This paper introduces a framework for modeling the movement of objects or individuals over multiple granularities. Granularity refers to selecting the appropriate level of detail for a task.

It is often desirable to consider an object’s movement over space and time from the perspective of different levels of de-tail or granularity. Changing to a more detailed view uncov-ers information that otherwise is unknown. Conversely, mov-ing to a simpler view can improve our understanding of the movement patterns of an object. This paper considers shifts in granularity over geospatial lifelines. A geospatial lifeline is a time-stamped record of all the locations that an object has occupied over a period of time. An approach to modeling lifelines at different granularities is presented, identifying dif-ferent aspects of lifelines that are relevant at refined or coarse granularities.

In modern GIS applications the control of complex systems dealing with mobile objects gains more and more importance, as for example in the area of airspace monitoring, tra#c monitoring on street networks, or location-based services. The logging of individual physical events, such as changes of the current position or vehicles passing a monitoring camera, results in a large amount of data, thus requiring the support of a data base management system. In this context the capability to validate observed events and to provide an 'intelligent' reaction to the occurrence of specific relevant situations becomes essential. For these tasks active and deductive rule techniques seem to be particularly well suited. This paper aims at examining the interaction between active and deductive rules in the context of monitoring moving objects using a DBMS.