Special Topics - Lab 2 - Accuracy Reporting

 Welcome to Lab 2!

This week carries on where last week left off still looking at data accuracy and standards. This week we branch a little deeper into the two types of standards that we could generally be looking for. First, are the National Map Accuracy Standards  (NMAS) of 1947, which were geared more toward printed maps. This standard revolves around allowable positional errors for the maps features, with 90% of well-defined points expected to fall within a map-scale-based tolerance. This standard while still applicable to many printed charts/maps has generally been replaced by the National Standard for Spatial Data Accuracy (NSSDA). This is the current standard which provides a statistical framework for assessing positional accuracy. It uses Root Mean Square Error (RMSE) to quantify how closely the data aligns with actual or true geographic positions. It reports accuracy at a 95% confidence level, which takes either a horizontal or vertical RMSE and applies a statistical multiplier. This approach is more consistent and flexible for digital mapping than the previous standard. 

For this weeks specific project, we looked at two different roads feature classes for Albuquerque, New Mexico. One, provided by the city GIS team, is supposedly the higher resolution standard for the city. The other is a separately provided road network. Both of these were tested against user (my) derived intersection points from 2006 based digital orthographic quarter quad imagery. 

The image above shows the base road network and the sample points which align to intersections throughout the study area. These intersections were then used to compare the reference point, to a point derived from the intersection of the road features polylines, converted to a point. The measured difference in points is used to determine a sum, average, and RMSE. A sampling of the point analysis calculations is below.

The calculations for the full data set result in a set of statistical measures seen below. While the units arent pictured below, remember we are working in feet, based off of the projected coordinate system in use. 

The last thing we do with these calculated results is report it in a semi-standardized method based on the NSSDA. My derived statements for which are below. There is a short statement which could suffice for some reports, or a more in depth detailed statement. I offer both below. 

Horizontal Positional Accuracy (Basic Statement – ABQ Street Data)

Using the National Standard for Spatial Data Accuracy, the City of Albuquerque’s ABQ_Streets data set tested 19.74 feet horizontal accuracy at 95% confidence level.

Horizontal Positional Accuracy (Detailed Statement – ABQ Street Data)

The intersection centerline accuracy of the Albuquerque Street dataset provided by the city of Albuquerque GIS Team was evaluated against a random sampling of intersection center points. The centerline points were observed and derived from 2006 Digital Orthographic Quarter Quad imagery for the study area. This accuracy assessment served to validate the ABQ-Street data as a baseline “high-accuracy” dataset with which to compare other datasets against. The NSSDA accuracy of which was tested at 19.74 feet horizontal accuracy at 95% confidence level.

This was determined through twenty intersections approximating equal spacing throughout the study area. These were adjusted to ensure that the reference sampling points would be applicable to two different street layers, the one discussed here, and a secondary discussed later. Taking the approximate centroid of the selected intersection a new point was derived, and compared against the intersection terminus of the ABQ-Street file, as defined by the intersection vertices transformed to a point feature. The imagery derived reference point and ABQ-Streets derived intersection points were then compared and utilized for the accuracy assessment.

The positional difference in easting and northing was calculated with the results garnering the sum, average, and Root Mean Square Error (RMSE). Following NSSDA guidelines, the RMSE was adjusted to 95% confidence utilizing a multiplier of 1.7308. See the tabular output above. 

Thank you,

Brandon