Abstract: Spatial data partitioning method plays an important role in spatial data distributed storage, and its key problem is how topartition spatial data to distributed storage nodes in network environment. This paper discusses massive spatial data partitioning strategies and analyses their disadvantages which these partitioning methods have not taken into account spatial object size and spatial proximity. Aiming at these questions,this paper proposes a new spatial data parallelpartitioning strategy based on MapReduce and capacity-aware method to improve load balance which could avoid unevenly distributed data storage and data skew. Experimental analysis shows that the presented spatial data parallel partitioning algorithm not only achieves better storage load balance in distributed storage system,but also keeps well spatial locality of data objects after partitioning.

Key words: MapReduce, Hilbert space filling curve, spatial data parallel partitioning