GIS4930 - Surfaces - TINs & DIMs

 

Comparison of elevation contours derived from a Triangulated Irregular Network (TIN) and a Digital Elevation Model (DEM) 

In this lab we explored how terrain is modeled and visualized in ArcGIS Pro using both TINs and DEMs. We used a TIN as the ground surface in a Local Scene, applied vertical exaggeration, and draped imagery to highlight relief with scene lighting. From a DEM we derived slope and aspect, reclassified elevation/slope/aspect, and combined them into a ski-run suitability map (weights: 40% elevation, 35% slope, 25% aspect). We then compared contours from the TIN to contours from a Spline-based DEM at the same interval: DEM contours appeared smoother, while TIN contours showed more faceting along triangle edges. Overall, the exercise showed how data model choice affects downstream products and interpretation, and why unit settings (Z-factor), exaggeration, and lighting matter for clear 3D communication.

GIS Internship - GIS Job Market Research

This week, I spent some time exploring the GIS job market and thinking about where I see myself down the road. The assignment asked us to search for a GIS job posting and reflect on how it connects to the skills we’ve built throughout the program, and I was surprised at how eye-opening this process turned out to be.

I came across a position for a Senior Geospatial Intelligence Analyst with CACI, supporting U.S. Special Operations Command. The role focuses on advanced imagery analysis, like LiDAR, multispectral, and satellite data, to create intelligence products that directly support missions. This isn’t an entry-level role — it’s the kind of work that blends advanced technical skills with real-world impact. It also represents the kind of meaningful and challenging career I’d like to grow into while building a stable and financially secure future.

What really stood out to me during this search was realizing how much of what we’ve learned already connects to these higher-level positions. Skills like ArcGIS Pro, Python scripting, LiDAR analysis, 3D modeling, and cartographic design are all part of what I’ve gained through my coursework and internship. My final course, Photo Interpretation and Remote Sensing, will take that a step further by giving me hands-on experience with multispectral datasets and advanced imagery workflows — a key requirement for jobs like this.

Of course, I also saw where I need to grow. Roles like this require a TS/SCI security clearance, more experience applying GIS to real-world intelligence operations, and a bachelor’s degree, which I plan to pursue (I’m currently considering Data Analytics through WGU).

Overall, this assignment helped me connect the dots between where I am now and where I want to be. It made me realize that all of my work so far has been worth it — every class, lab, and project has been a building block toward something bigger. Even though there’s still a lot to learn, I can see a clear path forward, and that’s both exciting and motivating.

Job Post

GIS4930 - Data Accuracy - Assessment of Road Network Data Completeness

In this lab, we examined road network completeness in Jackson County, Oregon, by comparing the locally maintained Street Centerlines to the US Census TIGER/Line (2000). The goal of the accuracy assessment was simple: measure how fully each dataset captures the real street network, not by point accuracy, but by total road length.

I projected TIGER to the centerlines’ projected CRS, then intersected each road layer with a 5×5 km grid so only the parts inside each cell counted and any segments crossing cell boundaries were split. After intersecting (when geometry changes), I recalculated segment lengths in kilometers, summarized the totals by grid cell for each dataset, and joined those totals back to the grid. To compare them, I computed a percent difference using Centerlines as the base: $\% \text{Diff} = \frac{\text{Centerlines} - \text{TIGER}}{\text{Centerlines}} \times 100$. Positive values mean Centerlines are more complete; negative values mean TIGER is more complete.

In short, a simple length-based metric provided a transparent way to evaluate completeness: TIGER was more complete overall by total length, but the grid map revealed many local areas where the county centerlines exceeded TIGER.

GIS4930 - Data Quality - Data Standards

Map view in ArcGIS Pro showing the Albuquerque City Street Dataset and the 20 digitized test points, placed at well-defined street intersections for use in the positional accuracy analysis.

We continued our journey into data standards this week with Lab 1.2, building directly on concepts of precision and accuracy from last week’s work. Instead of focusing on GPS waypoints, this exercise shifted our attention to testing the horizontal positional accuracy of two full street datasets: a locally maintained city street layer and the broader national StreetMapUSA dataset.

High-resolution orthophotos served as the independent reference data for this project. I digitized 20 control points directly on the imagery, carefully following NSSDA guidelines to ensure they were evenly distributed across the study area and spaced far enough apart to avoid clustering.

Because the orthophotos were massive and demanding on computer performance, I used a mosaic dataset, a skill I learned in the Applications in GIS course at UWF. Building the mosaic allowed me to seamlessly manage and display the imagery, drawing only what was needed in the map window. This not only kept ArcGIS Pro running smoothly but also made the digitizing process far more efficient and precise.

