Applications in GIS Module 1

Grid Overlay

Kernel Density:

Local Moran's I:

This weeks module focused on crime analysis using the grid overlay, kernel density and local Moran's methods. The grid overlay method (image one) involved layering a grid of half mile cells over the city of Chicago, isolating the cells in the top 20% of total homicides in 2017 and using that to compare it to locations of all the homicides in 2018 to determine where homicides where recurring. The kernel density method (image two) creates a density map by searching for clusters of points in an area and assigning points closer to the center of the search area higher weight values. All area total homicides numbering more than three times the average were isolated. Local Moran's I (picture three) determines whether points inside a cell are either clustered or dispersed and then determines whether or not they are statistically significant by comparing each cells value to the cells around it. All cells with high clustering and high statistical significance were isolated for analysis.

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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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