A Topology‐Inferred Graph‐Based Heuristic Algorithm for Map Simplification

In this article we present a heuristic map simplification algorithm based on a novel topology‐inferred graph model. Compared with the existing algorithms, which only focus either on geometry simplification or on topological consistency, our algorithm simplifies the map composed of series of polylines and constraint points while maintaining the topological relationships in the map, maximizing the number of removal points, and minimizing error distance efficiently. Unlike some traditional geometry simplification algorithms, such as Douglas and Peucker's, which add points incrementally, we remove points sequentially based on a priority determined by heuristic functions. In the first stage, we build a graph to model the topology of points in the map from which we determine whether a point is removable or not. As map generalization is needed in different applications with different requirements, we present two heuristic functions to determine the priority of points removal for two different purposes: to save storage space and to reduce computation time. The time complexity of our algorithm is which is efficient enough to be considered for real‐time applications. Experiments on real maps were conducted and the results indicate that our algorithm produces high quality results; one heuristic function results in higher removal points saving storage space and the other improves the time performance significantly.     

基于遗传算法的空间聚类分析

在分析现有空间聚类方法的基础上,结合遗传算法基本原理和特征,提出一种基于遗传算法的空间聚类分析方法,并讨论方法实施中的关键问题.实验结果表明,该方法在聚类中心点的选取上保持了空间全局分布特性,效果良好.     

矢量居民地多边形多级图划分聚类方法

针对复杂居民地多边形的信息挖掘问题,提出了一种多级图划分聚类分析方法,构造居民地多边形的图模型,并通过对图模型进行粗化匹配与重构、初始化分和细化得到聚类结果.首先构建研究区域内居民地建筑物的Delaunay三角网,生成包含研究对象之间的邻接信息图;然后结合空间认知准则和人类认知的特点,采用形状狭长度、面积比、凹凸性、距...     

叠置分区辅助的相位编组直线提取算法

针对现有相位编组方法在区域分界线处产生边缘断裂及同一分区内直线拟合难题,提出了一种叠置分区辅助的相位编组直线提取算法。该算法通过两次分区生成交叠的八分区模式,二次分区中心线与一次分区分界线相重合。首先根据初始四分区,将梯度相位相同且相互连接的边缘点编组生成直线支持区,再对其进行边缘分裂,进而拟合出对应的直线。然后将不满足一次分区条件的边缘点再依据二次分区进行直线提取,以弥补一次分区在分界线附近产生的边缘断裂。本文算法原理简单,不需要参数调整。试验验证和对比分析表明,该算法不仅能有效、准确地提取影像上的直线特征,而且对于影像上的曲线特征也能通过直线拟合得到较好的提取结果。     

Combining Geo‐SOM and Hierarchical Clustering to Explore Geospatial Data

Geo‐SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo‐SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo‐SOM and hierarchical clustering to tackle this problem. Geo‐SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo‐SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.     

球面地理网格剖分方案分析

为了便于空间数据描述、分析和比较,提出了直接在地球参考椭球面上构建全球地理网格的剖分模式,并逐渐成为地理网格剖分模型的重要发展方向。介绍了全球球面网格剖分的特点和发展现状,分析了当前球面网格剖分方法的分类;并在理想地理网格剖分标准的基础上,探讨了将其作为统一空间参考系的空间位置描述、空间数据集成和空间分析的全球球面网格剖分规则与方案,以及剖分所形成网格单元的位置描述方法。     

Area‐preservation Simplification of Polygonal Boundaries by the Use of the Structured Total Least Squares Method with Constraints

This article presents an area‐preservation approach for polygonal boundary simplification by the use of structured total least squares adjustment with constraints (STLSC), with the aim being to maintain the area of the original polygons after the simplification. Traditionally, a simplified line is represented by critical points selected from the original one. However, this study focuses on maintaining the areas of the polygons in the process of simplification of polygonal boundaries. Therefore, the proposed method in this article is a supplement to the existing line simplification methods, and it improves the quality of the simplification of polygonal boundaries in terms of positional and area errors. Based on the sub‐divisions of the original polyline, using the critical points detected from the polyline by the use of line simplification methods, the framework of the proposed method includes three main components, as follows: (1) establishment of the straight‐line‐segment fitting model based on both the critical and intermediate points on the sub‐polyline; (2) introduction of both area and end‐point constraints to reduce the geometric distortions due to the line simplification; and (3) derivation of the solution of boundary simplification by the use of STLSC. An empirical example was conducted to test the applicability of the proposed method. The results showed that: (1) by imposing the linear fitting model on both the critical and intermediate points on the sub‐polylines in the proposed STLSC method, the positional differences between the original points and the simplified line are approximately in a normal distribution; and (2) by introducing both end‐point and area constraints in the proposed STLSC method, the areas of the simplified polygons are the same as those of the original ones at different scales, and the two neighboring fitted lines are connected to each other at the optimized position.     

