Module 2 - Special Topics in GIS - Positional Accuracy of Road Networks

Map produced by the City of Albuquerque,  with sample intersections
placed for this accuracy assessment.

This week in Special Topics in GIS, we continued to practice making calculations of error between a sample dataset and a reference dataset considered to be more accurate.

Last week, we calculated error on two existing sets of data that we were given as part of our lab materials.  This week, we generated our own datasets from maps of the city of Albuquerque, New Mexico, then calculated error.





The figure above is a simple street map for the first dataset to be tested . The dark dots represent samples of street intersections taken from a map created by the City of Albuquerque. This City map is considered to be quite accurate, according to our lab materials this week.  We also examined the accuracy of another street network from StreetMap USA, over which we placed sample location points on the same street intersections as for the City map.    In order to assess the accuracy of these two datasets, we needed an independent reference dataset with corresponding street intersection points, which has been deemed to be much more accurate than those datasets we want to test.
For this "truer" reference, we digitized a new set of points on the centers of the sample street intersections, based on digital orthophoto quarterquads (DOQQ's) covering the city.

For this accuracy assessment, we followed the methods developed for the National Standard for Spatial Data Accuracy (NSSDA).  This document and others based on it line out a 7-step process for assessing and documenting the positional accuracy of a data set.  A very well-written summary of this process, with several case-studies, was produced by the Minnesota Planning and Land Management Information Center and included in our lab readings, and this was my main guide in this project. 

In summary, here are the seven steps for assessing the positional accuracy of the dataset in question:

1.  See if you need to test horizontal, vertical, or both accuracy in your dataset.
        (For this assignment, we assessed only horizontal accuracy.)
2. Select a set of test or sample points from the dataset that you're testing. 
       (We tested two datasets, one from the City of Albuquerque map, and the other from the
       StreetMaps USA map of the same area.)    
       For this step, select at least 20 points, make sure that at least 20% of them are in each 
       quadrant of your study area, and that they are at a distance from each other of at least 
      10% of the diameter of your study area.
3. Find another, independent dataset that represents the locations of those same points, 
    but with higher spatial accuracy.
      For this, you might have to digitize the locations from an orthophoto, or measure them in the field       with GPS, or the points might already exist in another more accurate dataset.  
      (Our reference data was digitized from the DOQQ's.) 
4. Tabulate the x and y coordinates for both datasets.
5. Calculate positional accuracy statistics, either horizontal or vertical.
       In this step, calculate the distances for both X and Y between each pair of points (test and
       reference), square those differences, then sum the squares.  
      This is the first part of the Pythagorean equation, but in this case, do NOT find    
      the square root of the sum.  Instead, calculate the average  of the sums of x and y squares
      for all of the pairs of points.   Then, take the square root of that average.
      This gives you the average error distance between all of your test and reference points.    
      This is called the Root Mean Square Error (RMSE).  
      From this statistic, we now want to calculate the error distance at which 95% of our 
      tested data points fall from their corresponding reference points.  
     To do that, simply multiply the RMSE by 1.7308 for horizontal error, and 1.9600 for 
     vertical error (these factors are calculated from the mathematical characteristics 
     of a Gaussian or bell-shaped distribution curve.) 
     These products are known as the NSSDA statistics and should be reported in the 
     final accuracy assessment.
6. Write a standardized accuracy statement in the NSSDA format.  
     (My statements for these assessments are shown below.  
7.  Include that accuracy report in the metadata of the dataset you are testing.


Here are formal my NSSDA accuracy statements for the two tested datasets in Albuquerque, NM.

a. City of Albuquerque road network, Horizontal Positional Accuracy:

Using the National Standard for Spatial Data Accuracy, the dataset tested 16.8 feet horizontal accuracy at 95% confidence level.

b. StreetMaps road network, Horizontal Positional Accuracy:

Using the National Standard for Spatial Data Accuracy, the dataset tested 341.5 feet horizontal accuracy at 95% confidence level.


As we can see from these statements, the City of Albuquerque dataset is much more accurate than the StreetMaps USA dataset.  We can say (based on this sample test at least), that 95% of the City of Albuquerque-mapped street intersections lie within 16.8 feet (horizontally) of their true positions based on the DOQQ's.  However, we can only say for the StreetMaps USA map that  95% of the samples tested lie within 341.5 feet (horizontally) of their true locations.  This disparity was easily seen from cursory visual comparison of the street network datasets and the orthophotos.  The City mapped streets were always very close to their counterparts in the DOQQ's, but those of StreetMaps varied considerably from being fairly close (generally in the center of the map) to hundreds of feet away, and skewed at large angles (generally at the corners and edges of the map).