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 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 4

Above is a choropleth map of the population change percentage in North Dakota counties between 2010 and 2014. The colors were chosen to intuitively indicate bad (red) for population decline and good (blue) for a population increase. The legend patches were snapped together to give them an appearance of continuous color that mimic the continuous data in the dataset.

GIS Portfolio - Michael Malone

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