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Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures.

The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection. 

Map 1: City of Albuquerque city map data. 

Map 2: City of Alburquerque SteetMap data

When selecting a random intersection to place the points within each quadrant, I choose a location that had data from each of the three layers (StreetMap, City, and reference). The lower left quadrant had a large area missing the reference base map for example, and did not meet my criteria.  I also made sure the points were separated at least 10% of the diagonal distance of each of the 4 quadrants. 

After creating the 20 random points in each of the three layers, I added their distinct coordinates via the Add XY Coordinates (data management tool). I came across issues using this tool when trying to add the coordinates to the City feature class; instead of coordinates, I got Null in each cell. I was not able to resolve the issue with this tool in this layer and ended up using the Absolute X,Y,Z tool to find the coordinate of each point and then manually input it into the attribute table. [My classmates later gave me some great alternatives: 1) create 2 new fields with floats and use calculate geometry 2) make sure one point is not selected as will cause an error.]

Next camethe data analysis. This step was a simple chug and plug in excel using the templates in the NSSDA handbook. (NSSDA, 1999)

Table 1: Positional Accuracy Analysis data using City data


Table 2: Positional Accuracy Analysis data using StreetMap data

In conclusion, utilizing the National Standard for Spatial Data Accuracy (NSSDA) steps if found: 
  1. The City of Albuquerque city-data tested 23.93 feet horizontal accuracy at 95% confidence level. 
  2. The StreetMaps data tested 232.78 feet horizontal accuracy at 95% confidence level.
I can confidently say the city data is significantly more accurate than the StreetMap data; specifically by 208.85 feet more accurate at a 95% confidence level. 

The Positional Accuracy Handbook noted to multiple the RSME by a factor of 1.7308 to determine the horizontal accuracy at a 95% confidence level, or 1.9600 to find the vertical accuracy at a 95% confidence level. I thought it is interesting that these factors were additionally researched to determine their continual accuracy. For example, the Federal Geographic Data Committee further evaluates positional accuracy of large maps at ground scale. (FGDC, 1998)

Sources:

Federal Geographic Data Committe. (1998) Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy. https://www.fgdc.gov/standards/projects/FGDC-standards-projects/accuracy/part3/chapter3

National Standard for Spatial Data Accuracy. (Oct 1999) Positional Accuracy Handbook.  https://www.mngeo.state.mn.us/committee/standards/positional_accuracy/positional_accuracy_handbook_nssda.pdf 






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