直线排列建筑物群渐进式典型化方法

地图综合中,建筑物群的排列结构是需要重点考虑的因素。当不同排列的子建筑物群之间存在空间图形冲突时,这些建筑物群的综合就显得更为复杂。直线排列建筑物群的综合在大比例尺地形图上以典型化操作为主。本文提出一种相互之间存在潜在空间图形冲突的多个直线排列建筑物子群的渐进式典型化方法,渐进式地处理多个直线排列建筑物子群之间的空间图形冲突,保留建筑物群重要的直线排列结构;以建筑物表达的视觉图形约束为限制条件,自动确定典型化后的建筑物位置、形状、大小和方位。本文还研究了基于建筑物群空间邻近图的直线排列建筑物子群的自动识别方法,分析了这些直线排列之间的邻近关系和相交关系。最后,以1：5000地图上的建筑物群综合为1：25 000为试验对象,验证了所提出算法的可用性和有效性。     

An Immune Genetic Algorithm to Buildings Displacement in Cartographic Generalization

Spatial conflicts may occur when map data are displayed at a scale smaller than that of the source map. This study applies the displacement operator in cartographic generalization to resolve such spatial conflicts and to improve the clarity and legibility of map. The immune genetic algorithm (IGA) is used in this study for buildings displacement to solve conflicts. IGA is based on the genetic algorithm (GA) and employs the self‐adjusting mechanism of antibody concentration to enhance population diversity. Meanwhile, the elitism retention strategy is adopted in IGA to guarantee that the best individual (antibody) is not lost and destroyed in the next generation to strengthen convergence efficiency. The compared experiment between IGA and GA shows that the displacement result produced by IGA performs better than GA. Finally, in order to make the displaced map more attractive to cartographers, two constraints – the building alignment constraint and building tangent relation constraint – are applied in IGA to restrict the buildings’ displacement. The same experimental data are adopted to prove that the improved IGA is useful for maintaining the two constraints.     

线图形简化与移位算子的协同方法

在地图综合过程中,线的图形简化和移位算子通常是分别执行的,图形简化和移位有时都会产生新的空间冲突。本文试图把这两种算子进行协同,避免在地图综合过程中进行空间冲突的多次探测,提高地图综合数据处理的效率。本文通过把线图形简化转换为线上的点移位,并构建邻近地图目标之间的移位传播路径,在考虑空间上下文关系和地图感受规则的前提下,使移位过程能考虑到线图形简化,并尽量保持有关地图目标的空间特征。最后,以道路和其周边的建筑物群为例,验证了该算法的有效性和可行性。     

Building Displacement Based on the Topological Structure

Map data at smaller scales than their source can result in spatial conflict, whereby map symbols become too close, or overlaid. Server map generalisation operators may be applied to solve this problem, including displacement. In this paper, we show how an optimisation algorithm, the snake algorithm, was used to displace multiple objects in order to resolve spatial conflicts and maintain important spatial relationships between objects during displacement. Two principles based on the snake algorithm are proposed in this paper. First, the truss structure mirroring spatial proximity relationships between buildings and between building and road is formed based on the weighted proximity graph derived from constrained Delaunay triangulations (CDT) in each map partition. In the weighted proximity graph, each connecting line is determined as a snake and as an element unit to assemble the global stiffness matrix in snake algorithm. Second, a buffer method that calculates force between a building and a road (or other linear features) or between pair of buildings is adopted in the snake algorithm. This avoids the imbalance phenomenon caused by different force calculation methods during the displacement. The feasibility of the approach is demonstrated in obtaining real geographic data. Finally, the results are cartographically usable and in particular, the spatial relationships between objects are preserved.     

用计算机模拟人类制图员解决地图缩编中的图形冲突

针对地图综合中因道路符号的夸大表示而引起的道路与建筑物的图形冲突,提出了让计算机模拟人类制图员具备视觉及分析功能的具体方法,设计了一种新颖的栅格-矢量混合数据结构,以便通过将移位区内的所有建筑物分类,进行制图移位和受控变形等解决图形冲突。     

利用Snake移位模型构建中心型地图

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。     

On the spatial distribution of buildings for map generalization

Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization.     

