Module 1.2: Data Quality - Standards

In this lab, I explored the concept of data accuracy standards by determined the positional accuracy of road networks. The two sets of road networks were for the city of Albuquerque, New Mexico. One was a shapefile of road center lines from the city of Albuquerque itself and the other was a shapefile with streets from StreetMap USA, a TeleAtlas product distributed by ESRI with ArcGIS software.

According to National Standard for Spatial Data Accuracy (NSSDA) guidelines, at least 20 reference points within the study area are needed to test the accuracy of each of the road networks. Additionally, no fewer than 20% of the reference points should be located in each quadrant and the distance between each of the points should be at least 10% of the diagonal distance across the study area. Following the NSSDA guidelines, I divided the study area into 4 quadrants measured the diagonal distance of each quadrant to use in my intersection selection process.

To select the reference points, I zoomed into intersections with the ortho imagery turned on. I selected intersections within each quadrant that occurred in both road networks and had a  easily identifiable intersection point. A total of 5 points were digitized in each quadrant and I used the Measure tool to ensure that the points were within the guideline distance. Below are the reference points selected for accuracy testing:


Once the reference points were digitized, a unique ID was added to each of the shapefiles so that the reference points could be tested against the same intersection points in each of the road networks. I used the XY Coordinate tool to add XY coordinates to each of the shapefiles. This data was entered into the accuracy statistic worksheet. The worksheet determines the error between each point and the reference, the root mean square error (RMSE) and the NSSDA number. The NSSDA number is the 95% confidence level which is determined by multiplying the RMSE by 1.7308 (the standard error of the mean).

My positional accuracy results for each of the data sets written as formal statements conforming with the NSSDA guidelines which would also be incorporated into metadata are as follows:

ABQ Streets City Data: Using the National Standard for Spatial Data Accuracy, the data set tested 21.531 feet horizontal accuracy at 95% confidence level.


Street Map USA: Using the National Standard for Spatial Data Accuracy, the data set tested 254.566 feet horizontal accuracy at 95% confidence level.

From these results, we can conclude that the ABQ Streets City Data was much more accurate than the Street Map USA data. This makes sense considering that the Street Map USA data is a nationwide dataset, whereas the  ABQ Streets City data is a local data set. We could better assess the accuracy of the data sets by using more reference points as well as using a GPS to truly ground truth the intersections. Positional accuracy is an important, necessary piece of information when considering the use of geospatial data and should be incorporated into metadata.

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