There are two methods for GIS similarity measurement problems, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. Both of these two methods can not measure subzone similarity of GIS subzone based on universal background. The rough measurement based on membership function solved this problem well. In this paper we used rough sets to calculate the GIS subzone discrete data similarity measurement, and used neighborhood rough sets to calculate continuous data's upper and lower approximation. We used neighborhood particle to calculate continuous attribute's rough membership function, then to calculate continuous attribute's subzone similarity measurement problem. This paper used rough membership to measure similarity problem for different subzones. Because Moran's I can only measure universe or each unit's spatial autocorrelation, it can not measure subzone, so our method in this paper can compute GIS subzone similarity based on universe. And for continuous value, we used distance function and neighborhood rough sets to divide continuous value's upper and lower approximation and classification problem, then we put forward a rough membership function based on neighborhood information granulation. Then, we used rough similarity measurement formula to measure GIS subzone similarity problem. This method can provide a new direction for GIS point group or others' object group similarity measurement. At last, using an example that includes discrete and continuous value, we can find that spatial autocorrelation can not measure discrete value, cross-coefficient can not measure discrete value too. If the subzone in map is not equal length for continuous value, cross-coefficient can not measure similarity, but the rough measurement based on membership function solved this problem well.