基于场论分析的建筑物群的移位

在多边形群目标综合中,移位是一种旨在解决空间邻近冲突的重要操作.以建筑物群在街道拓宽后产生移位操作为例,提出一种基于场论分析的移位方法,认为街区块多边形边界的收缩产生向街区块内部逐步传递并衰减的作用力,从而促使建筑物多边形的空间位置移动,借助于物理学场论中的"等势线"模型来表达这一移位现象.基于Delaunay三角网建立了类似于Voronoi图的建筑物群剖分结构,用于表达移位场模型的"等距离关系曲线".在移位场中目标的运动方向与运动距离由矢量和运算及梯度衰减函数计算完成.算法思想在一地图综合软件系统中已实现.     

地图综合中目标空间关系维护

在地图综合的过程中,比例尺的变化以及各种综合算子都可能导致目标产生空间关系冲突。以往在对空间关系进行维护时,通常只是对拓扑关系变化进行探测与控制。由于方向关系是人们在日常生活中用途最为广泛的空间关系,研究在地图综合中如何有效维护目标间的方向关系具有重要的科学意义。本文采用定性空间推理的方法,建立考虑目标方向关系的移位安全区,从而可以综合检测与控制拓扑关系与方向关系的冲突及其传播,并就本方法存在的问题和今后的研究方向提出了一些看法和思路。     

顾及格式塔原则的建筑物群移位实数编码遗传算法

在建筑物群的综合过程中,建筑物群的移位是一个非常重要的任务。在分析已有算法的基础上,探讨了建筑物群移位的一个新的自适应实数编码遗传算法,在该算法中,考虑了建筑物与街道之间的空间关系和建筑物群排列在图形表达上的格式塔原则,在适应度函数的设计上也考虑了多重因素的影响,使得移位结果更符合地图制图规范和地图感受规律。实验结果表明,该算法在移位空间充足时能精确收敛到最优解,移位效果良好。     

一种基于降维技术的街区综合方法

大比例尺城市建筑物综合一直是自动综合研究的重点。利用大比例尺城市建筑物和街道之间存在几何空间互补的特点,提出依据建筑物骨架线和街道骨架线对街区进行综合的新方法。定义骨架线提取方法,分析建筑物骨架线和街道骨架线之间的特性,在此基础上,提出基于降维技术的建筑物合并、化简、位移等综合方法。进一步,充分概括了本方法在自动综合技术实现上的优越性,并通过实验证明其有效性。     

A typification method for linear pattern in urban building generalisation

This paper presents a typification method for linear pattern in urban building generalization. The proposed method includes two processes. Firstly, structural knowledge in terms of linear pattern is detected using a two-step algorithm taking the advantages of Gestalt visual perception, computational geometry and graph theory. Spatial neighbourhood is captured using interpolated constrained Delaunay triangulation and the resulting proximity graph is pruned to be heterogeneous to get acceptable linear patterns with regard to Gestalt visual perception. Then, a typification strategy is proposed, in which typification is regarded as a progressive and iterative process consisting of elimination, exaggeration and displacement. The typification strategy iteratively executes eliminating the building with minimum overall effect, exaggerating remaining buildings considering key location and spatial characteristics and displacing them to preserve the linear pattern until elimination quantity is satisfied. Experiments show that this proposed strategy is effective and linear patterns are guaranteed with correctness and completeness.     

道路与建筑物空间冲突的几何相似性移位算法

保持建筑物群的空间分布模式一致是道路与建筑物空间冲突处理问题的难点。按道路对建筑物的包围程度,将道路与建筑物之间的关系类型划分为开放型、半开放型和闭合型。本文提出了基于几何相似性的建筑物移位算法,并以距离、角度和面积作为满足几何相似性的约束条件。以道路网眼为单位,采用最小生成树的方法生成建筑物群的骨架,以骨架的长度和角度特征分别作为建筑物移位的距离和角度约束条件,以建筑物与网眼的面积比作为建筑物移位的面积约束条件。针对可能出现的次生冲突,探讨了次生冲突的处理方法。试验结果表明,本文方法能够消除因道路拓宽导致的道路与建筑物冲突,并能完备地保持建筑物群的空间分布模式。     

基于弹性力学思想的居民地点群目标位移模型

本文引入弹性力学思想对空间目标进行冲突探测及受力分析 ,用空间距离聚类及建立最小生成树的方法把空间关系较近的、相互独立的点状要素聚类组合成多个有机的整体 ,对每个整体用有限元的方法通过多次迭代。本算法的实验结果满足了诸多的制图约束条件 ,能够很方便地应用在制图综合中的位移操作中     

