Lab 2: Quality of Road Network Data In Albuquerque, New Mexico

This lab was an exercise in testing data quality. I received two sets of street map data. One was from the City of Albuquerque and the other from StreetMaps USA. I first used ArcCatalog to create a rough network dataset of both street maps. This program predicts where streets form intersections and places points where the intersections are. I then used a sampling tool to randomly select one hundred points that I could use for the project. I had to find points that were present in both datasets, examples of good intersections, and met sampling rules. This caused me to drop from one hundred points to twenty-nine, displayed in the screenshot above. I matched up all the points on both data sets and created a reference set of points based on orthophotos of the study area. I then used the National Standard for Spatial Data Accuracy (NSSDA) to calculate how accurate both sets of data are when compared to the reference set. Using the standard reporting statements presented in the Positional Accuracy Handbook¹ I got two statements. For the City of Albuquerque data, I got:

Using the National Standard for Spatial Data Accuracy, the data set tested 26.7 feet

horizontal accuracy at 95% confidence level.

For the StreetMaps USA data, I got:

Using the National Standard for Spatial Data Accuracy, the data set tested 360.6 feet horizontal accuracy at 95% confidence level.

1 Positional Accuracy Handbook. 1999. Minnesota Planning, Land Management Information Center, St. Paul,

MN.