This is the first week of Special Topics in GIS and looks to be a class of new challenges with a different approach to data. However, after all, it is still data analysis and maps. That is what makes GIS fun and challenging to me. The title for this lab is Calculating Metrics for Spatial Data Quality. We learned the importance of accuracy versus precision and how one does not guarantee the other. Today’s cell phones and GPS units are quite accurate for the most part. They can be used to map data but should not be used without determining their accuracy and precision for horizontal and vertical positional accuracy and precision.
For this lab, we added shapefile data from the UWF repository that included waypoints and a reference point shapefile. We determined the precision and accuracy measurements of 50 waypoints that were mapped with a handheld Garmin unit with an accuracy of less than 15 meters, according to the unit owner’s manual. The points were sporadic at best with the actual location point being unclear. Once added to ArcGIS we created buffers of 1, 2, and 5 meters around an average waypoint location to make it simpler to map. After that was established we created three buffers that represented the 50th, 68th, and 95th percentile of the data. The 68th percentile was the most common representation of data. After this was completed we added the reference point shapefile that was mapped using a Trimble Pathfinder GPS unit with an accuracy of less than one meter if set up correctly, according to the manufacturer’s specifications.
Great! Now what? We have a map created and we need to make sense of the data. For this, we used Root-mean-square error (RMSE) and cumulative distribution function (CDF) to determine the accuracy and precision of the results. For this part, we used MS Excel, which was a bit of a challenge when trying to figure out the formulas to get the final results for the CDF. It was a large dataset to work with of x and y points, 200 to be exact. It helps to have some knowledge of Excel when doing this. We calculated the RMSE of the average between the x and y values which we derived from the coordinates. Once the results were determined we graphed them in a CDF, where the relationship between cumulative percentage and the errors in xy were shown in a scatterplot. The CDF helps with visualizing the results.
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| XY Coordinate errors and percentages |
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| Error metrics for the GPS positions |
The main point of our lab this week is determining accuracy and precision. Both can be confusing and may seem interchangeable but they are not. According to Bolstad in GIS Fundamentals, “accuracy measures how close an object is to the true value.” He defines precision as “how a dispersed a set of repeat measurements are from the average measurement.” In these definitions, you can see how related they might be but still different. The introduction of bias is not something to overlook in data and will certainly affect accuracy and precision. It could be measured in the wrong coordinate system or faulty equipment.
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