Module 1 of Special Topics in GIS dealt with the precision and accuracy of gathered waypoints from a GPS data collection unit. International Organization for Standardization defines precision as 'the closeness of agreement between independent test results obtained under stipulated conditions' [ISO, 2006]. With regard towards the lab assignment, precision would be determining the proximity of fifty gathered waypoints from a single location using a Garmin GPSMAP 76 data collection unit. As shown in the map below, many of the waypoints are in close proximity while others deviate from the majority. For this part of the lab, the mathematical mean was calculated for the x-, y-, and z- location for all fifty waypoints; this 'average' location is denoted on the map as a red 'X'. Once this average location was calculated, an analysis could be performed on the distance between each waypoint and the calculated average location. This precision analysis concludes that 50% of all gathered waypoints fall within 3.1 meters of the average location, 68% of waypoints fall within 4.5 meters of the average location, and 95% of all gathered waypoints fall within 14.8 meters from the calculated average location. Whether these precision analysis results would suffice varies widely between applications. These percentile distances may be acceptable and appropriate for one scenario and widely unacceptable in a different scenario; precision, therefore, is relative and must be determined at the beginning of each synopsis.
The second part of the lab assignment dealt with accuracy, and different tools that can be employed to determine the extent of accuracy within a dataset. According to GIS Fundamentals: A First Text on Geographic Information Systems, an accurate observation 'reflects the true shape, locations, or characteristics of the phenomena represented in a GIS', meaning that accuracy is a 'measure of how often or by how much our data values are in error' [Bolstad & Manson, 2022, p. 609]. For this portion of the lab, a dataset was provided and completely analyzed using Microsoft Excel. This was very beneficial, as it provided an excellent opportunity to deviate from the comfort of ArcGIS Pro, and into a program that is not so familiar. The first tool used to calculate the dataset's accuracy was a series of manual formulas, including minimum, maximum, mean, median and Root Square Mean Error. The second method used to display the accuracy of the dataset was a Cumulative Distributive Function graph, which is displayed below.
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