A parallel method to accelerate spatial operations involving polygon intersections

Polygon intersection is an important spatial data-handling process, on which many spatial operations are based. However, this process is computationally intensive because it involves the detection and calculation of polygon intersections. We addressed this computation issue based on two perspectives. First, we improved a method called boundary algebra filling to efficiently rasterize the input polygons. Polygon intersections were subsequently detected in the cells of the raster. Owing to the use of a raster data structure, this method offers advantages of reduced task dependence and improved performance. Based on this method, we developed parallel strategies for different procedures in terms of workload decomposition and task scheduling. Thus, the workload across different parallel processes can be balanced. The results suggest that our method can effectively accelerate the process of polygon intersection. When addressing datasets with 1,409,020 groups of overlapping polygons, our method could reduce the total execution time from 987.82 to 53.66 s, thereby obtaining an optimal speedup ratio of 18.41 while consistently balancing the workloads. We also tested the effect of task scheduling on the parallel efficiency, showing that reducing the total runtime is effective, especially for a lower number of processes. Finally, the good scalability of the method is demonstrated.