叠置计算中多边形形状复杂度的度量研究  

Research on measurement of polygon shape complexity in overlay calculation

作　　者：蒋元义 金宝轩 赵康[3] 周孙宇 JIANG Yuanyi;JIN Baoxuan;ZHAO Kang;ZHOU Sunyu(Kunming University of Science and Technology,Kunming 650032,China;Yunnan Provincial Department of Natural Resources,Kunming 650051,China;Wuhan University,Wuhan 430079,China)

机构地区：[1]昆明理工大学,昆明650032 [2]云南省自然资源厅,昆明650051 [3]武汉大学,武汉430079

出　　处：《测绘科学》2020年第11期177-184,共8页Science of Surveying and Mapping

基　　金：国家自然科学基金项目(41661086)。

摘　　要：针对地理计算中多边形形状复杂度难以量化的问题,而多边形形状复杂度是衡量对象空间结构复杂性的重要指标,对优化空间处理算法尤其是海量复杂数据的高性能叠置计算具有重要意义。该文提出了一种新的多边形形状复杂度度量模型。所提的多边形形状复杂度模型较好的度量了多边形形状复杂度,对于提升高性能环境下海量复杂数据叠置计算效率具有重要意义。该文以经典的Greiner-Hormann算法为例,在spark框架下验证了顾及多边形形状复杂度的数据划分方法相比现有的数据划分方法能取得更优的负载均衡指数和加速比。Aiming at the problem that the complexity of polygon shape is difficult to quantify in geographic computing,and the complexity of polygon shape is an important indicator to measure the complexity of the object’s spatial structure,it is of great significance for optimizing spatial processing algorithms,especially for high-performance overlay calculations of massive complex data.This paper proposes a new polygon shape complexity measurement model.The proposed polygon shape complexity model is a good measure of the polygon shape complexity,which is of great significance for improving the efficiency of superposition calculation of massive complex data in a high-performance environment.This paper takes the classic Greiner-Hormann algorithm as an example,under the spark framework,it is verified that the data division method that takes into account the complexity of the polygon shape can achieve a better load balancing index and speedup than the existing data division method.

关 键 词：形状复杂度 叠置计算 计算效率 spark框架 数据划分 

分 类 号：P208[天文地球—地图制图学与地理信息工程]