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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M1 Lab: Map Critique

This modules lesson topic was the history of cartography and appropriate map design principles. The lab for this module involved looking through many maps to find one well-designed map and one poorly-designed maps in an effort to develop our own individual cartographic design styles that align with the design principles. My two maps and evaluation are as follows. Map Evaluation Template Well-designed map: Synopsis: This map shows the six Wildlife Management Area Game Zones in South Carolina. It can be used Wildlife management professionals or hunters. I think the data to ink ratio has been nearly maximized. The zones being numbered outside the legend is redundant but makes the map easier to read at a glance. There is nothing misleading, the data is substantial, and clutter is minimized to the essentials. For these reasons I think this is a well-designed map. Answer the following questions for the well-designed map: General ▪ ...

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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