GIS5935 M1.2 Lab Data Standards

 

Assessing Positional Accuracy of Road Networks: A Case Study

In this project, I conducted an accuracy assessment of road networks using data from the city of Albuquerque and StreetMap USA. The goal was to determine the positional accuracy of the two datasets, following the methodology outlined by the National Standard for Spatial Data Accuracy (NSSDA). 

Below is a screenshot of the test point locations for the data from the city of Albuquerque:



Steps Taken to Complete the Accuracy Assessment:

  1. Data Exploration: I began by exploring both datasets, comparing road centerlines from the City of Albuquerque and StreetMap USA against orthophotos of the area to check for differences.

  2. Selection of Test Points: Using NSSDA guidelines, I selected 20 well-defined points, such as road intersections, that were visible in both datasets and the reference orthophotos.

  3. Independent Dataset: The orthophotos were treated as the independent reference data due to their high level of accuracy.

  4. Coordinate Comparison: For each test point, I recorded the X and Y coordinates from both the test datasets and the reference dataset.

  5. Accuracy Calculation: I calculated the positional accuracy using the root mean square error (RMSE) method. This involved computing the differences between the coordinates from the datasets and the reference points and then applying the NSSDA formula to express the final accuracy at a 95% confidence level.

Final Accuracy Statement:

  • City of Albuquerque Street Data: Tested 19.6 feet horizontal accuracy at 95% confidence level.
    This means the street data from the city is very reliable, with a high level of positional accuracy.

  • StreetMap USA Data: Tested 740.5 feet horizontal accuracy at 95% confidence level.
    This dataset, however, has much lower positional accuracy, making it less suitable for precision-based tasks.

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