Using flocks to drive a Geographical Analysis Engine

a side eeect, this search-intensity surface gives a clear indication of not just where has been searched but, more importantly, where has not yet been searched, providing for the rst time a measure of how thorough the ock search is. Conclusions The hybrid approach ooers many potential advantages over using any one of the methods independently. In a worst case scenario, the system should still perform at least as well as the best contributing component. This alone may be useful as, given the diierent capabilities of each engine, it would be diicult to chose which engine to employ. It is, however, with the introduction of time-attribute clusters that the new system has greatest potential. In this scenario, any individual cluster may be purely spatial , purely temporal, or both, as well as being focused within certain attribute constraints. It is tests such as these which push the computational intensity of the problem upwards and it is here that the hybrid approach is most likely to have greatest eeect as the wide exploratory powers of the ock and GA will, it is hoped, nd some indication of possible clustering which can then be exhaustively searched by a focused GAM run. Extensive testing is required, however, to give a full indication of the level of gain ooered by combining the diierent approaches in a cooperative, expandable, distributed architecture. Initial results from such trials, examining each engine separately have suggested that the ock method approaches the results gained from GAM and suggest that a combined approach may well be viable (Openshaw, et al, 1999).A mark I geographical analysis machine for the automated analysis of point datasets', Int.