Piece of Eden GIS Blog

 This module was extremely helpful, as we dived deep into projection systems. Through the lab, we analyzed UTM, State Plane, Albers and determined when they are the best fit, as well as how to project them properly.  A handful of tools we utilized are listed below:  Projection Wizard Geodetic Parameter  Search For our final map, we choose a state in the USA, and determined which projection to use; UTM vs State Plan.  I choose the state Utah because I have always wanted to go to Zion! I decided not to use the State Plane because there are three different ones that subdivide the state. Since state plane’s do not work here, I opted for UTM over Albers because we are looking at a relatively smaller area not close to the poles. I tried a couple of different UTM NAD83 Zones and landed on UTM NAD83 Zone 12. In this zone, Utah completely lies within one UTM Zone.  

This module explored the use of typography in map-making. For this assignment, I had to label larger cities/islands, waterways, parks, landmarks, neighborhoods, and topographical features in San Francisco, California.  My thought process for labeling Map 1 below is as follows.  General cities and islands: I used basic large black font, for these main locations' features to stand out. I placed them centrally, in a spot they wouldn’t block other features. I used a blockier font (century gothic) for easier legibility. Water Features: I used italicized blue font to signify water. I used a lighter blue for the Pacific Ocean, as it’s not local information. I used the font commonly used for water features, Bodoni MT Italic, for distinction and legibility. Park Names: I used a green front placed above the area of the parks. I used a blockier font (century gothic) for easier legibility. Landmarks: I used a bright orange to stand out and placed it over the bridge. I used a blockier font...

 In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution. I resampled the data using the Bilinear technique and found the following effect on DEM resolution.  As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether.  In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional distri...

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...