Alec's GIS Blog

 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 ...

 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...

  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...

  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...

  I am fortunate that my internship is also my current job. I provide a swath of services to many different departments at the City of Crestview. I look forward to listing some of those out in detail during this process.  To earn credit for my GIS internship, I’ll document tasks I perform, such as creating and maintaining geodatabases, performing spatial analyses, and producing maps for city utility projects. In addition, I will reflect on my experiences through regular blog posts, linking practical work to academic concepts learned in class. These deliverables—combined with any required forms or supervisor evaluations—ensure that my internship meets the course requirements and demonstrates both skill development and professional growth in GIS.  The GIS user group I chose was the Northwest Florida GIS User's Group. While this group does not have a website, they do have a Facebook page you can join. The region they cover is all of northwest Florida. There is no indust...

 In this lab, we analyzed the accuracy and precision of GPS-collected waypoints using ArcGIS Pro and Excel. Part A focused on determining the horizontal and vertical precision of repeated GPS measurements by calculating the average location and measuring how closely the observed points clustered around it. By creating buffers representing 50%, 68%, and 95% of the observations, we could visually assess the spread of the points and quantify the GPS unit’s precision. We then compared the average location to a surveyed reference point to determine horizontal and vertical accuracy, revealing how close the measurements were to the true location and elevation. Horizontal accuracy measures how close the GPS observations are to the true location, while horizontal precision measures how closely repeated observations cluster together, regardless of the true location. Part B extended the analysis to a larger dataset, where we calculated the root-mean-square error (RMSE), mean, median, and vari...