A. Starling's GIS Blog

                In this lab, we explored two types of elevation data models - Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN). The DEM is raster-based and information is stored in grid format, while TIN is vector-based and uses irregularly spaced points that were calculated according to the connections of sets of triangles for contouring. Because of this, the DEM has smoother curves in its contour lines, while TIN has pointed/jagged contour lines. The DEM contour lines in this instance also have a few more lines due to the data parameters.                 The TIN contour lines are likely more accurate, as TIN can differentiate between more complex terrain than DEM contour typically can (depending on resolution). TIN uses irregularly placed elevation points that vary based on data density, but DEM is a grid with elevation values stored within the grid cel...

     The goal of the analysis for this week’s study on accuracy assessment was to determine which dataset, TIGER Roads or Street Centerlines, were more complete for Jackson County, Oregon.  In this lab, we are measuring completeness using the assumption that the more road there is, the more complete it is.      We could use the length of the roads to determine completeness. After ensuring data was in the same projection, we looked at the statistics (Attribute Table -> Visualize Statistics) to determine the sum of the road segment lengths: Street Centerline : 10873.3 km TIGER Roads : 11382.7 km Because this lab measures completeness based on road length (the more road there is, the more complete it is) – in this case, TIGER Roads is more complete than Street Centerline. We went a little further in this lab to determine length of roads with each grid of Jackson County, Florida – see Map 1: Map 1: Percent Difference of Road Length between TI...

     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 “...

In this lab, we were tasked to determine the suitability of an area for housing development. For this, we must consider: l and cover, soils, slopes, distance to streams, distance to roads of the area. These five considerations will determine which areas in the location are suitable for development. Analysis First, I use the Reclassify tool to convert the values within the “landcover” raster into values of suitability. With the Polygon to Raster tool, I converted the “soils” polygon feature class into a raster. I then reclassified using the Reclassify tool turning soil class into suitability ratings . I used the Slope tool to create a slope of the “elevation” raster in degrees, and   the Reclassify tool to class slope (in degrees) into suitability rating. I used the Euclidean Distance tool to create a distance to rivers, and the Reclassify tool to class distance to rivers into suitability rating. I repeated the above steps for the “road” line feature class (creating a d...

       Hurricane Sandy (2012) path     This week’s lab in Applications in GIS allowed us to assess damage from Hurricane Sandy in Ocean County, New Jersey. To do so we used provided pre- and post- Hurricane Sandy rasters and compared both to each out using the Swipe tool. This allowed us to see the damage and determine where each parcel would fall under in categories of Structure Damage, Wind Damage, Inundation, and Structure Type.  ArcGIS Pro screenshot of structure damage in an area of Ocean County, New Jersey I created a new feature class “Structure Damage” as a point feature and digitized each parcel with a point indicating level of damage. I then created a new feature class “Coastline” as a line feature and digitized the coastline parallel to the study area. I used the Multiple Ring Buffer tool to create a buffer around the Coastline feature class with parameters to determine structures impacted within 100 meters, 200 meters, and 300 meters. Then ...