Piece of Eden GIS Blog

  This assignment delved into TIN and DEM’s elevation data models. The main difference between the two is that raster uses DEM while vector data uses TIN. I began by visualizing a 3D TIN of a valley/gorge using a TIFF image. (Image 1 below) For my second analysis, I converted a DEM to TIN, reclassified the data, and used a weighted overlay to develop a suitability model for a ski run. When comparing the DEM and TIN layers side by side, the TIN has more dimensionality and elevation texture than DEM does. Because of this, I would think that the TIN layer is more accurate. Overall, the smaller the elevation slope the greater the difference between TIN and DEM. In this area, DEM understates the slope while TIN overstates it.  I created a couple more models with TIN, and really enjoyed playing around and getting to know how to work with TIN models. The biggest differential element I learned about was in the last step. I found that when creating a modified TIN, using Edit TIN tool, thi

The goal of this analysis was to determine which road dataset (TIGER or Street Centerlines) was more complete for Jackson County, Oregon. Completeness in this case is defined by which dataset layer had a greater total length per grid subsection (267 grid subsections total) and greater coverage of the entire area of study. These two main points of my study were derived from the Haklay study. ”After gauging the level of positional accuracy of the OSM dataset, the next issue is the level of completeness. While Steve Coast, the founder of OSM, stated ``it's important to let go of the concept of completeness'' (GISPro, 2007, page 22), it is important to know which areas are well covered and which are notöotherwise, the data can be assumed to be unusable. ” (Haklay, 2009) To begin with, I used the Summary Statistics tool to find the overall completeness of each layer within the study area. Tiger roads had a total of 11382.7 km of length while Street Centerlines only had 10873

 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

 In this assignment, we analyzed 50 points collected at the same location via a GPS handheld device. Through these collected points we determined their precision and accuracy.  I first determined the mean of the points collected ("waypoints") by using the summary statistics tool, and found the exact coordinates of the "Average Waypoint" via the Absolute X,Y,Z tool. I re-projected and spatially joined the layers. Lastly, I created three new fields to determine the 50th, 68th, and 90th percentile.  Map1: GPS datapoint distribution and precision/accuracy analysis My horizontal precision for the 68th percentile is 4.5 meters. The distance between the "Average Waypoint" and the true reference point is 3.78 meters. Horizontal precision looks at the "consistency of a measurement method,"  and aims to provide "tightly packed results." (Bolstad, 2016) Horizontal accuracy on the other hand "measures how close a database representation of an