This is the 5th post on on my paper **“A Method for Determining and Improving the Horizontal Accuracy of Geospatial Features” **Other posts on this topic:

4. Coordinate Sample Builder

6. Five Methods for Determine the Spatial Accuracy of Lines

The Geospatial Positioning Accuracy Standards – Part 3: National Standards for Spatial Data Accuracy states that twenty or more test points are required to conduct a statistically significant accuracy evaluation regardless of the size of the data set or area of coverage. Twenty points make a computation at the 95 percent confidence level reasonable. The 95 percent confidence level means that when 20 points are tested, it is acceptable that one point may exceed the computed accuracy.

Personally, I make use of a spreadsheet that requires 30 points to calculate RMSE and the Standard Deviation for RMSE in both x and y. With this information we can calculate the following accuracy measures:

1. Estimated Root Mean Square of the population errors

2. Estimated Variance of the population errors

3. Estimated Standard Deviation of the population errors

4. Greenwalt & Schultz CMAS Standard normal (Z) interval of the population errors at 95% probability

5. Greenwalt & Schultz CMAS Standard normal (Z) interval of the population errors at 90% probability

6. NSSDA Statistic

7. Confidence interval on the estimate of RMSE at 95% probability

Using this information we were able to calculate the confidence interval on the estimate of RMSEx/y at 95% probability for our permits as:

Here are the two MS Excel spreadsheets for 30 and 100 points. Just copy 30 coordinates pairs in and you will get your accuracy measures.

Spatial Accuracy Assessment for 30 Points

Spatial Accuracy Assessment for 100 Points

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