Module 1.1 - Calculating Metrics for Spatial Data Quality

Part A: Determining accuracy and precision

Precision indicates the degree to which the collected points are in proximity to one another; it is considered high when the points are closely grouped. Conversely, accuracy assesses how near the points are to the actual reference location. Average accuracy is deemed high when the mean of the points is situated close to the true location. 

In this laboratory exercise, we initially computed the estimated horizontal precision for a set of points gathered using a handheld GPS device. By applying the 68th percentile, we determined that the horizontal precision was 4.47 meters. This suggests that the points are clustered and are closed to each other. 

Subsequently, we assessed the horizontal distance from the average waypoint to the reference point, which represents the true location of the mapped point, in order to determine the horizontal accuracy, which was found to be 3.24 meters. This suggest that the points are near the true location. This minimal discrepancy indicates a high level of accuracy. 

 

Part B: Root-mean-square error (RMSE) and cumulative distribution function (CDF)

Error metrics for the GPS positions:

Minimum = 0.14

Maximum = 6.95

Mean = 2.67

Median = 2.45

RMSE = 3.06

68th Percentile = 3.18

90th Percentile = 4.67

95th Percentile = 5.69

In the first section of part B of the lab, we used metrics to calculate the RMSE (Root Mean Square Error), and also calculated the Mean, Average, minimum, maximum, 68th percentile, 90th percentile and 95th percentile values for error_xy. The error_xy is the distance error from a given point to the benchmark point. In the second section of part B, we created a cumulative distribution function (CDF) graph showing the complete distribution instead of the selected metrics. To accomplish that we used the error_xy data, sorting by smallest to largest values and adding a new column which represent the percentage from 0 to 100. We created a CDF graph showing the entire probability distribution of the error_xy. We can say that the CDF is a distributional description of data, while metrics are summary statistics derived from data.

The cumulative distribution function (CDF) provides significantly greater insight than metrics. The slope of the CDF indicates variations in density. In this analysis, the curve rises steeply until approximately 68%, where the majority of points are situated near the true location, while the flatter sections signify lower density with fewer points present. Additionally, the CDF offers a visual representation of cumulative distribution, enabling the comparison of multiple distributions. In summary, the CDF enables a more detailed interpretation than merely depending on a mean or median obtained from metrics.