Alec's GIS Blog

 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...

  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 I updated my headline and summary to better reflect my skills and experience, fixed outdated job info, and made sure everything looked clean and professional.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

 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.