Discovering relative motion patterns in groups of moving point objects

Abstract

Technological advances in position-aware devices are leading to a wealth of data documenting motion. The integration of spatio-temporal data-mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data-mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non-trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic 'offside trap' behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.

Reporting Leaders and Followers among Trajectories of Moving Point Objects

“…Precursory to this research Laube and colleagues proposed the REMO framework (RElative MOtion) which defines similar behaviour in groups of entities [36][37][38]. They defined a collection of movement patterns based on similar movement properties such as speed, acceleration or movement direction.…”

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

“…As described above, the path of a moving object in the STC is approximated as a series of straight-line segments between sampled points (x i , y i , t i ) and (x i+1 , y i+1 , t i+1 ) [8]. Each trajectory is therefore represented by a continuous 3D polyline.…”

“…The path of a moving object is usually represented as a trajectory which is a sequence of positions in a two-dimensional (2D) geographic environment with time stamps [8], i.e. T = (x 1 , y 1 , t 1 ), ..., (x n , y n , t n ) for some n, such that (x i , y i ) is the measured geographic location of the moving object at time t i .…”

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

“…They are the spatiotemporal "trace" left behind by the behavior of moving entities [2]. Examples of movement patterns include flocking as in a "mob" of sheep [3], leading and following found in group dynamics [4,5], or converging and diverging of pedestrians in crowding scenarios [6,7]. Figure 1 illustrates the movement pattern of a prototypical "flock".…”