6 - Proportional & Bivariate

For our last module this semester, we focus on creating proportional symbols and bivariate maps. We started on creating a cities' population map for India to get familiar with proportional symbols especially when they are overlapping with other features. Good knowledge about  the principle  of hierarchical organization and visual contrast comes into place. For the India map, I focused on the following symbolization considerations:

Proportional Size: I used a minimum of 15 with maximum size of 90 so that the lower population areas would still be legible and visible to the viewers

Color Symbols: I used an orange color with a 50% transparency and then white outline enough to define its separation from overlapping circles. In addition, I used a lighten feature blend to smoothen the color overlays amongst overlapping symbols.

India: I used a gray scale color with a dark gray outline to create a visual contrast to the light gray base map that I added.

Overall, this makes an effective map to put the focus on the population data. The highest hierarchy in my symbols would showcase that my map is presenting the population count in cities using proportional symbols. I kept the legend the same using the orange color and then adjusted the classes to show 1000, 1,000,000 and 10,000,000 due to the large population range in India. I made sure that I add a white rectangular background to easily show hierarchy as to our map frame. I added an additional map frame to show where India is located around the Asia continent.

For the 2nd map, we are still using proportional symbols but for negative and positive increase values. We had to separate the U.S. States data showing the increase and decrease in jobs in order to visualize them in different colors yet the value for the proportional symbol should be the same so that only one legend will be used. I had to adjust manually the circle sizes to meet the expectations for both job increase and decrease. I made use of the same color scheme where I used orange with 30% transparency for the decrease and blue with 50% transparency for the decrease. I had a hard time creating a 4^th division for 100,000 jobs so I just sticked with using 3 values, but I know this can be improved because the range of values from 10,000 to 1,000,000 jobs is too large.

For our last concept for this semester, learning how to create bivariate symbology for  two variables that has positive correlation -  physical inactivity and obesity. Just a tip: Bivariate classification is already built in ArcGIS Pro so instead of doing it manually there is an option to use that symbolization directly. I made use of the colors teal and red and worked around on that in creating a color scheme. In that way, I can represent a darker brown red to show the high-high values. I used the teal for the high physical inactivity to show low values in lighter color and then the high values in a darker teal. Then for the percent obese I chose the red to brownish red color to provide focus that high inactivity and being obese also has a positive correlation and provide caution that areas in this color have high obese percentage.

You’ll notice that hue and saturation are low around the low-low to low-high area and hue increases a lot around the high-high area due to the darker colors. For my legend design, I created a separate choropleth line for both physical inactivity and obesity. This will help the map viewer to determine the 3x3 sequential color scheme on how it compares to combining the 2 variables together to create a bivariate representation. Then below I rotated my sequential color scheme to show focus on the both high-high values and then below would show the low-low values.

Again, when using choropleth maps where the viewer’s focus should be on the color scheme, it is important to use colors with less hue and saturation as background – in this case grayscale colors for the base map.

Bivariate choropleth mapping using 2 variable that has positive correlation allows us to quickly see the correlation between obesity and physical inactivity. This will allow us to cater to states that might require activities within the area to increase people’s daily activities which will help us to lessen obesity across states. In addition, we can create additional studies on why the southeast part of the US has high physical inactivity affecting obesity rates while the ones on the far northwest of the country, although they are active, they still have high obesity rates. This will then help us focus on activities that relates to our map results and finding  additional variables that affect obesity rates in the far northwest even though they are active and then different activities can be supported in the southwest by promoting physical activities to lower obesity rates.