Lab 2 M1.2 Data Quality - Standards

Street Map USA points

Albuquerque street points

Reference points

This week’s lab Is about data quality for standards of road network quality and horizontal positional accuracy. In determining the road network quality, the National Standard for Spatial Data Accuracy (NSSDA) procedures were used. The data for this were the streets of Albuquerque. We compared Street Map USA data from ESRI’s TeleAtlas and street data from Albuquerque to orthophotos of a study area. The orthophotos are the reference points for Albuquerque's two test point street maps. Street intersections are the most easily identified and easy to establish for points to compare. 

We were to choose at least 20 points so I chose 30 points from the two test points and 30 of the reference points. I created these points by first creating a point feature class in the geodatabase. From here in Edit mode I created points at intersections that all three study areas had in common. Once these were completed, I uploaded the x and y coordinates into MS Excel for good formatting and access to formulas to get results. These formulas are simple calculations of adding, subtracting, squaring, averaging, and calculating the root mean square error(RMSE). Each of these results enabled me to get the result for the NSSDA error. 

My numbers were rather large perhaps due to choosing 30 points as opposed to only 20. I maintained the same zoom level on the map scale when creating the points. The RMSE is used to calculate the NSSDA result, multiply the RMSE by 1.7308. According to the Federal Geographic Data Committee in Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy, “A minimum of 20 check points shall be tested, distributed to reflect the geographic area of interest and the distribution of error in the dataset. When 20 points are tested, the 95% confidence level 4 allows one point to fail the threshold given in product specifications.” As I read this and also according to Dr. Morgan’s lab exercise, a minimum of 20 points is how I ended up with 30 points. It does not necessarily mean that 30 test points will provide greater accuracy or error in the results. However, in the article, it does not mention going beyond the number of 20 points. A journal article written by Zandbergen et al. (2011), required reading for this lab, where a test conducted used 100 sample points of data for their study of census data to determine accuracy. I mention this to show the comparison of the two studies, obviously, the census data comparison study was more comprehensive and covered a larger area.  

(Zandbergen, Paul & Ignizio, Drew & Lenzer, Kathryn. (2011). Positional accuracy of TIGER 2000 and 2009 road networks. Transactions in GIS. 15. 495-519. 10.1111/j.1467-9671.2011.01277.x.)

The results of the data tested to meet NSSDA standards were 4716.28 feet horizontal accuracy for the Albuquerque street data. The results of the data from the StreetMapsUSA data were 4481.29 feet horizontal accuracy. My numbers seem far too large for this to be considered accurate and I am not sure where I went wrong with gathering the data and running calculations. 

For the ABQ street data set: Tested 4716.28 feet horizontal accuracy at 95% confidence level. 

For the Street Map data set: Tested 4481.29 feet horizontal accuracy at 95% confidence level.