Determining the Spatial Accuracy of Polygons Using Buffer Overlay


This is the 7th post on on my paper “A Method for Determining and Improving the Horizontal Accuracy of Geospatial Features” Other posts on this topic:

6. Five Methods for Determining the Spatial Accuracy of Lines
8. The Final Solution

Buffer Overlay is a geospatial analysis technique that creates a buffer around a control line, the buffer is then used to clip a test line, the lengths of both lines are compared and if the test line is significantly shorter than the control a larger buffer is used. When test and control lengths are equal the test line can be said to be the size of the last buffer applied.

In our case the control is parcel line work and the test are permits. However, it is not practical to buffer the line work of an individual parcel and then clip the permits because not all parcels have permits. So we will have to reverse the procedure and buffer the test line and find the length of control within the buffer. A more complete description of the procedure is as follows:

  • Convert parcel polygons to parcel lines
  • For each permit
    • Buffer from .5 ft to 60 ft @ .5 ft intervals
      • Clip the parcel lines (control) using buffer
      • Drop dangling nodes (where length = buffer)
      • Calculate the Cumulative Probability (CP)
      • CP = (Length of Clipped Parcel Line/Perimeter of Permit)
      • If CP >= 1
        • Horizontal accuracy is +/- buffer distance
      • Else If CP <= 0.999 and buffer < 60
        • Next buffer

The results of this procedure is the creation of individual probability curves for each permit as shown in the image and video below.

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Spatial Accuracy Assessments Using an Excel Spreadsheet


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