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

 Module 5 Communicating GIS

During this weeks module we learned about using and presenting analytical data. We downloaded county health ranking data and used it to create our own infographic. Our infographic was made up of two maps and two charts showing associations between the two data variables that we selected. We also had to search for other data sources and present that data in a creative manner. I chose to use data related to infant mortality and food insecurity. I figured that there had to be a correlation between the infant mortality and food insecurity. 

For my infographic design I had to carefully plan my layout considering potential readers and the serious nature of the topic that was being presented. I wanted to keep frills to a minimum and make sure that my infographic was easy to read and straightforward. I chose somewhat muted colors with the exception of a bright red that I used to show extremes and data and to highlight some of the data presented. I made sure to use plenty of guidelines in my ArcPro layout to ensure that all the items I used were properly sized and aligned. I used a couple of pie charts to visualize some data that I included. I thought the pie charts were simple to read and did not draw attention away from other items on the document. Below is my final infographic.  

 GIS 6005 Module 4

This week we are working with colors and choropleth maps. We took a deep dive into the meaning of HSV in terms of colors and we learned how to use different colors for different types of data. We learned about differentiating data into categories such as qualitative, sequential or converging to determine how to best represent it on a map and in a legend. 
We got to experiment with color ramps in ArcPro and in ColorBrewer. As part of our assignment we had to create our own sequential color ramps. Below is the color ramps I created. When created the color ramps we had to carry out calculations and determine what values to use on an RGB scale to create a legible and distinctive color ramp. 

  

The first two color ramps are ones that I made by varying RGB values. I derived my second color ramp by choosing RGB values one third greater and one third less than the average difference between each color. The third color ramp is one that I derived using data from ColorBrewer. The ramp I created using ColorBrewer  data has more differences amongst the colors. ColorBrewer added green to the ramp at the lower end to create lighter more distinct colors. 

The final portion of our assignment required us to make a map depicting population change in individual counties in Colorado. The data provided allowed to compare the change in population between 2014 and 2010. Below is my final map. 

I chose to use a diverging theme to represent my data as the data has a center value and seems to diverge away from the middle value. To best represent my data I chose to use a map with varying hues. The varying hues provide a clear distinction of the counties and also are good at representing the change in the population. I wanted to keep my legend simple and legible. I did not have much room for the legend so I had to limit features such as borders and titles. I added percentage signs to all values as I did not have room to explain that numbers were presented in percentages. 

 GIS 6005 Module 3

This week in class we learned about terrain visualization. We learned the fundamentals of terrain visualization and we got to practice some techniques for enhancing terrain in our maps. We carried out various exercises with digital elevation models and hillshade analysis. Along with terrain visualization we learned how to best represent contour lines and their associated labels. I really enjoyed learning about hypsometric tinting. I am looking forward to incorporating hypsometric tinting in my regular work. As part of this week's assignment we also got to experiment with terrain visualization in 3D scenes. 

Below is one of the maps that we had to produce this week. Our goal was to show land cover within Yellowstone National Park while effectively representing the terrain of the park.