We chose to use a dataset, in CSV format, containing data for 275 American metropolitan areas. Our job was then to decide the best way to visualize that data. We proposed different visualization methods, such as the two concepts below.
However, as our ultimate goal was to allow users to quickly and easily compare cities and trends while reducing the amount of clutter and occlusion in the visualization, we decided to try something completely different. We instead made use of both a scatterplot and a star plot, which are linked together; the user may click on a city in the scatterplot to add it to the star plot. The scatterplot excels at viewing general trends of sprawl in the US overall, while the star plot allows for direct multivariate data comparison between individual cities. Both visualization components allows the user to highly customize the data; for example, the data variables representing the axes in both the scatterplot and the star plot are all customizable.
Finally, I suggested the idea to use this urban sprawl dataset to my teammate; I got the idea because of my strong interest in topics about human geography and urban studies. Throughout this project, I increased my knowledge regarding urban sprawl, the trends, and the factors that cause sprawl.