The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis

When studying geographical phenomena, different levels of spatial and temporal granularity often have to be considered. While various approaches have been proposed to analyse geographical data in a multi-scale perspective, they have all focused on either spatial or temporal attributes rather than on the integration of space and time over multiple scales. This study introduces the continuous spatio-temporal model (CSTM), a conceptual model that seeks to address this shortcoming. The presented model is based on (1) the continuous temporal model (CTM), a multi-scale model for temporal information, and (2) the continuous spatial model (CSM), an extension of CTM for multi-scale spatial raster data. At the core of the presented conceptual model is a spatio-temporal evolution element or, in short, stevel, which is described by four variables: (1) pixel location, (2) spatial resolution, (3) temporal interval, and (4) temporal resolution. By varying one or more of these variables, a CSTM-tree consisting of (sets of) stevel arrays is created, forming the basis of an exhaustive CSTM-typology. These arrays can then be used to systematically cluster spatio-temporal information. The value of our approach is illustrated by means of a simplified example of mean temperature evolution. Various suggestions are made for modifications to be developed in future research.