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AA-Maps: attenuation and accumulation maps for spatio-temporal event visualisation

Publication . Albino, Catarina; Moura Pires, João; Datia, Nuno; Silva, Ricardo Almeida; Santos, Maribel Yasmina

Some phenomena, such as crimes in a city, fires occurred in a country and road accidents can be interpreted as sets of spatio-temporal events. A spatio-temporal event is described by a geographic location, a time instant and other characterising attributes. The cartographic visualisation of spatio-temporal events remains unresolved, due to the challenges related with portraying multiple dimensions simultaneously: the spatial, the temporal and the semantic (zero or more dimensions) phenomenon's components. In this context, this article presents the Attenuation and Accumulation Maps (AA-Maps). The main idea of this visualisation analytic approach consists in showing in a map, the resulting effect of combining attenuation and accumulation, from a temporal reference of observation, given a spatio-temporal Level of Detail (LoD). Imagine the footprints of people crossing a garden in various directions. They leave different traces that summarize the cumulative effect of the footprints on the grass, which is attenuated as time goes by. AA-Maps support different combinations of attenuation and accumulation functions. In addition, this method also enables analysis with different Levels of Detail (LoD), both spatial and temporal. This allows distinct analytic perspectives of the phenomenon while promoting the search for the most suitable parametrization for its characteristics.

2017-11-16Conference object

Restricted access

Visualising hidden spatiotemporal patterns at multiple levels of detail

Publication . Silva, Ricardo Almeida; Moura Pires, João; Datia, Nuno; Santos, Maribel Yasmina; Martins, Bruno; Birra, Fernando

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed. Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns. This paper presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.

2018-12-06Conference object

Open access