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.

---

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.

This lab was a natural continuation of last week’s work, moving from evaluating precision and accuracy in GPS data to applying those same concepts to full-scale datasets. It was gratifying to bring together skills I’ve learned across different courses — like using mosaic datasets for performance optimization — to handle real-world GIS challenges.

Most importantly, this lab reinforced the critical importance of independent, high-quality reference data when testing spatial datasets, and why carefully managed local data often outshines national-level products.

On to the next one!