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...
In this lab, I created a supervised land-use classification of Germantown, Maryland, to visualize current land-cover patterns and support sustainable planning. Maryland’s population has grown 30% over the last 30 years, while land consumption has doubled, making it critical to understand how urban and green spaces have changed. I used the dataset and selected a band combination that minimized spectral confusion between classes. Using histograms and mean plots, I created and refined spectral signatures for urban areas, roads, water, grass, deciduous and mixed forests, fallow fields, and agriculture. Adjusting parameters like Spectral Euclidean Distance and Neighborhood helped ensure each signature accurately represented its feature. The final map, recoded into eight classes, includes the classified image, a distance file inset, a legend with colors, class names, and areas, plus essential map elements. This project provided hands-on experience in creating accurate, meaningful land-...
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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