首页> 外文会议>10th International Symposium on Spatial Data Handling, Jul 9-12, 2002, Ottawa, Canada >A QTM-based Algorithm for Generation of the Voronoi Diagram on a Sphere
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A QTM-based Algorithm for Generation of the Voronoi Diagram on a Sphere

机译:基于QTM的球面上Voronoi图生成算法

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To efficiently store and analyse spatial data at a global scale, the digital expression of the Earth's data must be global, continuous and conjugate, i.e., a spherical dynamic data model is needed. The Voronoi data structure is the only published attempt and only solution (which is currently available) for dynamic GIS. The complexity of the Voronoi algorithms for line and area data sets in a vector-based context limits its application in dynamic GISs. As yet, there is no raster-based Voronoi algorithm for objects (including points, arcs and regions). To overcome this deficiency, an algorithm for generating a spherical Voronoi diagram, that is a Voronoi diagram on a spherical surface, is presented based on 0-QTM (Octahedral Quaternary Triangular Mesh). The basic idea is to apply the dilation operation developed in mathematical morphology to objects on the sphere in an effort to produce the effect of distance transformation. The distance contours of objects will form the Voronoi boundaries of the spherical objects. The algorithm presented in this paper can handle point, line and area objects. Additionally, it has been tested and concluded that the processing time required for this algorithm with point, arc and region data is proportional to the levels of complexity of the spherical surface tessellation. The difference (error) between the great circle distance and the QTM cells distance is related to the spherical distance.
机译:为了在全球范围内有效地存储和分析空间数据,地球数据的数字表达必须是全局的,连续的和共轭的,即需要一个球形动态数据模型。 Voronoi数据结构是动态GIS唯一公开的尝试,也是唯一的解决方案(当前可用)。基于矢量的上下文中线和面数据集的Voronoi算法的复杂性限制了其在动态GIS中的应用。到目前为止,还没有针对对象(包括点,弧和区域)的基于栅格的Voronoi算法。为了克服这一缺陷,提出了一种基于0-QTM(八面体第四纪三角网格)生成球面Voronoi图的算法,即球面Voronoi图。基本思想是将以数学形态学发展的扩张运算应用于球体上的物体,以产生距离变换的效果。物体的距离轮廓将形成球形物体的Voronoi边界。本文提出的算法可以处理点,线和面的对象。此外,已经测试并得出结论,使用点,弧和区域数据进行此算法所需的处理时间与球面曲面细分的复杂程度成正比。大圆距离和QTM像元距离之间的差异(误差)与球面距离有关。

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