Module 6 GIS 6005

This week Module 6 has introduced us to proportional symbol and bivariate mapping. During this module we learned to explore data and determine what method is best used to present the data particularly when there is more than one variable and there is a link or potential link between the data. 

We started off with proportional symbol mapping. Proportional symbols are good way of simplifying data on maps and making it easier to understand. Unfortunately proportional symbols take a good amount of effort to get just right. Symbol size, shape and color are all vary important when using proportional symbols. 

Our task was to make a proportional symbol map that demonstrates job losses and gains in the US between 2007 and 2015. When working with this kind of data we know we are going to encounter negative values. ESRI ArcPro is unable to symbolize negative values. In order to overcome the inability of ArcPro to symbolize negative values we had to create extract negative values from our original dataset and create a new feature class with negative values converted into positive values by utilizing the field calculator. By creating a second feature class we could represent negative values as a proportional symbol. When showing two feature classes with the same data it is important to keep parameters for your symbology equal or the reader could be misled. My final proportional symbol map below. 

The second part of Module 6 had us create a bivariate map. A bivariate map is a helpful way of mapping two distinct variables that have some relation. We created a map that shows a link between inactivity and obesity in the US. Showing a link between the data starts by creating physical links in the data/attribute table. In order to create a bivariate map the data from the two variables needs to interact with each other. To prepare our data we had to come up with different statistical classes and then assign values to each class in a manner that would relate to the other variable. For example areas with high levels of obesity were issued a value of 1 while areas with less obesity were assigned the number 2. For inactivity data we assigned a letter with A being designated for areas with high inactivity and C for areas with low inactivity. To carry out the classification of data we had to carry our various search by attribute and field calculator operations. Eventually the data from both variable (1,2,3 and A,B,C) was combined into a single field that could be mapped. We learned that colors and legends are the basis of bivariate maps and are crucial to creating a good product. 

My final bivariate map