Data Quality

Continuing with the same focus of last week on positional accuracy, we compared two different road networks of the city of Albuquerque for positional accuracy.  The first network tested was made by the city of Albuquerque itself and the second was made by StreetMapUSA.  As to be expected, the network produced by the city of Albuquerque was considerably more accurate of the two since they have a much greater vested interest in having an accurate map of their own city (e.g. for properly dispatching ambulances for 911 calls).

To compare the accuracy of a network map you need to have a reference map to work from.  For the independent reference points we used the orthographic satellite photos to find intersections that exist on both map networks.

The next step of the positional accuracy process is to set the points.  Each point for both networks and the ortho photos has to be for the same intersection.  It is also important to get an even spread of points across the study area.  For best accuracy, greater than 20% of the points should be present in each quadrant and the points should be spaced out from one another.  At least 20 test points are required for reliable results.

After picking the points and then exporting them to an Excel spreadsheet, I took that raw data to process the accuracy assessment.  For each point on the network, the difference in the latitude and longitude is found.  Then, that difference is squared.  And the squared difference of both latitude and longitude are added together.  The sum of the squared differences is calculated, and then the average of that sum is also calculated.  Finding the square root of the average of the sum of the difference in latitude and longitude squared gives the RSME.  Multiplying the RSME by a provided value (in this case, the National Standard for Spatial Data Accuracy statistic determines that to be 1.7308 for horizontal accuracy) gives the final NSSDA value.  The lower the value the better the positional accuracy.

A road network map with the associated points for the intersections.  Both of the network layers and the reference points (from the ortho imagery) each had their own set of 20 points that all corresponded to like intersections.

The final results on accuracy are as follows:

Streetmap:
Tested 478.683 feet horizontal accuracy at 95% confidence level

Vertical positional accuracy: not applicable 

ABQ:

Tested 21.669 feet horizontal accuracy at 95% confidence level Vertical positional accuracy: not applicable