魏智威, 刘远刚, 许文嘉, 王洋. 利用Snake移位模型构建中心型地图[J]. 武汉大学学报 ( 信息科学版), 2022, 47(12): 2105-2112. DOI: 10.13203/j.whugis20220553
引用本文: 魏智威, 刘远刚, 许文嘉, 王洋. 利用Snake移位模型构建中心型地图[J]. 武汉大学学报 ( 信息科学版), 2022, 47(12): 2105-2112. DOI: 10.13203/j.whugis20220553
WEI Zhiwei, LIU Yuangang, XU Wenjia, WANG Yang. Central Time-Space Map Construction Using the Snake Model[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2105-2112. DOI: 10.13203/j.whugis20220553
Citation: WEI Zhiwei, LIU Yuangang, XU Wenjia, WANG Yang. Central Time-Space Map Construction Using the Snake Model[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2105-2112. DOI: 10.13203/j.whugis20220553

利用Snake移位模型构建中心型地图

Central Time-Space Map Construction Using the Snake Model

  • 摘要: 中心型地图可表示空间各点到中心点的时间距离,能直观反映地理分布因交通系统影响而产生的相近性变化。其构建核心是依据控制点的位置移动确定非控制点的新位置,因此,将中心型地图的构建过程建模为地图目标的移位问题,以相对邻近图表示点之间的邻近关系,并基于控制点的位置移动计算点的初始受力,应用Snake移位方法迭代计算各点的新位置生成中心型地图,同时利用后处理解决潜在拓扑错误。利用所提方法和最小二乘方法可视化武汉市到其他各市的旅行时间,并定量、定性地进行对比分析。结果表明,所提方法能更好地避免拓扑错误,且局部形态保持更好。

     

    Abstract:
      Objectives  The central time-space maps deform the map space to visualize the one-to-many time distance, which can intuitively reflect the distance changes due to the influence of a traffic system. The core of the central time-space map construction is to calculate the non-control points' locations based on the control points' displacements. Displacement is a basic operation in cartographic generalization and many approaches have been developed.
      Methods  We model the central time-space map construction as a problem of displacement in cartographic generalization, use the relative neighborhood graph to express the proximity relations between the points, and the initial forces on the points are computed based on the control points' displacements.And the Snake model is applied iteratively based on the built graph to obtain the new locations of all points, post operations are also applied to avoid topology errors in the iterative process.
      Results and Conclusions  Compared to the existing approaches, the proposed method can reduce the topology errors and improve the shape similarity for the deformed boundaries, and can better avoid topology errors and maintain the local morphologies.

     

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