Optimal partitioning of spatial dataset is an import concern for many data management context such as spatial indexing

parallelizing GIS and distributed data access.This paper aims to discuss an adaptive spatial data partitioning method based on Hilbert space-filling curve and spatial pattern detection.A spatial data partitioning approach is proposed for spatial data partitioning of regular

random

and clustering objects

respectively.We show that the proposed method can achieve optimal partitioning point dataset without point dataset boundary information.We also show that the proposed method can adapt to available information about data distributions

data sizes and clustering.