A spatial contextual immune genetic algorithm to building cartographic displacement

In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns.     

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。     

基于约束条件的地图目标移位

地图目标移位是地图综合和空间信息可视化研究中的一个重要问题,其难点是如何在移位过程中满足一定的约束条件以保持目标之间的空间关系。本文以城市道路扩张为例,借助带约束性Delaunay三角网,结合有限元和空间推理方法,重点探讨顾及空间关系的基于约束条件的地图目标移位问题,并给出满足一定约束条件下的节点移位算法和计算实例。     

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。     

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。     

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。     

顾及制图规则的地图多要素协同移位方法

在地图自动编制中,有时多类地图要素多个空间目标之间同时发生空间冲突,在移位过程中会影响到邻近区域的地图目标,这就需要对地图要素移位进行协同处理。在自动识别邻近空间冲突区的条件下,提出了一种顾及地图制图规则的解决地图目标群空间冲突的协同处理方法,并用参数表方法形式化表达了地形图上地图要素移位的常用制图规则。首先利用约束性Delaunay三角网识别出可能的空间冲突区域,并建立移位操作区;然后,建立移位传播关联线网,依据制图规则在关联线网上以能量最小化Beam移位模型为基础,对整个空间冲突区的地图目标群进行协同移位;最后,用实验验证了所提方法的有效性和适用性。     

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.     

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.     

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。     

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.     

Graph distance and feature-guided multi-view clustering: A novel method for clustering urban buildings

Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi-view building clustering method that captures cross-view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple-attribute graph). The three graphs undergo iterative cross-diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real-world community-building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering.     

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.     

Data matching of building polygons at multiple map scales improved by contextual information and relaxation

The aim of matching spatial data at different map scales is to find corresponding objects at different levels of detail (LODs) that represent the same real-world phenomena. This is a prerequisite for integrating, evaluating and updating spatial data collected and maintained at various scales. However, matching spatial data is not straightforward due to the ambiguities caused by problems like many-to-many correspondence, non-systematic displacement and different LODs between data sets. This paper proposes an approach to matching areal objects (e.g. buildings) based on relaxation labeling techniques widely applied in pattern recognition and computer vision. The underlying idea is to utilize contextual information (quantified by compatibility coefficient) in an iterative process, where the ambiguities are reduced until a consistent matching is achieved. This paper describes (1) a domain-specific extension to previous relaxation schemes and (2) a new compatibility coefficient that exploits relative relationships between areal object pairs in spatial data. Our approach were validated through extensive experiments using building data sets at 1:10k and 1:50k as an example. Our contextual approach showed superior performance against a non-contextual approach in general and especially in ambiguous situations. The proposed approach can also be applied to matching other areal features and/or for a different scale range.     

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.     

地图目标移位的空间关系维护

在分析有限元方法处理地图目标移位问题的基本思路和地图目标移位产生的空间关系冲突传播特征的基础上 ,提出了一个利用空间推理和有限元分析相结合的方法维护地图目标空间关系的算法     

顾及功能语义特征的建筑物空间分布模式识别方法

本文从空间-语义双重约束角度,提出一种顾及空间邻近和功能语义相似的建筑物空间分布模式识别方法。首先,基于建筑物的空间位置邻近性(即建筑物间的最小距离)约束进行聚类,获得建筑物的空间分布模式和建筑物间的空间邻近关系;然后,根据建筑物的功能语义相似性约束进行分割,获得建筑物的初步聚类结果;最后,考虑簇内相似性与簇间差异性进行整体优化,获得最终聚类结果。试验验证表明,本文方法比现有方法能够更有效地识别空间邻近与功能语义一致的建筑物群,服务于智慧城市建设中对建筑物进行语义层次综合和对城市结构进行深入研究的需求。     

A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density

Spatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a S patial A nomaly P oints and R egions D etection method using multi‐constrained graphs and local density ( SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm.     

顾及上下文的线状目标实体派生方法

针对制图自动综合中发现的线面数据存在的没有构建空间实体拓扑问题，本文提出一种顾及上下文的线状目标实体派生方法。首先，对围墙线、附属设施和建筑物建立空间拓扑邻近关系，对直接邻近群组进行聚类，然后，将其划分为3种模式，即两个围墙之间的拓扑连接、围墙与建筑物之间的拓扑连接及建筑物邻近的悬挂围墙线与建筑物之间的拓扑连接；最后，对这3种模式分别采用各自对应的最短路径、外轮廓线以及最近点的拓扑连接方法进行连接。以福建省数据为例进行试验，本文方法能够有效构建空间实体拓扑连接，有助于提高地图自动综合，同时能够满足生产实践的要求，具有很高的实用性。     

基于改进Snake模型的道路网空间冲突处理

在对Snake模型研究分析的基础上,结合地图制图的需求,从3个方面对Snake模型进行改进：首先建立Snake模型中参数与道路曲线形态特征的关系,以更好地保持移位前后道路形态的相似性;其次控制Snake模型中外力的传播范围,以尽量保持要素位置的准确性;最后,通过道路交叉点的权重属性控制,保证移位后各交叉点的连通性以及道路的整体拓扑关系不发生变化。在此基础上,提出道路网移位整体思路,并采用改进的Snake模型对其进行移位,解决空间冲突。