Agricultural regionalization based on spatial clustering of mixed data

Agricultural regionalization, which is largly achieved according to the similarity within a certain area and the difference between this area and other ones, is the foundation of agricultural production. Due to the fact that clustering is much like regionalization, methods for clustering are also commonly used in regionalization. However, the clustering algorithms applied are usually for only numerical attributes and large amounts of categorical data with great values cannot be handled with traditional clustering methods, which largely limits the utilization of clustering in agricultural regionalization. In this paper, we propose a new spatial clustering algorithm which combines the ROCK algorithm with fuzzy mathematics, can handle both numerical and categorical data at the same time, to satisfy the actual needs of agriculture regionalization. The effectiveness of the new algorithm is tested on the agricultural regionalization of ZengCheng, GuangZhou, China. During the test, we test both the effectiveness of the new spatial clustering algorithm and some old algorithms. The final result shows that the new algorithm performs better in agricultural regionalization, and its result is also closer to the artificial agricultural regionalization.