The goal of accuracy assessment in mapping involves the verification of data reliability and quality. In the context of road networks, this assessment evaluates positional accuracy, attribute accuracy, completeness, and consistency. Given that road networks are essential to applications such as address geocoding and routing, it is important that the attribute data is both complete and precise.
There are various methods to ensure data accuracy. One effective approach is ground truthing, which utilizes high-precision GPS units to validate data accuracy. Additionally, data can be compared against high-resolution imagery or existing high-accuracy datasets to further determine its reliability.
Both positional accuracy and completeness are critical in the evaluation of data accuracy. Positional accuracy is determined by comparing the data against a higher quality dataset. Completeness, on the other hand, assesses the degree to which real-world objects are adequately represented. This involves evaluating the number of objects that should exist within the database but are absent, as well as identifying data that may need to be excluded.
Thematic accuracy and thematic completeness represent two additional measures of data quality. Thematic accuracy is measured by the extent to which attribute values correspond with reference data, such as the correct recording of street names or speed limits. On the other hand, thematic completeness is assessed by the number of missing attributes.
In the previous lab assignment 1.2 Data Quality – Standards, we evaluated the quality of two road networks: the streets of Albuquerque, derived from a shapefile provided by the city, and the Street Map USA data obtained from the TeleAtlas product. The aim was to evaluate positional accuracy using metrics defined by the National Standards for Spatial Data Accuracy (NSSDA).
In this lab assignment 1.3 Data Quality – Assessment, we initially calculated the total length of roads from two distinct layers: Jackson County Street centerlines and TIGER Roads, in order to evaluate which dataset exhibits greater completeness.
Comparing the two shape files Street_Centerlines and TIGER_Roads we find that the total length of roads represented in the TIGER_Roads shapefile exceeds that of the street_centerline shapefile by around 509.4 kilometers. This suggests that the TIGER_Roads dataset includes a larger quantity of digitized streets. Typically, TIGER data incorporates unpaved roads and private driveways, which are generally absent from local street centerlines. It can be concluded that although TIGER data may not possess the positional accuracy of local street centerlines, it often provides greater completeness.
In the second step, we employed a 5km x 5km grid to determine the total length of roads contained within each grid for both networks. As a result, we identified the number of grid polygons where the county centerlines are more complete than the TIGER roads, as well as the number of polygons where the TIGER roads surpass the county centerlines in completeness.
In the final step, we calculated the percentage difference in total length between the two road datasets for each grid polygon, utilizing the centerlines as the reference and applying the formula:
% difference = (total length of centerlines - total length of TIGER roads) x 100% (total length of centerlines)
Ultimately, we produced a choropleth map that illustrates the percentage differences between the two datasets.
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