The goal of an accuracy assessment is to help determine how reliable data is compared to the "actual" - or as close as can be reasonably determined as the actual. With the standardized techniques in the National Standard for Spatial Data Accuracy (NSSDA), we get a determination of positional accuracy that can be used to quantify how "off" a geospatial dataset is from the actual. With standardized protocols like this the accuracy statements become comparable to one another between datasets, allowing a user to select data that meets their needs for this aspect of data quality.
In the NSSDA protocol, the horizontal or vertical distance between a subset (>=20) of dataset features and the "actual" location of the feature is compared, and the difference between the actual and the dataset coordinates are then squared, summed, averaged with mean, and square-rooted to return the Root Mean Square Error. This error is then multiplied by a standard value that represents the average amount of error in the 95th percentile for horizontal or vertical data. The result is the shortest distance in the dataset that can be "trusted", expressed as an accuracy statement to the 95th percentile.
For this lab a different aspect of data quality was examined; completeness. If accuracy helps determine if a dataset can be used based on how "trustworthy" it is, then completeness determines if a dataset can be used based on the amount of information it contains. In this lab, the completeness of two street datasets was assessed based on amount of data. The street polylines were overlaid and assessed with a grid. This method highlighted areas where the county-provided shapefile contained more lines (reds) and areas where the US Census' Topologically Integrated Geographic Encoding and Referencing (TIGER) dataset contained more lines (blues).
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| Red areas where the Jackson County file contained more road and blue areas where the TIGER dataset contained more road. |
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