Data Quality Standards

 

This week's lab focused on evaluating the positional accuracy of two Albuquerque road datasets: one from the city and one from StreetMap USA. Using high-resolution 2006 orthophotos as reference, I followed the National Standard for Spatial Data Accuracy (NSSDA) guidelines to assess horizontal accuracy.

I began in ArcGIS Pro by creating a fishnet to divide the study area into four quadrants. I then digitized 20 intersections that were clearly visible on the imagery and present in both road layers. Each point was entered into three feature classes ( ABQ test points, StreetMap test points, and reference points), with matching Point IDs. I confirmed that the sample met NSSDA criteria (minimum of 20 points, even quadrant distribution, and spacing greater than 10% of the study area diameter) using the Near tool to check distances.

Figure 1. Sampling locations used to assess road network positional accuracy.

After verifying point distribution, I used the Add XY Coordinates tool and exported all three datasets to Excel for analysis. There, I calculated squared differences, RMSE, and final positional accuracy. The results showed the city streets data has a horizontal accuracy of ±26.65 ft at 95% confidence, while StreetMap USA’s data came in at ±217.90 ft. 

While I initially assumed the city data was more accurate just by visual inspection, this lab showed how to formally test and quantify positional accuracy using industry standards. It reinforced the importance of knowing where your data comes from and how accurate it really is when doing spatial analysis. The NSSDA method provides a clear, standardized way to measure and report that accuracy.