Module 1: Calculating Metrics for Spatial Data Quality

 How important is it to monitor spatial data quality? 

Spatial data quality is very important for GIS to accurately present data in a project and consider adjustments to properly format the data. Additionally, it is important to identify the potential errors data can have and summarize our findings. The accuracy and precision of data are two factors to identify errors. 

In the lab, we used the Root Mean Square Error (RMSE) calculations to show errors in accuracy and measured the data points to identify variance in find errors of precision in the data. 

In our lab, we were asked to summarize 50 GPS points taken on the reference point and create buffers to determine the range of precision at the 50th, 68th, and 95th percentile values. Our horizontal accuracy was 0.26 m. We find the horizontal accuracy we took the precision value subtracted the averages of the data points to get 0.26m. Since it was still in the range of the horizontal precision value of 2.44m at the 50 percentile; it was more accurate. The vertical accuracy was 5.46 m and since the vertical precision value was 3.063m the values were very above the vertical precision therefore it was not as accurate. Our vertical precision was the average elevation of the points and our vertical accuracy was calculated by the reference point's elevation subtracted from the averages of the data elevation. 







 

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