Special Topics- Calculating Metrics for Spatial Data Quality- Module 1

 This module covered spatial precision, accuracy and bias. We also learned how to calculate root-mean-square error and cumulative distribution function.

Accuracy refers to the closeness of a collected data to an accepted reference point, while precision refers to the agreement between the data points. 

To determine accuracy we created a feature class with a single feature that represented the average of the latitude and longitude of all the collected data. We then compared this with a provided reference point that represented the "true" value.

To determine precision we spatially joined the average point feature class with the original point data feature class to obtain a distance attribute calculation. We calculated the distance of 50, 68 and 95 percentiles of the data from the average location, and used the buffer tool to create a visual representation of the percentiles. After determining the horizontal precision and accuracy we then determined the vertical precision and accuracy using the altitude data.

Our horizontal accuracy was 2.97 meters while our horizontal precision was 4.17 meters.