Piece of Eden GIS Blog

In this assignment, we learned about four different interpolation methods: Thiessen, IDW, and Spline Regularized/Tension. We used these methods to analyze the Biochemical Oxygen Demand (BOD) in milligrams per Liter in the Tampa Bay of Florida.  The first method, Thiessen interpolation, is a widely used method mainly because of its ease of use and high accuracy when using a large sampling density. Because this method assigns a value based on the nearest sample point, it does not do well with continuous data. However, because of this same attribute, this method can be highly beneficial when dealing with data that is oddly shaped or ends abruptly. Model 1: Thiessen IDW interpolation draws in the theory that points closer to each other are more alike.  Model 2: IDW The biggest difference in the Spline layer from the other two methods is it has a much smoother expression. This makes sense as the spline interpolation runs through each data point and aims to smooth out the surface el...

  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 ...

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 f...

 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 ...

This week I learned how to utilize ArcGis Pro to make out the pathway of a past hurricane, Hurricane Sandy, along the North East coast of the USA in 2012. Later, I was also able to assess structural damage on the coast of New Jersey from Hurricane Sandy.  Below you can see the map I created of Hurricane Sandy's pathway/timeline. If you look closely, you can see via the symbology what category storm Sandy was along its path along with information on its wind (mph) and pressure (barometer) information.  In the second part of the lab, I assessed the structural damage of Hurricane Sandy in a select area in New Jersey. I created a new point feature class and subsequent domain with categories no damage, affected, minor damage, major damage, and destroyed to categorize each parcel. After creating the points and designating their damage category, I analyzed the data future by determining what damage feel 0-100m, 100-200m, and 200-300m from the coast.  1.     ...

 For the first part of this module, I compared before and after pictures of the New Jersey coast, pre and post Hurricane Sandy. I created DEM's from lidar data to compare the before and after. Below, you can see the magnitude of erosion and accretion on the Jersey coast due to the Hurrican Sandy in 2012.  In the second portion of this lab, I created a storm surge impact model for Collier County, Fl. This was a very educational assignment and I learned a lot. We not only created a storm surge model for the coast of Florida, but we also did so utilizing both Lidar and USGS DEM data to compare accuracy. I first began by converting the Lidar layer to meters, and then reclassified both to only include data where the elevation is under 1 meter. I used the region group tool to clump together into one large area the land along the coast, and isolated it (via select by attributes) to only analysis this single large connected area. Lastly, converted that large area for both Lidar and US...