GIS5935 Module 1.2: Standards in Data Quality

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The next Special Topic in GIS for Data Quality is Standards, particularly in regard to positional accuracy!

Using two different road datasets in Albuquerque, New Mexico, we gained a better understanding of how to determine and verify the horizontal accuracy of these road networks, based on the National Standard for Spatial Data Accuracy (NSSDA). One of these road networks was from the city of Albuquerque, while the other is from TeleAtlas and ESRI. Orthophotos from 2006 were used to ground these roads to a true reference.

Screenshot featuring the 20 Sampling Locations for the New Mexico Study Area.

Out of the gate, as the lab prefaced, the city street data already looked visually more accurate to the ortho imagery than the Street Map USA data. As described by Bolstad and Manson (2022, p. 617), the NSSDA consists of 5 steps for calculating horizontal accuracy; here's how I applied them to each step.

1.) Identify test points, which 20 points were created at various road intersections.

  • I first used the Grid Index Features on the Study Area shapefile to break up the rectangular polygon into 4 equal quadrants.
  • I measured the diagonal width of the Study Area and then multiplied it by 10% to confirm the minimum distance the test points could be.
  • 20 test points were created for the city streets and Street Map USA based on clear intersections where both road datasets had coverage, ensuring the test points' distance from each other did not go below the minimum required distance and were evenly distributed across all quadrants.

2.) Identify a dataset/method as a true reference to ground and compare the test points to, which involved checking the two different road datasets against the orthophotos.

  • Ortho imagery from 2006 was provided by UWF GeoData Center, which was used as the true reference to gather a valid baseline for the road data.

3.) Measure the positions of the test points from each of the true references.

  • Ran the Add XY Coordinate tool on each of the road datasets' test point feature classes and the true reference feature class to get their coordinate information as X and Y columns.
  • Exported each feature class's attribute table for use in Excel.
  • Each road dataset had its own Excel sheet, city versus Street Map USA, where I pulled the true reference X & Y columns into each road dataset sheet.

4.) Calculate the test points' positional horizontal accuracy.

  • Input the proper formulas for each field: ∆X, ∆Y, ∆X^2, ∆Y^2, RMSE X, RMSE Y, RMSE R, and, last, the NSSDA Horizontal Accuracy in feet.
  • To prepare for the horizontal accuracy calculation, the RMSE (root mean square error) of both X & Y were first calculated, which were then used to find the RMSE R (radial).
  • Finally, the RMSE R was multiplied by 1.7308 to find the horizontal accuracy in feet.

5.) Conclude with a formal horizontal accuracy statement. (I have rounded to the nearest one-hundredth in my calculations.)

  • City of Albuquerque Streets: Tested 13.80 feet horizontal accuracy at 95% confidence level.
  • Street Map USA: Tested 190.79 feet horizontal accuracy at 95% confidence level.

Overall, it was insightful to walk through how positional horizontal accuracy is determined. Having done mainly georeferencing work early on in my GIS career, it's nice to finally take the time to break down the statistical calculations behind RMSE. I have a greater appreciation and understanding now.


References:

Bolstad, P., & Manson, S. (2022). GIS fundamentals: a first text on geographic information systems (7th ed.). Eider Press.

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