I. Introduction
Large-scale data analysis is the core of main enterprises and scientific research. In recent years, with the development of LBS, the amount of geospatial data is growing rapidly. The increasing volume of spatial data, however, poses many new problems for traditional indexing mechanisms which usually assume an in-memory index or optimize for local disk access. Processing the spatial analysis and query leads to frequent disk access using traditional methods. So, it be-comes essential to provide efficient index method for spatiotemporal databases. The parallel and distributed processing seems a good solution to this problem. MapReduce [1] is a widely used parallel programming model and computation platform. With MapReduce, it is very easy to develop scalable parallel programs to process data-intensive applications on clusters of commodity machines.