Space–time density of trajectories: exploring spatio-temporal patterns in movement data

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

“…When that number increases, the STC suffers from occlusion and cluttering, making it difficult to identify trends [16]. Usually aggregation, for example, using kernel densities [17] or generating clusters of the most salient patterns [18] can resolve this. We extend this idea with a new approach for clustering trajectories based on the projection of trajectories on the sides of the STC, described in Section 3.…”

Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time

“…Space-time density of trajectories is a generalisation of the 2D kernel density into 3D kernel density around polylines in the space-time cube [13]. The principle is based on geometric -(a, b, c, d) and three-dimensional kernels (e, f, g, h) for trajectories.…”

“…The approach is inspired by the aggregated space-time density of trajectories within a space-time cube [13], but alters the density algorithm and combines it with home range/utilisation distribution principles in order to show the usage of space by an animal over time. The stacked densities volume in the spacetime cube facilitates the following tasks in particular: 1) to visually identify spatial patterns in animal movement (for example, whether areas exist that are frequently visited by the animal) and 2) to explore temporal characteristics of animal movement and thus identify temporal changes in the use of geographic space (for example, does the animal visit the same point at the same time every day and stays there for a while, i.e.…”

“…This means that they are indistinguishable if considered only in 2D space and if only 2D density distribution is used to visualise the home range. We also evaluate the stacked densities algorithm against the geometrically optimal space-time density [13] with regards to identifiability of crucial visual elements (using Qualitative Result Inspection, QRI [29]) and computational efficiency (using Algorithm Performance measures [29]). We further apply the visualisation method to a case study of trajectories of lesser black backed gulls (Larus fuscus) to identify two different types of bird behaviour by distinguishing between spatio-temporal and spatial-only hot spot, similar to those in our synthetic example.…”

Vídeo-Mapa: uma interface de geovisualização multimídia aplicada a transportes e meio ambiente.

“…Para as aplicações que não precisavam de muita precisão, como a de inspeção, o serviço tornou-se muito mais econômico e proporcionou um grande incremento no uso de sistemas de aerofotogrametria e de sensoriamento remoto na Web (WMS). Com isso acredita-se que cada vez mais as empresas de aerofotogrametria e sensoriamento remoto tradicionais passarão a vender serviços de processamento automático de imagens e ortofotos para empresas como a Google e Microsoft, Dodge et al (2008), como já acontece, por exemplo, com as imagens de São Paulo, disponibilizadas pelo Google, que provém da Base Aerofotogrametria S.A. As imagens na internet, que são fotos aéreas, sem muito tratamento: não são ortofotos e possuem um sistema de projeção próprio.…”