Chapter 8 of Nathan Yau’s book Visualize This: The FlowingData Guide to Design, Visualization, and Statistics covers the most popular form of visualization, thought not one traditionally thought of in regards to data. The chapter breaks down plotting points and creating data to display on maps, before turning to different ways to display data through the map medium.

1. Two essentials for determining what goes where on every map are latitude and longitude, and to get those, you need to use something Yau refers to as geocoding. By using a handy service (Yau suggests the free website geocoder.us), one can plug in an address and wait while the service queries its database before spitting out the latitude and longitude for wherever in the world that address might be. (p. 273)
   
2. Once the data has been created, you can begin to plot the points. Yau recommends thinking of maps in layers, the bottom layer being the map, with the next layer being whatever type of data you choose to share in the space. Each different set of data points you chose to show (say for instance, every McDonald’s and Burger King in North Central Florida) is a different layer. (pp. 277-278)
3. Scaled points can add depth and spatial understanding to a map hoping to show proportions. The book uses an example of bubble sizes as they relate to adolescent fertility rates, and just by examining the map and understanding the purpose of bubble sizes, you can easily and quickly identify that the highest rates are located across the continent of Africa, even without a legend to help quantify the size. (pp. 283-284)
4. Another interesting way to create data on maps (and one of the most popular in regards to mapping regional data) is through the use of color, what is referred to as a choropleth maps. While the bulk of this section contains his coding tips and tricks to graphing a choropleth map, I found the information on map shading particularly intuitive and useful. (pp. 286-302)
5. Touched upon in the intro and expanded upon toward the end of the chapter, mapping in relation to time and space is another handy and engaging way to display data. The book shows different stylistic choices when it comes to time progression, with multiple maps in the same space being differentiated by progressing headlines, but the method I found to be the most visually appealing was the animated one. Yau’s example of the growth of Walmarts and Targets across the country was very interesting, and the coding that follows, while complex, could be a very handy tool in the future. (pp. 303-325)