It's hard to believe that we are already going full speed in the fall semester of 2019. Soon 2020 will be upon us and I will have completed my first year of graduate school here at UWF. Last semester, I focused primarily on GIS Programming and Spatial Data Management using SQL. This semester, I'll be focusing on both special and advanced topics in GIS as well as Remote Sensing and Aerial Imagery. Let's jump right in for this phenomenal first week!
For this weeks lab in Special Topics of GIS, I was tasked with calculating metrics for spatial data quality. In this lab, I analyzed spatial data quality for a set of gathered waypoints taken by a handheld GPS unit in two separate ways. The first was via a map with buffer zones (below) showing three percentiles of precision. The second which will not be discussed in this post is a root-mean-square error analysis and a cumulative distribution function graph.
Before I delve into my findings, accuracy and precision need to be explained in the realm of GIS. For the purpose of this post, I am assessing horizontal accuracy and precision. To derive the horizontal precision, which is the closeness of the recorded points to one another, I calculated an average projected waypoint I then created three buffers for precision percentiles that contain an x amount of points. The buffers I created were at 50%, 68%, and 95%. For horizontal accuracy, which is how close the measured values are to the actual (reference) point, I measured the distance of my average projected waypoint from my horizontal precision calculation to the actual reference point.
Now that my methods of determining horizontal precision and accuracy have been explained, I would like to share my results with you.
For horizontal precision, I got a value of 4.5 meters when measuring precision at the 68th percentile. If we are basing the precision off of the 68th percentile then these results would be precise. For my horizontal accuracy, the distance from the average waypoint (blue) to the reference (actual) point was 3.24 meters. Compared to the precision value, these results have fairly high accuracy. Overall, after assessing the horizontal accuracy and precision, it can be observed that the GPS waypoints collected in this test are more accurate than precise. Determining accuracy and precision is, of course, subjective. If these measurements were taken by a surveying company, the resulting precision and accuracy values would be considered failure by survey standards. However, if these waypoints were referencing an object such as the location of a fire hydrant or electrical unit box, they would be much more suitable. Finally, in terms of bias, many factors impact the results. How good is the GPS unit? Are there any satellite connection interference variables such as buildings or weather? Is the user holding the unit consistently in one position? These can all play a role in how data is collected.
I look forward to sharing my future work with you all and as always...
~Map On!
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