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GIS5935 Mod6: Scale Effect and Spatial Data Aggregation

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 In this lab, I had the chance to dive deep into how scale and resolution affect spatial data and how spatial aggregation can impact analysis outcomes. I started with hydrographic vector data for Wake County at multiple scales—from 1:1,200 to 1:100,000. Comparing the total lengths, areas, and perimeters of rivers and lakes, it was clear that larger-scale data captures far more detail, while smaller-scale data tends to generalize features and omit smaller elements. This exercise highlighted how scale influences geometric properties, which is critical when making decisions or interpreting spatial patterns. Next, I examined raster data by working with LIDAR-derived DEMs at various resolutions. As expected, coarser resolutions smoothed out the terrain and lowered the average slope, while finer resolutions captured subtle topographic variations. This illustrated the importance of choosing the right resolution for accurate terrain modeling and slope analysis—too coarse and you lose impor...
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GIS Internship Post #3: LinkedIn update

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  I actually set up my LinkedIn profile years ago, but I never really used it. It was just there—basic info, a photo, and my job history—but I didn’t put much thought into it or engage with anyone. Recently, I decided it was time to give it some attention. I updated my headline and summary to better reflect my skills and experience, fixed outdated job info, and made sure everything looked clean and professional. I haven’t started posting or adding projects yet, but just refreshing my profile feels like a good first step. For me, LinkedIn is now more of a “ready when I need it” tool. Even a little effort to update it makes it feel useful and gives me a profile I can confidently share if an opportunity comes up. You can view my LinkedIn account at this URL https://www.linkedin.com/in/alec-stapp-b412441a8
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GIS5935: Mod5 Surface Interpolation

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 In this lab, we explored different interpolation methods to create continuous surfaces from point data using ArcGIS Pro. Part A focused on generating a Digital Elevation Model (DEM) from elevation points using IDW and Spline interpolation. Comparing the two surfaces showed that Spline produces smoother results but can exaggerate extreme values, while IDW provides a more realistic representation of terrain. Raster calculations highlighted the areas where the two methods differ. Part B applied interpolation to water quality data in Tampa Bay. Thiessen polygons, IDW, and Spline methods were used to create continuous surfaces. Results showed that Spline can overestimate values in areas with sparse sampling, whereas IDW and Thiessen provide more conservative estimates. This lab emphasized the importance of choosing an appropriate interpolation method based on data distribution, density, and analysis goals.
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GIS5935: Mod4 TINs and DEMs

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 In this lab, I explored how different elevation models—TINs (Triangulated Irregular Networks) and DEMs (Digital Elevation Models)—represent terrain and how they can be applied in GIS analysis. The exercises emphasized both visualization and analysis, showing the strengths and limitations of each model. In Part A, I draped a radar image over a TIN of Death Valley, then exaggerated the vertical scale to better highlight subtle landforms. This helped illustrate how 3D visualization can reveal relationships between surface features and elevation patterns. In Part B, I worked with a DEM to build a ski run suitability map. By reclassifying elevation, slope, and aspect, then combining them with weighted values, I created a raster showing areas most suitable for ski runs. Displaying the result in 3D with appropriate symbology highlighted how terrain factors interact in real-world site selection. In Part C, I experimented with TIN symbology, adjusting slope, aspect, edges, and contours to ...
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GIS Internship post 2

 For this assignment, I was asked to reflect on my "dream job" or a job search exercise. I decided to focus on my current position because it truly represents the kind of GIS work I want to be doing. I serve as the GIS Analyst for the City of Crestview, overseeing all GIS operations (right now, that’s just me!). My role is to provide spatial data, analysis, and maps to any city department that needs them, which keeps my work varied and engaging. Completing this assignment reminded me of how much I have learned on the job. I have developed strong skills in ArcGIS Pro and ArcGIS Online, managing geodatabases, running spatial analyses, and producing professional-quality maps. I also integrate data from multiple sources; utilities, parcel data, and transportation networks. One of my key takeaways from this assignment is that GIS is a career of constant growth. The skills I still need to acquire usually reveal themselves when a new challenge arises, and I enjoy the opportunity t...
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GIS5935: Mod3 Data Quality - Assessment

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  In this module, we focused on assessing the quality and completeness of road network data. The main goal was to compare two datasets for Jackson County—Street Centerlines and TIGER Roads—to determine which network provides more comprehensive coverage. Completeness was measured using a simple yet effective method: calculating the total length of roads both across the county and within 1 km² grid cells. This approach allows for a spatially detailed comparison and highlights areas where one dataset may be missing roads. The analysis involved projecting the datasets to a common coordinate system, calculating road segment lengths, and summarizing the totals for each grid cell. From this, we could determine both the overall completeness and the relative differences across the county. Numerical summaries identified which network was more complete in specific areas, while visual mapping of percentage differences highlighted spatial patterns that are not immediately obvious from raw numbe...
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GIS5935 Mod2: Data Quality Standards

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  In our recent GIS lab, we assessed the horizontal positional accuracy of two street datasets — the ABQ_Streets_Sample and the USA Streets dataset — using the National Standard for Spatial Data Accuracy (NSSDA). This standard provides a consistent framework for reporting the accuracy of spatial data by comparing dataset points to high-quality reference points. For each dataset, we selected 20 well-defined check points and calculated the distance between the dataset points and their corresponding reference points. From these distances, we computed the Root Mean Square Error (RMSE), which summarizes the overall error, and then derived the 95% confidence accuracy, which indicates the expected maximum positional error 95% of the time. Our results show that the ABQ_Streets_Sample dataset has an RMSE of 12.38 meters, with a 95% confidence accuracy of 21.44 meters, reflecting a high level of positional precision. The USA Streets dataset, by comparison, has an RMSE of 90.19 meters and a 9...
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