Identifying Characteristics of Collective Motion from GPS Running Data

Much of the research that has been carried out into movement patterns focuses on one level of granularity (Andrienko and Andrienko 2007, Dodge et al. 2008). However, our research into collectives has shown that this is not adequate when representing collective motion (i.e., the motion exhibited by a collective). We have developed a framework (the Three Level Analysis (TLA) framework) that analyses the motion of a collective on three levels of spatial granularity. The ultimate aim of the research reported in this paper is to develop a system that, using a combination of visual and automatic analysis, identifies whether or not a collective is present within a data set and if so, the type of collective it is. The TLA framework could be used as the basis of such a system but to do so, within the dataset, it must be possible to identify and extract the movement patterns occurring at each of the three levels as set out in the framework; and, to identify and extract the set of episodes occurring at each of these three levels. To test whether this is possible GPS data has been collected from the runners of various races around Exeter; it is this data set that is presented within this paper. A brief overview is given of the TLA framework (section 2) followed by why we believe this framework to be a possible bench-mark data set (section 3). Section 4 outlines how we are using the data set to address the problem of validating our framework. The paper concludes with a statement of how we believe an analysis of our current results could allow the identification of collective motion within a data set (section 5).