Alec's GIS Blog

 I created a GIS portfolio using ArcGIS StoryMaps to showcase my professional work as a GIS Analyst in local government. The portfolio highlights a range of projects demonstrating technical skills, applied GIS workflows, and real-world problem-solving. Using StoryMaps allowed me to combine interactive maps, visuals, and project descriptions in a clean, organized format. Each project includes information on the datasets used, GIS tools and methods applied, and the resulting outputs. Key projects featured in the portfolio include: LiDAR and Elevation Analysis – Creating DEMs, DSMs, and Canopy Height Models for 3D visualization of forest structure. Coastal Flooding & Storm Surge Analysis – Mapping flood zones and estimating structural impacts using high-resolution DEMs and building data. Hurricane Damage Assessment – Evaluating the effect of Hurricane Sandy on coastal structures and analyzing damage patterns relative to coastline proximity. Suitability & Leas...

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 I updated my headline and summary to better reflect my skills and experience, fixed outdated job info, and made sure everything looked clean and professional.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

 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...

 This lab focused on suitability and least-cost analysis to identify the best potential corridors for connecting two protected areas. The workflow combined both vector- and raster-based approaches to evaluate multiple environmental and spatial criteria. The process began by creating individual layers that represented each condition, such as forest cover, slope suitability, proximity to streams, and distance from major highways. These were chosen to reflect ecological needs, like providing continuous natural habitat, and to avoid negative impacts such as fragmentation from roads. In the vector portion, each criterion was processed into a polygon dataset, and the Union tool was used to merge them into a single layer containing all attributes from the different analyses. A query was then run to select only those polygons that satisfied all criteria simultaneously, producing a refined dataset of suitable habitat corridors. This required careful examination of the attribute table to...

In this module we covered damage assessments in ArcGIS Pro. The lab started by creating a map showing the path of hurricane Sandy. We mapped not only the path of Sandy but also the changes it made from a tropical depression to a hurricane and the locations where that took place.  In this section of the lab, I used ArcGIS Pro to analyze structural damage caused by Hurricane Sandy, focusing on how distance from the coastline influenced the severity of damage. The process began by overlaying pre- and post-storm imagery to visually inspect damage, using swipe and flicker tools for quick comparisons. I then calculated the distance of each damaged structure from a digitized coastline using the Near tool. To explore patterns, I grouped structures into distance bins (0–100 m, 100–200 m, and 200–300 m) and summarized the number of structures in each damage category within those ranges. The results showed a strong trend: structures closer to the coast were more likely to be destroyed or seve...

 In this lab, I explored how GIS can be used to model and analyze the impacts of coastal flooding caused by storm surge, using real-world data from Hurricane Sandy in New Jersey and simulated flooding in Collier County, Florida. By working with elevation models (DEMs) and building footprint data, I was able to map potential flood zones and estimate which buildings would be affected by a 1-meter surge. The process involved reclassifying elevation rasters to identify low-lying areas, filtering out isolated depressions not connected to open water, and using spatial joins to determine which buildings intersected the predicted flood areas. One major takeaway was understanding how different data sources (like high-resolution LiDAR vs. traditional USGS DEMs) can lead to different impact estimates—and how those differences can be measured through errors of omission and commission. This lab highlighted how GIS is not just about mapping, but also about making data-driven decisions in emergen...

In module 3 we used ESRI Academy to complete 4 trainings. Those trainings were Introduction to 3D Visualization, Preforming Line of Sight Analysis, Preforming Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages. In these trainings not only do you gain a basic understanding of the topics, but you also get to interact with different tools and learn some tips for practicing the concepts.  Introduction to 3D Visualization In this training, I learned the basics of working with 3D data in ArcGIS Pro. It showed how to create a 3D scene, set up elevation surfaces, and apply 3D symbols to features so they appear realistically in space. The course helped me understand how to view and explore geographic data in three dimensions, which can reveal patterns and relationships that aren’t as obvious in 2D maps. One of the main tools used was the 3D Scene Viewer, which lets you build local or global 3D scenes and visualize how features interact wit...

