This week was a good intro to the program and a good start to getting used to how the system works. It will just take more practice to get used to where things are and what they do.
I began my internship with the Orange County Utilities Department , where I support GIS staff on various projects related to mapping requests, data review, and field operations. This internship gives me exposure to how GIS supports real-world utilities, infrastructure, and planning decisions that directly affect residents and public services in the county. To earn academic credit for this internship, I will be completing the required course components, including the internship work plan, weekly work activities, professional development assignments, participation check-ins, and the final reflection and documentation needed at the end of the semester. As part of the professional engagement portion of this course, I also selected a GIS-related professional user group to join. I chose AWRA Florida (American Water Resources Association) , which focuses on water resources science, management, and policy throughout the state. AWRA Florida offers technical meetings, networking oppo...
This week’s assignment was to create a choropleth map showing the population densities of countries in Europe and then using proportional symbols to visualize the total wine consumption in those countries. Two of the data classification schemes that I used for this map are the color scheme and the Quantile scheme. I chose the yellow-green (5 classes) color scheme because I like the way green represents the data and highlights the countries, and the greens are calm and easy to look at. I chose the Quantile scheme to represent the data because all of the classes are visible on the map and it spreads the color nicely around the map, making it look better than the other monotone classification schemes. There are 5 classes represented in the Quantile scheme and all of them have at least a couple countries in them which makes the class sizes just right, and makes it easy for the viewer to understand and differentiate the data. ...
T his week’s assignment focused on evaluating the precision and accuracy of GPS data . To do this, we worked with a set of waypoints mapped multiple times at the same location, then calculated an average position and compared it to a known reference point. By creating buffers around the average location and analyzing both horizontal and vertical errors, we explored how GPS measurements vary and how close they come to the true position. This exercise helped demonstrate the difference between accuracy (closeness to truth) and precision (closeness of repeated measurements), as well as the limitations of consumer-grade GPS receivers. The map shows the projected GPS waypoints, the calculated average location, and circular buffers representing the 50%, 68%, and 95% precision thresholds. These buffers illustrate how far most points fall from the average location, helping visualize the spread of the data. Results: Horizontal Precision (68%): 6.0 m Horizontal Accuracy: 4.3 m Vert...
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