This week's lab was about accuracy and precision of data and related error metrics, using a set of GPS points as a case study. Accuracy measures how close a feature in the data is to the real-life location of that feature and is measured by finding the difference between the data being assessed and a reference dataset known to be more accurate. Precision refers to the consistency of repeated measurements and can be measured by taking their mean and comparing the individual points to that mean. The map below illustrates the GPS data from the lab along with an average of all the points and buffers encompassing 50%, 68%, and 95% of the data points relative to the mean. The distance in meters for each interval is 3.1, 4.5, and 14.8, respectively. The accuracy of the dataset was later evaluated by comparing the average point to a reference point; the difference was 3.2 meters.
