Visual Analytics Focusing on Space

• Published in: Visual Analytics of Movement pp. 253 - 305
• Main Authors: Andrienko, Natalia, Keim, Daniel, Andrienko, Gennady, Bak, Peter, Wrobel, Stefan
• Format: Reference Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2013; Springer Berlin Heidelberg
• Subjects: Data mining; Dependency Series; Geographical information systems (GIS) & remote sensing; Geospatial data; Original Time Series; Sequence Mining; Time Graph; Time Series
• ISBN: 9783642375828; 3642375820
• DOI: 10.1007/978-3-642-37583-5_7

This chapter considers analytical tasks focusing on space treated as a discrete set of places of interest (Fig. 7.1). We present several methods for defining a set of places of interest based on the available movement data and depending on the analysis goals. For visual exploration of time series (TS) associated with places or with links between places, we suggest a time graph display enhanced with tools for data summarization and various computational transformations. Other analysis methods include clustering of TS by similarity, TS modelling, and computational extraction of peaks or other features followed by representing them as spatial events. These methods are supported by interactive visual techniques. Binary relations between places are analysed by combining flow maps with time graphs. We also consider dependencies between attributes of flows emerging when the movement is constrained by channels with limited capacities, such as in a street network. The dependencies can be represented by regression models, which can be built, evaluated, and refined with support of interactive visual tools. We consider also the ways to reveal and explore ordering and temporal relations involving more than two places. When places of interest are few, these relations can be revealed and investigated by means of interactive visual displays. When places are more numerous, frequently occurring sequences of visited places can be discovered by means of sequence mining algorithms after transforming trajectories of movers into sequences of strings representing visited places. The algorithms return frequently occurring subsequences, which can be interpreted and explored in the spatial context after being transformed to trajectories. Sequence mining may particularly useful in analysis of episodic movement data.