Module 1.1 Calculating Metrics for Spatial Data Quality

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As we begin this semester's journey, this week we started with calculating metrics for spatial data quality. This is something that I really need to refresh my skills on as I can't seem to really meet statistics and GIS in the middle. So I had to refresh myself with the terminologies that rather confused me in the past - which upon finishing this week's lab helped me.

The horizontal accuracy is 3.24 meters which is the spatial distance between the average way point to a reference point. While we got our horizontal precision by getting an average of the data values/measurements collected equal to 4.5 meters. difference of 1.26 meters between the two which for me still provides a reasonable difference though by using the percent error formula of (Observed – True) / True then I would get a percent error of 38.89%. Furthermore, it tells us that our horizontal precision data is 61.1% accurate. 

Horizontal precision is the dispersion or closeness of the value to the average waypoint while horizontal accuracy is the distance between true (reference waypoint) to observed value (average  waypoint). See map below for more information.



For Part B, we examine a .dbf file which we then converted to an Excel file. For all of our waypoint values, we had it compared against the benchmark value to calculate the metric values for our RMSE, Mean, Median, Percentile Ranks, Min and Max. In addition, we also create a scatter plot which will show us a cumulative distribution function (CDF) graph that visualizes the values in different percentile ranks.


The metrics and the CDF provide us values of how precise and/or accurate our observed values to the true value. In addition, it can also give us averages of our data values collected. Yes, we can determine the CDF for all the values in Deliverable 5. CDF helps us visualize or maps values according to their percentile ranks. It just gives us an easier access to visualize all percentile ranks compared to having the percentile which gives you the value associated with the percentile rank. This is important as knowing the accuracy of our GPS unit will play a big role in our work/project/data collection.

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