A universal parallel scheduling approach to polyline and polygon vector data buffer analysis on conventional GIS platforms. Issue 6 (4th August 2020) - Guo, Mingqiang
Abstract: With the increasing volume of spatial data, conventional vector data buffer analysis algorithms cannot meet the demands of fast data processing, so parallel computing is introduced to accelerate vector data analysis. However, it is difficult for these GIS platforms to adopt parallel approaches to conduct buffer analysis due to their conventional data format and vector buffer analysis algorithms. To address the problem, a universal parallel scheduling approach to buffer analysis on conventional GIS platforms is proposed in this study. First, the key impacting factor of buffer analysis computing time is identified. The relationship between the impacting factor and the buffer analysis computing time is analyzed to generate computational intensity transformation functions. Then, computational intensity grids (CIGs) of polyline and polygon are constructed by partially computing the computational intensity of a lattice. Using the corresponding CIGs, a two‐stage adaptive spatial decomposition method for parallel buffer analysis is developed. Firstly, the whole domain of the spatial vector dataset is divided into sub‐domains, with the computational intensity of each sub‐domain being equal as far as possible; secondly, the features are evenly assigned within the sub‐domains into parallel buffer analysis tasks for load balance. The experiments demonstrate that the approach presented in this article can effectively evaluate and spatially represent the computational intensity of buffer analysis for polylines and polygons. Compared with typical spatial adaptive decomposition methods and regular domain decomposition methods, the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near‐linear speedups. The new approach achieves excellent performance on three conventional GIS platforms, indicating that our approach is an effective parallel scheduling approach to vector data buffer analysis for conventional GIS platforms.