Lab 1: M1.1 Fundamentals

The above layout shows the precision of the data collected by comparing the data to the average point (the star) It was found that the data was precise within 4.5 meters of this location (the yellow area). The data was also found to be accurate within 9.3 meters. Precision was determined by comparing the test waypoints to the average location and determining the distance where 68% of the points fell were within the distance. Accuracy was determined using the same method but rather than using an average location it was determined where 68% of the data was within range of a benchmark location known to be the 'correct' location. With this it was determined that the data is more precise than accurate since the data is clustered more around the average than the reference point.

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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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