Lesson 6: Representing Volumes and Surfaces - Week 2
Paulina Fernandez Luengo
This week’s lesson was focused on becoming familiar with and applying the following GIS concepts and techniques:
Part II: Using elevation data from a Digital Elevation Model (DEM) in the format of an Interchange file, we were able to create a hillshade, or shaded relief map with contour lines (Figure 1 below).
Figure 1: Oregon Hillshade and Elevation Contour Lines (click image to open full-size)
The hillshade was created with an Azimuth of 315, an Altitude of 45, and a Z Factor of 1. The isoline contour interval is set at 200 meters. The highest elevation on the map is 4202 meters, so I chose a contour interval that would most closely capture this peak value. For example, a contour interval of 250 would generate the highest contour line at 4000, leaving out the top 202 meters. I also didn't want to make interval too small, such that the map reader couldn't distinguish between the lines. Actually, at the 50-meter interval, there are portions of the map that don't even look like line at all, but rather blotches of color. An important design factor to consider that wasn't needed for this exercise is the print size, or the size that the map would be viewed at onscreen. In real-life, I imagine that I would adjust the contour interval depending on how large the final map output would be.
Part III: Figure 2 (below) depicts interpolated precipitation values for the state of Oregon for December 2003. An Inverse Distance Weighting (IDW) interpolation method was used based on known precipitation data taken at each of the meteorological data collection stations (represented as point features).
Figure 2: Interpolated Precipitation in Oregon, December 2003 (click image to open full-size)
For the interpolation raster, I chose a color ramp with increasing hue value of the same color to represent the continuous nature of the data being depicted, with darker values signifying greater precipitation values. The interpolated contour lines are divided into 3 classes (1-9, 10-17, 18-29) and correspond to the 3 classification breaks of the raster surface data. In order to make the lines more easily discernable, I employed the use of varying line thicknesses and styles, with a thin line (0.5 width) for the first class, a thicker (1.0 width) dashed line for the second class, and a thick (2.0 width) solid line for the third class. I decided to keep the color of the contour lines black so as not to make the map look overly busy with colors. I think the use of thickness and style is sufficient to tell them apart from one another, and adding another color factor is unnecessary. If I had made the raster surface grayscale, then color on the contour lines would add visually to make the map look more interesting, but in this case, I think there is already enough color.
The map in Figure 3 (below) is similar to the map in Figure 2, with the addition of a hillshade map.
Figure 3: Interpolated Precipitation in Oregon, December 2003 With Hillshade (click image to open full-size)
A transparency of 60% was applied to the hillshade; I found that by increasing the transparency, the colors of underlying interpolation raster (blue hues) are more easily distinguishable but at the same time, I didn't want to make the transparency too high or the hillshade layer would lose its 3D effect, thus compromising the overall visual effectiveness of the map.
Hillshade Specifications - I came up with four very different images, but the one I ended up selecting for the map has the following specifications:
Z Factor: 1
For comparison/contrast, follow this link to see all four hillshades I came up with as well as their specifications. They are all so different. The fourth image looks reversed, tricking the into the illusion that peaks are valleys and vice versa, since the Z Factor is high and the sun is set to the Southeast, creating a "pseudoscopic effect."
Gruver, Adrienne (2008).
Cartography and Visualization, Lesson 6, Representing Volumes and Surfaces -
Week 2. The