GIS 6005 - Communicating GIS - Module 6

  

Once my layout was completed, I tweaked my symbol classes to make the job loss red circles, and job gain blue circles the same size, relative to the equivalent number on the opposite side.  Then, I added another copy of the job gain category, and changed the symbology to hollow circles with black outlines. (This was done in order to include all circles, and communicate that the number associated is symbolized by the size of the circle.)

After ungrouping the legend, I stacked the circles in order of size and labeled them with appropriate labels. Then, I created a secondary legend that explained the colors displayed on the map, and placed it above the first legend. Between the two of these legends, the colors, as well as the sizes of the circles are communicated, and the same circle size can be applied to all circles.

Color values presented are in the HSV color model. (Transparency is set to 0 for all values)

A1 – 0, 0, 96.08

A2- 171, 37.56, 80.39

A3 – 175, 99.02, 40

B1 – 42, 43.95, 87.45

B2 – 92, 11.87, 66.08

B3 – 156, 62.89, 38.04

C1 – 33, 92.86, 54.9

C2 – 45, 70.53, 41.25

C3 – 92, 43.17, 35.88

I began by generating a dividing color ramp in Color Brewer, and selected 9 classes. I chose the middle value as A1, then every other value in each direction as the next value diverging from A1. (Teal for numbers, Brown for letters)

Next, I calculated the rest of the table by adding the RGB values of opposing extant cells, and dividing the results by two, as we had done in the chloropleth module.

If a color palette is chosen well, and the values are easy to distinguish for the typical person, two distinct values can be simultaneously communicated in a single map. While three value classes may not be very thorough in communicating specifics of the degree for each value, the correlation can be communicated very well by showing areas of concentration for both variables.

GIS 6005 - Communicating GIS - Module 5

Brief discussion of your design choices for your selected visualization technique(s).

            I began by creating the pie chart. Because I wanted to include an additional data to accompany my insufficient sleep map, I decided to select a matching color scheme. The entirety graph shows a breakdown of all U.S. counties considered in this study, and the labels reveal how much of the population within these counties reports insufficient sleep. While this may not have been the best way to label this data (it may be confusing whether the label pertains to the general variable size, or a fraction of said variable), ultimately, I found this way to be the most detailed and informative.

            The comparative line chart was generated with data obtained from a single source site, (data.worldbank.org) which cites multiple sources (The UN Population Division, and U.S. Census Bureau) itself. I looked for the change of life expectancy over time in the United States, and decided to add several other countries from all over the word to give the viewer a contrast. The countries were selected randomly, but I did want to show a somewhat worldwide distribution.

Briefly discuss what strategies you used in finalizing your layout.

            The layout was generated on a 17x22 landscape-oriented canvas. I began by creating several guides at the top, bottom, left and right of my canvas, in order to give my elements an equal amount of space from the edges of the paper. I repeated this process, and used more guides to divide the canvas into four distinct parts. I centered my map exactly how I liked it, and added it to the upper left quarter of my layout. I created the second map layout from the first, and placed it in the lower left.  

            I created a number of neatlines, and placed them around my canvas. First, a large gray canvas across the whole page, then smaller, black rectangles over which all data will be placed. Staying consistent with the theme of the project, all of the charts were exported with a dark grey-black background. The data represented on each chart was colored to match its respective map, unless the data represented showed unrelated variables, in which case they were assigned various colors.

            I tried to keep the font consistently dark-red, except for the Sleep map, for which I made it purple to match its data. The overall title was placed on the upper right, and the graphs were distributed for the best possible fit. The left-over space was used to insert citations for external data.

GIS 6005 - Communicating GIS - Module 4

Color ramp	Notes	Stepwise Intervals
Linear progression		R	G	B
	·         After setting the parameters for the darkest color based on the suggestion, I attempted to make my brightest color comparable in hue. ·         The formula for the R scale progression: 255-150=105 105/5=21 ·         The formula for the G scale progression: 130-0=130 130/5=26 ·         The formula for the B scale progression: 225-75=150        150/5=30	R150 R171 R192 R213 R234 R255	G000 G026 G052 G078 G104 G130	B075 B105 B135 B165 B195 B225
	·
Adjusted progression
	In order to make a legible adjustment, I decided to make the 4th value in each column the half-way point between the darkest and the lightest. For the red scale:255-150=105 105/2=52.5 (then add 52.5 to the darkest value) For the green scale:130-000=130 130/2=65 (then add 65 to the darkest value) For the blue scale:225-75=150 150/2=75 (then add 75 to the darkest value) The remaining values were placed intuitively, and rounded to the nearest point. Made to equal approximately 1/3rd of the difference between 1 and 4 for the second and third value, and an approximate half way point between 4 and 6 for the fifth.	R150 R166 R184 R202.5 R227 R255	G000 G022 G044 G065 G097 G130	B075 B100 B125 B150 B187 B225
ColorBrew
	·         The values obtained from Color Brewer don’t seem to follow any easily definable pattern. While the blue and green levels seem to gradually increase on the spectrum, the red does not systematically increase. The amount of red in the 4th value drops below that of the second and third values, and spikes at the end to the highest level to create an almost-shite hue for the brightest value.	R152 R221 R223 R201 R212 R241	G000 G028 G101 G148 G185 G238	B067 B119 B176 B199 B218 B246

Brief discussion of your results. How do the linear and adjusted progression color ramps compare to the results from ColorBrewer? (Aprox. 150 words)

            When working on my linear progression, I selected a pre-determined purple color as my starting point. Then, in compliance with the lab instructions, I created my brightest color. I did so by estimating what the end of the range would look like, and attempted to keep the hue as close to my dark purple color as possible.  After completing the spectrum, I have used the same starting and ending point to create the adjusted progression ramp. Despite my attempt to broaden the spectrum around the dark end of the scale (as described above), the color ramps look very similar at a glance. Most of the values across the spectrum differ only by a few points, making it quite hard to distinguish without a close examination.

            The spectrum generated in Cold Brewer began with a very similar dark purple to the previous two. However, because I selected a multi hue spectrum, the degree of ‘redness’ across the spectrum does not increase regularly like in the first two color ramps. This makes for a much more legible color ramp, as the brighter colors are not only brighter, but also appearing more blue than those in the linear and adjusted ramps. The brightest value in the Cold Brewer ramp is also significantly closer to white than the brightest value in the previous ramps making the overall spectrum broader and easier to read.