A. Starling's GIS Blog

     Within this study analysis, I compared different sets of street/road intersection data for Alburquerque, New Mexico that was collected separately by the City of Alburquerque and StreetMaps USA . Using an orthophoto base layer as the reference for this analysis, I then compared the accuracy of both datasets using the procedure set out by the National Standard for Spatial Data Accuracy (NSSDA).      I started off by sectioning the study area into four quadrants, which allowed for each section to have 20% of the points needed to determine data accuracy and I ensured that points were at least 10% of diameter apart. Per NSSDA (1999), “Twenty or more test points are required to conduct a statistically significant accuracy evaluation regardless of the size of the data set or area of coverage. Twenty points make a computation at the 95 percent confidence level reasonable.”     There were 208 sub-quadrants, and these were evenly divided to create fo...

 This lab provided us with the opportunity to compare accuracy and precision of (50) points that were collected using a GPS unit.     Accuracy is the difference between the reference point/true value and the measured point(s)/value(s), while precision refers to how close measured  point(s)/ value(s) are in relation to one another.  Horizontal accuracy is determined by calculating the difference in distance between the reference point and (in this case) average waypoint. We used the Measure tool in ArcGIS Pro to determine the distance between these two points (3.27 meters apart). Horizontal precision in this lab focused on determining waypoints that feel within the 68 th percentile from the average waypoint. We determined vertical (elevation) and horizontal po ints  that were within the 68 th percentile, as the 68 th percentile is the most commonly used measure of precision and indicates the distance within which 68% of observations (points) are....

       For this portion of the last lab in Applications in GIS, we were tasked with determining potential corridors for black bears between two protected areas within the Coronado National Forest, Arizona. Analysis I used the Reclassify tool on the “elevation” raster to indicate the suitability (“New” column) between elevation range: I similarly used the Reclassify tool on the “landcover” raster to indicate the suitability rating (“New” column) of landcover values: I then used the Euclidean Distance tool on the “roads” layer, then used the Reclassify tool to apply distance values. I used the Weighted Overlay tool to combine the output reclassed rasters for land, elevation, and road distance. I used the Raster Calculator tool on my weighted overlay raster with the below expression: (("S4WeightOverlay" - 10) * -1) + 1 I then used the Cost Distance tool on the “Coronado1” polygon layer and separately on the “Coronado2” polygon layer – these would both be “...