Communicating GIS - Module 6 Lab - Proportional Symbol and Bi-variate Mapping

Hello Everyone!

This week's module was all about proportional and bi-variate symbol mapping. These two mapping styles presented some unique challenges and really opened my eyes to the possibilities of mapping in ArcGIS Pro. The most interesting map that I made was the Bi-variate map. This map type is unique because it allows you to map two variables in one map using a unique 3x3 color scheme. For my map, I chose to map the correlation between obesity and physical inactivity. My map can be seen below:

As you can see the color ramp legend allows you to show the two variables and their low medium high combinations. The color at the top right illustrates the highest presence of obesity and inactivity percentages while the lowest left shows the lowest of both. Unlike my previous posting for the info-graphic, this map allows you to better show the correlation between the two variables.

~Map On!

Communicating GIS - Module 5 Lab - Analytics and Info-graphics

 Hello Everyone!

This week's lab focused all on infographics and data analytics. These mediums have such a powerful potential to illustrate data in a meaningful way. For my infographic, I attempted to analyze and illustrate the correlation between obesity in America and excessive drinking. To my surprise, there is actually a negative correlation between these two variables even though alcoholic beverages are so high in carbs and calories. Below my analytic page layout can be found.

For this layout, I used a few interesting infographic modules. I first used a chart showing the increasing trend in obesity in the United States. I used a scatterplot illustrating the negative correlation between the two variables I was showing. Additionally, I used a chart showing the top three counties in the US with the highest percentage of obesity and excessive drinking and a bar chart showing the average nationwide percentages for my variables. Finally, I used two choropleth maps illustrating the distribution of my two variables. 

This infographic really was very eye-opening, I thought for sure that these to variables were correlated. As can be seen on the map, the highest percentage of the obese population tend to be in the south, and the highest percentage of drinking populations can be found in the north. But why is this? I believe there are a few factors as to why there is this negative correlation. The first is that the diet of the south is much different than that of the north where the south has a lot of 'comfort foods'. Additionally, the south is the home of the bible belt where alcohol consumption may be less due to religious belief/preference. Finally, the north consists of large beer powerhouses where high alcohol consumption is present.

~ Map On!

Communicating GIS - Module 4 Lab - Choropleth Mappin

Hello Everyone! 

This week's lab we covered choropleth mapping. Before I get into that part, I'd first like to share a bit about color ramps and their creation. Color ramps are comprised of colors which are composed of RGB (Red, Green, and Blue) these color values can be changed to create lighter and darker versions of a color. For example, when you increase the values together you get a lighter variation of that color and vice versa. Below you can see 3 variations of this: 2 I did on my own, and 1 generated by a software called Color Brewer.

As you can see, each progression has varying step values depending on what time of color ramp you were creating. Now that I've explained color ramps in some brief detail, let's see what they can be used for in GIS. Below is a Choropleth map of the population change in counties of Colorado from 2010 to 2014. 

Within choropleth mapping, there are a variety of classification methods. For this map, I used the Equal Interval classification which creates equal class intervals. Additionally, I used a Blue to Red diverging color ramp, counties with the largest population decrease are darker blue while counties with the largest population increase are in a brighter red. Equal interval classification for this data be presented the total range and gave the 0% class a nice middle ground among the data without having to get too complicated in the manual classification.

~Map On!