Visual Analytics Focusing on Time

Abstract

This chapter presents methods and procedures that can support movement analysis tasks focusing on time units (Fig. 8.1). Spatial situations characterize time units in terms of the spatial positions and movement characteristics of the existing moving objects. Spatial situations can be represented in an aggregated form by spatial presence and flow distributions. When the number of spatial situations is large, clustering by similarity is a suitable way to reduce the analytical workload. For each cluster, a representative spatial situation is constructed or selected. A complementary method for analysing characteristics of spatial situations is extraction of local features, such as local maxima or minima, and representing them by spatial events, which may be then analysed by means of methods suitable for spatial events. Quantitative changes between spatial situations can be analysed with the help of change maps. Changes of object positions (displacements) can be visualized on flow maps or by origin–destination matrices. A Dynamic Categorical Data View (DCDV) display enables exploring object positions changes over multiple selected time units when the number of different places is small or the places can be grouped into a small number of place categories.