This paper describes CELLAR, a language for cellular programming which extends the cellular automata model through the concept of regions. Regions are spatiotemporal objects that define zones of the automaton (set of cells), containing interesting and meaningful data patterns or trends that can be defined as events. Each cell of the automaton can monitor regions for a given period and observe their evolution by global functions (max, min, sum etc.). Furthermore, each cell can have an associated attribute called its perception rating, that indicates how far that cell can 'see'. On the basis of this value and the cell's position in the cellular space, we can define the regions that are visible to the cell. Using these constructs, a cell can define significant events to extract data of interest in one or more regions and perform actions when an event is detected. In the paper, we show that regions simplify programming and allow the building of more complex models. After describing the main constructs of CELLAR, the paper illustrates the region-based programming model by describing the design of a parallel model of animal migration. Performance results of the model implemented on a Meiko CS-2 are also given.