Defining spatio-temporal granularities for raster data

The notion of granularity is used in several areas of computing. In the temporal database research field, granularity relates to the fact that the time frame associated to an event of interest (e.g., an accident) can be envisaged at several levels of detail (e.g., hour, day, month, etc.). Similarly, granularity in data warehousing is the level of detail at which facts (e.g., sales) are captured in dimensions (e.g., product, store, and day). However, there is no commonly-agreed definition of spatial or spatio-temporal granularities. Sometimes, the term spatial granularity is confounded for multiple resolutions. Further, the few proposals about them are mainly focused on vector data models. Raster model is an alternate representation to the vector one used, for example, in environmental information systems. In this paper, we extend the approach already proposed in a previous work and we define spatial and spatio-temporal granularities for raster data models. In our framework relations and operations between spatial and spatio-temporal granularities are defined.