As parallel computing has become mature and practical, data intensive raster data processing algorithms are desiderating parallel computing technologies to reduce the running time. The objectives of this research focuses on the parallelization of neighborhood-scope algorithms. the sequential/parallel temporal model was developed, the affecting factors of each component of the temporal model were analyzed, and two optimization methods were proposed, which can further promote the parallel performance of neighborhood-scope algorithms: the Parallel I/O method that can reduce the data I/O cost; and the Halo Prediction method that can reduce the data communication cost. Experiments verified the effectiveness and efficiency of the proposed optimization methods, which can further promote the parallel performance by making the parallel algorithmic program fully take advantage of parallel computing resources.