To overcome the disadvantage of non-partitioned geo-cellular automata（GeoCA） modeling

a partitioned GeoCA based on dual-constraint spatial cluster is discussed in this paper.Comparing with the traditional spatial cluster method

dualconstraint spatial cluster concerns not only spatial distance but also the likelihood of attributes between objects.Under the partitioned GeoCA based on dual-constraint spatial cluster modeling framework

cellular space is departed into several partitions by dual-constraint spatial cluster.And then

a set of transfer rules corresponding to each partition are calculated accordingly.Later on

Hangzhou City is selected as the study area and the Partitioned GeoCA modeling based on dual-constraint spatial cluster is employed to simulate the land use change during the period between 2000 and 2005.The study area of Hangzhou is divided into 5 partitions by using K-Means cluster algorithm according to both spatial distance and attribute likelihood

and C5.0 decision tree is employed to obtain the cellular transferring rules for each partition for the study area.In each partition

corresponding cellular transferring rules are employed to drive GeoCA to run simulation.Finally

confusion matrix and Moran I index are calculated to evaluate the accuracy of simulated results by using the GeoCA model.Results show that the accuracy by using the partitioned GeoCA modeling based on dual-constraint spatial cluster owns higher simulation accuracy compared with those simulated based on non-partitioned GeoCA simulation and the partitioned GeoCA based on the traditional spatial cluster.Besides

when Moran’s I index is employed to evaluate the accuracy of simulation result;the result of partitioned GeoCA modeling based on dual-constraint spatial cluster is closer to the real land use pattern.The empirical study shows that a more accurate simulation result can be achieved by using the partitioned GeoCA modeling based on dual-constraint spatial cluster.