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首页> 外文期刊>GIScience & remote sensing >A minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology
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A minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology

机译：基于矢量化和并行计算技术的面向最小体积的包围盒策略，用于提高城市细胞自动机的性能

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As an effective tool for simulating spatiotemporal urban processes in the real world, urban cellular automata (CA) models involve multiple data layers and complicated calibration algorithms, which make their computational capability become a bottleneck. Numerous approaches and techniques have been applied to the development of high-performance urban CA models, among which the integration of vectorization and parallel computing has broad application prospects due to its powerful computational ability and scalability. Unfortunately, this hybrid algorithm becomes inefficient when the axis-aligned bounding box (AABB) of study areas contains many unavailable cells. This paper presents a minimum-volume oriented bounding box (OBB) strategy to solve the above problem. Specifically, geometric transformation (i.e. translation and rotation) is applied to find the OBB of the study area before implementing the hybrid algorithm, and a set of functions are established to describe the spatial coordinate relationship between the AABB and OBB layers. Experiments conducted in this study demonstrate that the OBB strategy can further reduce the computational time of urban CA models after vectorization and parallelism. For example, when the cell size is 15 m and the neighborhood size is 3 x 3, an approximately 10-fold speedup in computational time can result from vectorization in the MATLAB environment, followed by an 18-fold speedup after implementing parallel computing in a quad-core processor and, finally, a speedup of 25-fold by further using an OBB strategy. We thus argue that OBB strategy can make the integration of vectorization and parallel computing more efficient and may provide scalable solutions for significantly improving the applicability of urban CA models.
机译：作为模拟现实世界中时空城市过程的有效工具，城市蜂窝自动机（CA）模型涉及多个数据层和复杂的校准算法，这使其计算能力成为瓶颈。高性能城市C​​A模型的开发已经采用了多种方法和技术，其中矢量化和并行计算的集成由于其强大的计算能力和可扩展性而具有广阔的应用前景。不幸的是，当研究区域的轴对齐边界框（AABB）包含许多不可用的单元格时，此混合算法变得无效。本文提出了一种面向最小体积的边界框（OBB）策略来解决上述问题。具体而言，在实施混合算法之前，应用几何变换（即平移和旋转）来查找研究区域的OBB，并建立一组函数来描述AABB和OBB层之间的空间坐标关系。在这项研究中进行的实验表明，OBB策略可以进一步减少矢量化和并行化之后城市CA模型的计算时间。例如，当像元大小为15 m且邻域大小为3 x 3时，MATLAB环境中的矢量化可将计算时间提高约10倍，而在并行环境中实现并行计算后，则可提高18倍。四核处理器，最后通过进一步使用OBB策略将速度提高了25倍。因此，我们认为OBB策略可以使矢量化和并行计算的集成更加有效，并且可以提供可扩展的解决方案，以显着提高城市CA模型的适用性。

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