My students often request to do work that is relevant and real. Visualization of data is one thing that is real and relevant in the worlds of statistics, business, research, politics, sports, art, and many more fields. By transforming a large set of numbers into a clear picture that tells a story, one is able to lessen the mystique of otherwise scary, intimidating, and large sets of numbers. With the advent of new technologies, people can not only collect large sets of data in new ways, but they can also bring it to life in fascinatingly unique ways.
My statistics class recently undertook a project that required them do just that; collect their own data and display it in a truly unique way. They accomplished this goal by using a tutorial from the blog FlowingData.com and an iPhone app, RunKeeper. For over a week, students recorded where they walked using the RunKeeper app, which tracks your location using GPS. Run keeper does a great job of showing you details on each route. However, it is hard to make sense of trends of where everyone walked if you can only see one route at a time. To combat this problem, the students downloaded the over 3900 longitude and latitude coordinates, complied them into one spreadsheet, and then wrote a program that connected the dots and overlaid all of the maps together.
Each student used their creativity to make the same data set look different on a map. Some of the obvious differences are color and the inclusion of a google map. However, if you look closely, you will see that the transparency and size of the lines vary as well. One student got so engaged in the project that he researched different ways to show the data and made a heat map as well.
It's one thing to have a map, it's another thing to make sense of what it tells you. The students already wrote up their own analyses, but I want to hear from you. Feel free to post in the comment section different things that jump out to you when you look at these different maps.
Source used for writing Program:
"How to Map Graphing Paths in R." How to Map Geographic Paths in R. Web. 14 Nov. 2014. <http://flowingdata.com/2014/01/28/how-to-map-geographic-paths-in-r/>.