Once the reference points were complete, I snapped matching points to the centerlines in both the city and StreetMapUSA datasets. From there, I exported all three datasets to Excel, where I calculated coordinate differences (ΔX, ΔY), root mean square error (RMSE), and ultimately converted those values into Accuracy_r(95%), the standard NSSDA measure of positional accuracy.

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Results

The results highlighted a striking difference between the two datasets:

City Dataset (Alb):
Tested 16.94 feet horizontal accuracy at 95% confidence level (NSSDA).

StreetMapUSA Dataset:
Tested 409.01 feet horizontal accuracy at 95% confidence level (NSSDA).

The city’s dataset proved to be highly reliable, with 95% of features falling within roughly 17 feet of their true location. In contrast, StreetMapUSA’s much larger error of over 400 feet made it unsuitable for parcel-level or detailed mapping applications.

GIS Internship: Santa Rosa County Property Appraiser's Office

 This semester, I am completing my internship with the Santa Rosa County Property Appraiser's Office (SRCPA) in Milton, Florida, as part of my undergraduate GIS certificate program at the University of West Florida. My role primarily revolves around cadastral mapping, where I assist with projects such as converting the county's parcel database to Esri’s ArcGIS Parcel Fabric, georeferencing historical aerial imagery, and improving data quality for land records management.

I have been in this position for six weeks now and have really enjoyed the experience. Although the commute is about an hour each way, I genuinely enjoy the drive. Milton reminds me of my hometown, which makes the trip something I look forward to.

One of my first challenges was learning how to work with ArcGIS Pro mosaic datasets, a tool my office hadn’t used before. Our team was unsure how to crop georeferenced images efficiently, so I took the initiative to research solutions independently. Through this process, I learned how to use footprints for cropping, perform color balancing, and export GeoTIFF files, significantly improving our workflow.

Currently, I am preparing to process the historical aerial images of Santa Rosa County that I have georeferenced in GDAL. These mosaics will be finalized and published to the SRCPA website, where they will be made publicly available for residents, businesses, and other stakeholders.

As part of this course, I have also joined the Florida Association of Cadastral Mappers (FACM), a professional organization focused on cadastral mapping across the state of Florida. Membership is affordable, with a lifetime fee of just $5 for students or $40 for professionals, making it a valuable resource for networking and professional development. Since cadastral mapping is at the core of my internship work, this group is an excellent fit and will allow me to learn from other professionals in the field.

For more information on the FACM, visit their website: https://www.facm-online.org/

GIS4930 - Data Quality - Fundamentals

We kicked off the Special Topics in GIS course with a lab focused on data accuracy and precision, two important concepts for evaluating GPS data quality. In this exercise, we worked with a set of 50 waypoints collected using a handheld GPS unit. The goal was to figure out how tightly the points clustered together (precision) and how close they were to a known reference point (accuracy). Using ArcGIS Pro, we calculated both horizontal and vertical precision, created buffer zones to visualize the spread of the data, and compared our results to the reference point. To wrap up, we explored error metrics and built a cumulative distribution function (CDF) to get a clearer picture of how GPS errors are distributed across the dataset.

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Horizontal Accuracy: 3.24 m
Horizontal Precision (68%): 4.5 m

Horizontal precision is a measure of how closely repeated GPS measurements align with one another. In this lab, it was calculated as the distance within which 68% of the collected waypoints fall.

Horizontal accuracy measures how close the average GPS location is to a known reference point. This was determined by comparing the average waypoint location to a surveyed reference point.

In simple terms:

• Precision = consistency (how tightly grouped the points are)

• Accuracy = closeness to the “true” location

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After finishing our GIS work in ArcGIS Pro, we switched to Excel to calculate detailed error metrics for the GPS points. Using formulas, we compared each collected point to a benchmark location and calculated the distance error for every point. From these values, we determined statistics like the minimum, maximum, mean, and percentiles.

One key metric we calculated was the Root Mean Square Error (RMSE), which is a single number representing the typical error across the whole dataset. In this lab, our RMSE was 3.06 meters, meaning that, on average, the GPS points were about three meters away from the benchmark location. RMSE is especially useful for quickly evaluating and comparing the overall quality of GPS data.

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The Cumulative Distribution Function (CDF) scatter plot can be used to visually determine data metrics, such as the minimum, median, and maximum error, as well as percentiles (68%, 90%, and 95%). However, it cannot be used to directly determine the mean or RMSE.

The CDF provides a clearer picture of the dataset than individual metrics alone, as it shows how all the errors are distributed. It helps identify whether most GPS points are clustered together or if there are a few outliers with significantly higher errors, extending the line out into a “tail.” This insight goes beyond summary numbers and helps in evaluating the overall quality of the GPS data.