This paper covers a number of different aspects of integrating movement with GIS and spatial visualizations. Two in particular interested me. One is the challenge of big but thin data. The authors point out that while there are more Big Data than ever before on individuals’ locations to use as movement data, they are thinly attributed. This reminds me of a paper I had to read in GEOG 307, “Mobile Phone Data Highlights the Role of Mass Gatherings in the Spreading of Cholera Outbreaks.” The authors used mobile phone information to track the movements of individuals in Senegal in the wake of a cholera outbreak, to see where people leaving the affected area were going to and gathering. This is a perfect example of having access to tons of movement data that are thinly attributed: there were no names or demographic information attached to the call locations (for confidentiality reasons if nothing else), and the infection status of these individuals was also unknown. Even so, however, the authors were able to draw powerful conclusions about where people were moving and therefore where the epidemic could potentially spread. This begs the question: is thinly attributed data an issue, when so many of them are available? I would say it depends on the question to be answered. In the cholera study, while it would have been nice to have more data for the phone calls, this was not necessary to conduct effective and meaningful analyses. However, this may not be the case in all such movement studies. There will likely be studies where discriminating between data, for example, would be necessary, and in cases like these more attribute data than the ones currently available would be required, even with the massive volume of data accessible.