Map Generalization with a Triangulated Data Structure

Automation of map generalization requires facilities to monitor the spatial relationships and interactions among multiple map objects. An experimental map generalization system has been developed which addresses this issue by representing spatial objects within a simplicial data structure (SDS) based on constrained Delaunay triangulation of the source data. Geometric generalization operators that have been implemented include object exaggeration, collapse, amalgamation, boundary reduction and displacement. The generalization operators exploit a set of primitive SDS functions to determine topological and proximal relationships, measure map objects, apply transformations, and detect and resolve spatial conflicts. Proximal search functions are used for efficient analysis of the structure and dimensions of the intervening spaces between map objects. Because geometric generalization takes place within a fully triangulated representation of the map surface, the presence of overlap conflicts, resulting from individual operators, can be detected due to the introduction of singularities in the triangulation, the structure of which is used to generate displacement vectors to resolve the conflict. Examples of the application of the implemented operators are described and illustrated using large scale topographic map data.     

多因子影响的地图居民地自动聚群与综合研究

提出了地图综合目的的居民地聚群需要遵循Gestalt的邻近性、相似性和方向性原则,描述居民地结构、形态及其关系需要6个因子,即居民地间的距离、可视区域面积、大小相似度、形状相似度、方向关系、居民地内部方向;进而运用这些原则和因子,给出了居民地的自动聚群和综合方法.     

多尺度等高线簇拓扑关系定量表达方法研究

等高线拓扑关系定量表达对空间认知、地形特征挖掘、地图综合等至关重要。然而,已有研究多针对等高线树的生成及综合算法,缺乏等高线拓扑关系及其与尺度变化关系的定量表达,不同地貌类型其变化规律是否一致仍未知。为此,通过分带与投影转换、非闭合等高线拓扑检查与闭合及有向等高线树的构建等,发展了一种等高线拓扑关系定量表达方法。采用高山、中山、平原地貌60组多尺度等高线数据集,探究不同地貌类型的多尺度等高线拓扑指标及其随尺度变化规律的一致性。试验结果表明,相邻尺度等高线拓扑指标变化规律符合空间认知与推理,不同地貌类型多尺度等高线拓扑指标与尺度变化间关系可用同一个幂函数定量表达,该结论为基于多尺度空间相似关系等高线全自动综合的实现提供了理论支撑。     

基于约束D-TIN的等高线簇与河网协同综合方法

传统制图综合中针对水系、地貌等不同地理要素的分治策略,往往导致综合后要素目标之间在空间与语义上的冲突.考虑地形与河流之间的空间依赖性,提出一种等高线簇与河网协同的制图综合方法,即利用两种要素目标顶点在约束D-TIN描述下的空间邻近关系,并通过提取和组织谷地等高线弯曲段数据,构建两要素有效协同关系.在此基础上将等高线按所...     

Quantitative relations between spatial similarity degree and map scale change of individual linear objects in multi-scale map spaces

Quantitative relations between spatial similarity degree and map scale change in multi-scale map spaces play important roles in map generalization and construction of spatial data infrastructure. Nevertheless, no achievements have been made regarding this issue. To fill the gap, this paper firstly proposes a model for calculating spatial similarity degrees between an individual linear object at one scale and its generalized counterpart at the other scale. Then psychological experiments are designed to validate the new model, taking four different individual linear objects at five different scales as test samples. The experiments have shown that spatial similarity degrees calculated by the new model can be accepted by a majority of the subjects. After this, it constructs a formula that can calculate spatial similarity degree using map scale change (and vice versa) for individual linear objects in multi-scale map spaces by the curve fitting method using the point data from the psychological experiments. Both the formula and the model can calculate quantitative relations between spatial similarity degree and map scale change of individual linear objects in multi-scale map spaces, which facilitates automation of map generalization algorithms for linear features.     

基于Snake模型的建筑物群移位算法改进研究

在对Snake模型研究的基础上,结合建筑物群在移位中的特点,从2个方面对Snake模型进行改进:首先,在Snake模型中加强对齐排列建筑物群这一重要空间特征的识别,以便保持对齐排列建筑物群在移位前后不变;其次,针对传统Snake模型有时无法解决两个建筑物空间冲突的缺点,对街区中的建筑物群实行分层次移位,先将有冲突的两个建筑物作为整体,与剩余其它建筑物利用Snake模型一起移位,然后再根据文中提出的建筑物间相互冲突的4种受力模型,进行建筑物间空间冲突的移位。最后利用改进的Snake模型进行移位实验和分析。