In traditional CA

general transfer rule and same speed are used to drive models. In this kind of CA

spatial-temp differences of geographical phenomenon

both transfer rules and transfer speed

are ignored. To solve this problem

a partitioned & asynchronous CA based on spatial data mining is given in this paper. In this model

cell space is departed by dual-constraint spatial cluster and general transfer rule is replaced by partition transfer rule

asynchronous transfer speed is calculated using general grid and synchronous transfer speed is replaced by asynchronous transfer speed. Spatial differences of geographical transfer rule and geographical transfer speed are taken into account in this kind of models. Taking land use change in Hangzhou as a case

partitioned & asynchronous CA based on spatial data mining are implemented in land use. Result shows：comparing with traditional CA

partitioned & asynchronous CA based on spatial data mining can get more accurate simulation result

and this kind of models can be used to simulate geographical phenomenon in a larger area for a comparatively longer time. A new viewpoint of GeoCA is given in partitioned & asynchronous CA. In this kind of Model

spatial differences and temporal diversities are taken account into GeoCA

it makes simulations with this kind of model much closer to actuality. Yet theories and methods of partitioned & asynchronous CA is still in tentative research stage

there are many problems

such as divided methods of cell space

methods of calculating weight of dual-constraint spatial cluster

calculation of cell transfer speed

getting of transfer rules

evaluation of CA result accuracy

and applications of partitioned & asynchronous CA in simulating land use at larger area and during longer time and etc. should be discussed and analyzed.