GIS6005: Proportional & Bivariate

 

    This week's lab had us exploring the uses and applications of Proportional and Bivariate maps in GIS.

    The proportional map, as shown above, is useful when you want to show how much of something exists in different places, not just where it exists. To create this proportional map, we had to use negative values which is not something the program likes to work with when creating classes for the proportional symbols. In order to generate two different symbols as shown, two of the same features were needed and the negative values turned into positive ones. This process was easy enough, but the legend was a different story, as two legends would have been cumbersome in the final map design. We had to add some "false" values into the negative values (job loss) feature attribute table so that when the class breaks were made, the accompanying symbol size for both features would have the same breaks. After the sizing issue was corrected, creating the legend and plotting the symbols with outlines to stand out over each other was a breeze, leading to the map design we see here. 

    In order to create the bivariate symbol map that we see above, some additional data needed to be created in order to correctly symbolize the relationship we were trying to show. The data in this case was the percentage of those obese, and the percentage of those who were physically inactive by county. We first organized our data and found which fields we would use in our analysis, in this case % obese and % inactive. Using these fields, we individually utilized ArcPro's quantile class breaks to produce the most even groups. We took those breaks from each field and created new fields where the values under, between, or above the breaks were labeled with either a 1, 2, or 3, or an A, B, C, and then combined into a new final column A1, A2... etc. This new field now specified which county was "low obesity, low activity" with an "A1" designation, up to "high obesity, high inactivity" with a "C3" designation. Using that new field, creating the bivariate map was as simple as choosing unique values, converting the legend into graphics, and rearranging the legend into a 3x3 square indicating the progression of the values with accurate, yet minimal labeling. 

    The coloration choices for the symbology of the legend was no easy task. A good way to find a bivariate color scheme is to start by choosing a 3‑class sequential color ramp for one variable, then pick a complementary color on the opposite side of the color wheel and create a second 3‑class ramp for the other variable. Once you have those two ramps, you fill in the 3×3 grid by adjusting hue, saturation, and value so the colors blend smoothly from one corner to the other. The lab recommends using HSV instead of RGB because it makes it much easier to control these gradual changes and create a balanced, readable bivariate scheme.

    I tried numerous methods to find an appropriate color scheme, AI, Google searches, trial and error. I just was not satisfied with any of my results and spent way more time trying to create a color scheme than worrying about the lab itself. Ultimately, I scrolled down the lab a bit more and saw the color scheme examples from our reading this week containing the hexadecimal values. In ArcPro’s color editor, there is an option to use a HEX number. I chose my favorite scheme, typed in the HEX, and the HSV auto populated and I was satisfied with the scheme