Special Topics in GIS - Module 2 - Data Quality - Standards

The second module in Special Topics in GIS focused on data quality standards with an exercise on determining the horizontal positional accuracy of two road networks in the city of Albuquerque, New Mexico. Our findings were to be reported in accordance with the National Standard for Spatial Data Accuracy (NSSDA). We were provided two polyline shapefiles that represented road centerlines from the city of Albuquerque and StreetMap USA as well as a mosaic of orthophotos of the study area.

The NSSDA standard requires at least 20 test points within our study area, with each point separated by more than one-tenth of the area's horizontal distance. The NSSDA value is an accuracy measurement of our Root Mean Square Error at the 95% confidence interval. To help achieve the requirements I used the split tool to divide the study area into four quadrants and then bookmarked each quadrant which reduced the need to zoom in and out to check the spacing of my points.

Next step was to begin finding "good" intersections that contained data from both the Albuquerque and Street Maps data sets.  After identifying my "good" intersections it was then time to determine what I thought were the "true" reference points at the intersections using the orthophotos. Below is a screenshot showing my reference or "true" locations of the intersections according to the orthophotos.

Next step was to use the Add XY Tool to determine X and Y coordinates for each of the points for all three datasets. I then exported the three attribute tables to excel files using the Table To Excel tool. Following the NSSDA horizontal accuracy statistic worksheet, the independent (true) points from the orthophotos X and Y coordinates were compared to the test points datasets. Calculations were then completed to determine the accuracy statistics for the two test datasets.  Below are the results from my worksheet for the StreetMap USA NSSDA value calculations: 

The final column of the table shows the calculated squared error distance. The values are summed and then averaged. The NSSDA horizontal accuracy is calculated by multiplying the Root Mean Square Error (RMSE) by 1.7308. Below are my final accuracy statements for each of the two datasets.

Tested 13.01 ft (3.96 m) horizontal accuracy at 95% confidence level for the Albuquerque Streets data set.

Using the National Standard for Spatial Data Accuracy, the Albuquerque Streets data set tested to 13.01 (3.96 m) feet horizontal accuracy at 95% confidence level.

Tested 312.95 ft (95.38 m) horizontal accuracy at 95% confidence level for the Street Map USA data set.

Using the National Standard for Spatial Data Accuracy, the Street Map USA data set tested to 312.95 ft (95.38) feet horizontal accuracy at 95% confidence level.