Visually driven analysis of movement data by progressive clustering

TLDR

Trajectories are complex spatio‑temporal constructs whose similarity assessment requires distance functions, yet a single comprehensive function is difficult, time‑consuming, and hard to understand. The study proposes progressive clustering for visual exploration of large trajectory collections, applying simple, interpretable distance functions iteratively to reveal structure. Progressive clustering is implemented by repeatedly applying a clear, lightweight distance function, using the OPTICS algorithm and various trajectory distance measures to refine results stepwise. This approach yields sophisticated analyses through gradual refinement and enables efficient computation by reserving costly methods for small, promising subsets identified by inexpensive distance functions.

Abstract

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories.