Call for participation

Aims and scope

Recent developments in movement pattern analysis reflect the broad interest in this field. Just as broad seems to be the methodological spectrum scientists develop to investigate movement patterns. Although the plethora of application fields, in fact, calls for a wide spectrum of methodologies, it is difficult to find a common strategy in the community that would help in sharing results, exchanging methods as well as heading towards what would be an established theory on movement pattern analysis. It is the goal of this workshop to contribute to such a common view on methods of movement pattern analysis. For this purpose, concrete datasets will be moved into the centre of this workshop. The idea is to arrive at an answer to the question of what makes a useful benchmark dataset for movement pattern analysis. Such benchmark datasets could significantly help in the long-term goal to work on a common theory of movement pattern analysis, since benchmark datasets provide means to compare different methods. Generally, movement pattern analysis endeavors to explicitly capture the space-time structure in data in order to meaningfully analyze moving objects.

Repositories of reference movement datasets are rare, partly due to privacy, security or copyright restrictions. Also, for datasets that are available in the public domain metadata is often scarce, as semantically annotating movement data is expensive and hence mostly copyrighted. It is assumed, however, that the spatial information science community, with a lot of data acquisition techniques available today, is in the position to have among its members significant amounts of movement data that could be potentially developed into reference datasets. It is, however, not entirely clear what defines a useful benchmark dataset, for evaluating and comparing methodologies.

Who should come to MPA'10?

This workshop aims specifically to bring together researchers with interests in spatial information theory and method development with application researchers, institutions, and private companies that collect, manage, and analyze movement data in some given application context.

This workshop covers any form of movement of trackable entities in unconstrained and network spaces, including but not limited to:

  • Traffic and transportation, e.g. car tracking data, fleet management data
  • People, e.g. pedestrians, shoppers, crowds
  • Mobile phone applications
  • Animal movement

Workshop outline

We are particularly interested in two kinds of contributions: short discussion papers and overview presentations. The short discussion papers (maximally 1500 words, excluding references) have to present a specific problem in the context of movement analysis. In particular each paper should present the dataset of a specific use case in an application context or in the context of a specific research question. Any information underlining the data sets potential as a benchmark data set is considered a plus (context of observed process, metadata, data structure, observable patterns and processes, research questions or tasks addressed). The inclusion of maps and figures is encouraged. Those datasets will play an essential role during the entire workshop. Young researchers are particularly encouraged to present their research problems. Overview presentations are asked to have the character of short tutorials. With these presentations an overview of the state-of-art in movement pattern analysis is expected. Submissions for overview presentations should be well referenced and include a tentative structure of the presentation (also 1500 words, excluding references).

The workshop program will be complemented by working in groups. Each group will have to analyze one of the presented reference problems in the context of the presented methodologies. Moreover, each group will have to work on how their reference problem generalizes to other application problems.

The outcomes will be discussed in the plenum. Thereby, an overall goal consists in the identification of what distinguishes benchmark datasets and their corresponding analytical problems and what needs to be done by the community in order to arrive at a repository of useful benchmark datasets and problems.