In this demo paper we present ST VISIONS, an easyto-use Python library for interactive visualizations of spatial and spatio-temporal datasets. By automating the low-level details of the underlying visualization library (Bokeh), ST VISIONS allows data scientists to create interactive, map-based visualizations, by writing Python code at a higher level of abstraction. Consequently, we accelerate the task of visualization from different sources, while we support interactive filtering, colorization, as well as multiple graphs, for various types of spatial and spatiotemporal data.

In this paper, we propose a novel unified online group pattern mining algorithm, EvolvingClusters, that aims to enrich geospatial data through the mapping of their group behaviour. Specifically, Evolving-Clusters is used to discover collective movement behaviour (like flocks and convoys) by monitoring the activity of multiple clusters through time and space. We evaluate the aforementioned algorithm using a realworld marine traffic dataset consisting of vessels' movement in Brest Bay, France. Our study demonstrates the efficiency and effectiveness of the proposed algorithm as well as its value towards a semantic enrichment tool that can be used to observe and categorize the behaviour of multiple moving objects in real time.

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