Positional Accuracy: NSSDA Standards In GIS
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiN3azVdb0ECRNo5K6f6-TPqVBIwJ-k84hAwep9_p4WMU6tJpfAmKbbfNQ19UCFTl9CcG8V0Ws2WM92hWsffd-Z08r97iReEVKDlZBRxpkUXgwYbwVAxkB6h-hykZC76DgUbcbtfwyI7j3g/w640-h472/Deliverable+1.jpg)
Today We Are Using Imagery and Street Data I am creating digitized test points in the city below to compare a dataset of streets to a more accurate, local dataset of city streets. We are doing this to calculate the horizonal accuracy of the streets, by dropping points on intersections that both datasets share in commonality. This is what my Test data looked like with the new streets data. I am comparing these streets to the ABQ local streets (which are extremely accurate) to compare this sample of USA streets to the real data or "accurate street data". The red points are the intersections in the city. As you can see, these points were scattered to all 4 quadrants of the city for unbias distribution. Yet there will still be some bias included in the data due to the user, me, picking the intersections and the points. I also was using very high resolution oblique imagery to make sure I was comparing my test data to the real center point of the intersection. There is bias in i