Segmentation techniques for the summarization of individual mobility data

What is it about?

Segmentation techniques partition a sequence of data points in a series of disjoint sub-sequences - segments - based on some criteria. Depending on the context and the nature of data points, segments can be given an approximated representation. The result is a summarized representation of the sequence. This intuitive mechanism has been extensively studied, for example, for the summarization of time series in order to preserve the 'shape' of the sequence while omitting irrelevant details. This survey focuses on the use of segmentation methods for extracting behavioral information from individual mobility data, in particular from spatial trajectories. Such information can then be given a compact representation in the form of summarized trajectories, e.g., semantic trajectories, symbolic trajectories. Two major streams of research are discussed, spanning computational geometry and data mining respectively, that are emblematic of the multiplicity of views.

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Why is it important?

This survey focuses on the key paradigm of trajectory segmentation. In this article we overview major directions of recent research and highlight the methodological differences among these directions. While several surveys on collective movement analysis have been published to our knowledge, this is first survey explicitly focusing on the individual movement.

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