Trajectory data of people, cars, planes, animals, parcels, or oil spills typically comes in the form of time-stamped sequences of locations. From these data we can derive basic properties such as presence (was person A present at location B at time C?), meetings, (did person A and B meet on day C?), as well as descriptive statistics (how long did person A move, and how far?). We can also derive information such as speed, direction, acceleration, and tortuosity. Using all this data, we can try to segment (classify) trajectories into transportation modes (still, walk, bike, car, bus, train), or even activity modes (home, commute, sport, shopping...).
Higher order concepts like aggregation, generalisation, resampling, smoothing, map matching, and visual analytics will be discussed, and
(further) developed in small groups. The project will mostly use R, R package development will be discussed in a side event.
The project starts with a series of short introductions from students on selected topics in trajectory analytics, followed by group discussion. In the second part, one or more aspects will be further developed and implemented in small groups as programming project.