A granularity theory for modelling spatio-temporal phenomena at multiple levels of detail

Reasoning about spatio-temporal phenomena requires the adoption of common granularities that facilitate and enhance the comprehension of a particular phenomenon. In our day-to-day activities, spatial granules like state, province or country, and temporal granules like day, month or year, are used to index facts and to allow reasoning adopting the level of detail considered appropriate in a particular analytical context. In an era where huge amounts of spatio-temporal data are collected every day, it is crucial to model the spatio-temporal phenomena expressed in such data sets having in mind that different levels of detail can be useful in the analysis of such phenomena and that different levels of detail are related, for instance, through a spatial or temporal hierarchy. As the size and level of details of the data sets increase, the need to use multiple levels of detail that enhance our capability to achieve useful insights from data also increases. This paper presents a granularity theory devised to model spatio-temporal phenomena at different levels of detail. This granularity theory is more general than the existing granularities proposals. In fact, we relate those proposals with the presented granularity theory.