  In Module 2 we covered the use of LiDAR in ArcGIS Pro. Some of this module served as a refresher from a previous class in the certificate course called remote sensing. This lab gave me a much better understanding of how elevation data is used in forest analysis. The main goal was to process a LAS file to create a Digital Elevation Model (DEM), Digital Surface Model (DSM), and a Canopy Height Model (CHM). Once the CHM was built, I created a 3D view to visualize tree canopy density across the landscape. What stood out most was seeing how LiDAR data can clearly show the height of trees and other features compared to the bare earth surface. The 3D model included things like roads, rivers, and dense forest areas, with visible buildings. This was a enjoyable lab for several different reasons but primarily due to the 3D aspect. It was also helpful seeing a practical use for LiDAR in this setting. I hope to use it more as this class progresses. 

 Crime analysis in ArcGIS Pro involves using spatial data and geoprocessing tools to identify patterns, trends, and hotspots of criminal activity. By integrating crime incident data with demographic and geographic layers, analysts can perform operations like spatial joins, density mapping, and statistical analysis to better understand where and why crimes are occurring. Tools such as Kernel Density, Local Moran’s I, and attribute-based symbology allow users to visualize high-crime areas and evaluate relationships with social or environmental factors. This type of analysis supports informed decision-making for law enforcement. Below are a few of the maps we completed in the crime analysis lab. I used a spatial join to calculate homicide counts within a half-mile grid. The top 20% of grid cells with the most homicides were selected and dissolved into a single hotspot polygon. This method is simple and straight forward but may not capture subtler patterns. The Kernel Density analysis ...

  My name is Alec, and I am currently enrolled in the GIS certificate program at University of West Florida here is a  link   to learn a little more about me. I decided to start this course primarily because I moved into a roll at my place of employment as a GIS Analyst. While I had picked up a good bit of information over the years from experience at previous jobs and through research it became apparent rather quickly that some formal education in the field would be necessary to excel in my position. Thus far this course has been extremely helpful, and I have grown in my knowledge and skills in GIS. My hope is that with the completion of this course I will have the head knowledge as well as the practical application to complete any task necessary in my role as a GIS Analyst. I pay increase and more career opportunities would also be nice.     

 In module 6 we covered Geometries. This module provided a foundational understanding of how geospatial data is represented and managed within computational environments. Geometries serve as the abstract mathematical constructs defining the spatial characteristics of real-world phenomena, enabling their digital portrayal. Gaining insight into the intricacies of these representations, particularly how complex features like rivers are translated into definable shapes, significantly enhanced my conceptual grasp of spatial data modeling and its practical application in mapping. In the lab portion we wrote a TXT file and also covered nested loops. This hands-on part was key to turning what I learned into practical skills, especially for getting specific details from spatial data. The main goal was to use programming to break down geographic datasets and pull out all the coordinates and other info for every point that makes up a feature. Nested loops were super important for going throug...

The lab assignment for module 5 focused on applying data exploration and manipulation. The core of the assignment involved automating several geoprocessing tasks to streamline data management. This was primarily achieved by first establishing a new geodatabase, a central repository for all geospatial data, and then programmatically populating it with existing feature classes from a designated data folder. This initial data handling laid the groundwork for subsequent analysis and ensured a clean and organized workspace for the module's objectives. A key aspect of the data manipulation in Module 5 involved extracting specific information from a feature class and structuring it for further use. This was accomplished by employing a search cursor to iterate through records and selectively retrieve attributes like city names and populations, specifically focusing on "County Seat" cities in New Mexico. This targeted data extraction was then used for populating a Python dictionar...

In Module 4, we explored geoprocessing in ArcGIS Pro, focusing on how Python can automate GIS tasks. Geoprocessing in ArcGIS Pro refers to the framework and tools used for processing and analyzing geographic data. It allows users to perform operations like data conversion, spatial analysis, and data management tasks. Essentially, it transforms geographic data, taking an input, performing an operation, and producing a new output dataset. For the lab in module 4 assignment there were two main parts. First, I used ModelBuilder to create a visual workflow. This model clipped soil data selected specific areas and then erased those areas from a basin. ModelBuilder is a good way to see how geoprocessing tools connect. See the results below. Model  Model Runs Successfully Results  Second, I wrote a Python script in an ArcGIS Pro Notebook. This script processed hospital data by adding XY coordinates, creating buffers, and then dissolving those buffers into a single feature. Key aspects...