Greetings all!
It is absolutely crazy that this is the last module, minus final, for this class! Where did the time go? This whirlwind experience of a class is culminating with proportional symbols and one of the more unique applications of choropleth styling. That is the multivariate mapping method. In this case I am taking two different variables and visually depicting the trend between them across an area. This final week explored making meaningful proportional symbol legends, establishing an effective color scheme for bivariate choropleth mapping, and creating custom legends by manipulating graphical elements.
The first map that I will present below is a proportional symbol map which maps the changes in the number of jobs by state. While this is only one variable being mapped, the unique thing is that there are states with positive and states with negative values. So, cumulative gains or losses, being presented. The proportional symbols provide a visual representation of the magnitude of jobs gained or loss. One tricky part of this map is that there are far larger gains than there are losses. So despite the symbol type (circles) being fairly close to one another in size, there are large differences possible on the positive side as compared to the negative side.
For this map, I
went with two separate nested legends. This is predominately because of the (as stated) incredible difference between the high value for positive growth and high value
for loss. The Growth section has a much larger proportional symbol range than
the loss side. Even though the smallest symbol for growth is portrayed as the
same value for the largest symbol for Loss. That is a bit of an illusion when
looking at the two comparatively. But in this regard the size difference is
key. Additionally, the two symbols are oppositely colored. Blue for positive
change, with a green background, and red for a negative change with orange
background.
The second map below focuses on comparing obesity and inactivity statistics at the county level.
Brewer 2016, Chapter 9 expresses that multivariate mapping excels
at showing the relationship and distribution of multiple related variables. In
this case, I am applying a sequential – sequential arrangement to convey the
interaction between these two related variables. In the previous labs, using
the same data set as an example we learned that the dot plot effectively shows
that the two variables are correlated. This visual depiction now shows the
strength of the relationship and areal distribution of both factors combined.
There are significant areas of low inactivity correlated with low obesity (so
more active people with less obesity challenge). There are also significant
areas of high inactivity percentages combined with high prevalence of obesity.
Unlike the previous labs where we focused on a traditional
or single variable choropleth, this bivariate style provides a direct visual
comparison of the spatial trends. The 3 x 3 sequential color shades give a strength
guide to visualize the interaction between the two. In addition to seeing areas
that are highly correlated, we can identify the areas that are particularly one
sided or less directly linked, but favoring one over the other variables. Ultimately,
the use of this method allows us to better understand the regional dispersion
of both variables simultaneously. While this is informative, one accompanying
map that I think could provide even more context would be a population
distribution choropleth.
While some areas have high or low correlation, we don’t get
a sense of how large or small the issue is because we aren’t seeing the total
underlying population. A large population
county with a low percentage may still have more people impacted than a low
population county with a high percentage problem. Thank you.
v/r
Brandon
No comments:
Post a Comment