电离层总电子含量时空特征分析及分区建模

利用地统计方法研究了全球VTEC时空分区特征,提出了IONEX单天产品发布的改进策略和VTEC时空分区建模方法,包括组合函数与IRI2007模型背景场建模方法。纬度10°～70°、经度60°～180°范围内单天VTEC分区建模与结果分析表明,分区建模极大地降低了模型的复杂性。上述两种方法均可得中低纬分区B13约为高纬分区A23中误差(2～3TECU)的2倍,验证了顾及VTEC时空特征分区建模的有效性。     

Street‐level Spatial Interpolation Using Network‐based IDW and Ordinary Kriging

This study proposes network‐based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross‐validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network‐based methods and their conventional counterparts. The study suggests that both network‐based IDW and network‐based OK are generally more accurate than their existing counterparts, with network‐based OK constantly outperforming the other methods. The network‐based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network‐based OK improve as more sample locations are used, whereas those of standard IDW and network‐based IDW remain stable regardless of the number of sample locations. The two network‐based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations.     

Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.     

基于SOFM网络的声图非监督分类

利用局部窗口内的灰度纹理共生矩阵的统计量、灰度均值和两个分维数作为特征矢量,利用SOFM网络进行非监督分类侧扫声纳海底图像,通过实测数据验算,取得了理想的效果。     

利用凝聚层次聚类的多视影像重建算法

无序多视影像的三维重建由于缺乏先验信息而可能导致重建结果发生漂移。提出一种稳健的重建算法,以匹配点对数量作为影像间的相似性量度,利用层次聚类算法对影像集进行分层分组,通过自下而上的顺序,对各层中的每一组子影像集进行重建;然后利用公共点将相邻子影像集间的重建结果进行融合,直到完成整个影像集的重建。最后通过3组具有代表性的影像集的重建试验,验证了算法的有效性。     

Anomaly Detection from Hyperspectral Images Using Clustering Based Feature Reduction

An anomaly detection method with a clustering based feature reduction is proposed in this paper to improve the performance of the Local RX detector. Because of high dimensionality of hyperspectral image and the low number of available samples in each local region around each testing pixel, the estimate of local covariance matrix is not possible. So, because of singularity problem, Local RX cannot use the local covariance matrix and misses the local structures of data to model the background clutter. To deal with this problem, a supervised clustering based feature reduction is introduced for extraction of background features with minimum overlap and redundant information. In the projected feature space with reduced dimensionality, the local structures of background pixels are estimated to efficiently model the background data. The experiments done on both synthetic and real hyperspectral images show the superior detection performance of the proposed method with a relatively high speed.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

顾及密度对比的多层次聚类点群选取方法

在语义信息缺乏的情况下进行点群选取是制图综合的难点之一。提出了一种新的通过多层次聚类进行点群选取的方法。首先,针对k-means聚类算法的不足,利用改进的密度峰值聚类算法实现点群自动聚类,主要表现为用基尼系数确定最优截断距离及用局部密度和相对距离的关系自动确定聚类中心。其次,提出一种顾及密度对比的选取策略,通过点群多层次聚类,将点群划分成不同等级的簇,确定不同等级的聚类中心,建立点群的层次树结构;依据方根定律计算的选取数量,按照各级别簇的点数比例,自上而下逐层分配待选取点数,确定选取对象,实现点群的自动选取和多尺度表达。对不同分布模式的点群进行实验,验证了该方法的普适性和有效性。     

利用模糊密度聚类和双向缓冲区自动识别热点区

通过数据挖掘手段获取聚集模式（即热点）等地理空间知识是地理信息智能化服务的基础和前提。点群聚集模式的提取本质上是热点及其边界（热点区）的探测。首先分析了使用空间聚类提取热点并以凸壳表达热点轮廓的不足,进而提出一种利用模糊密度聚类和双向缓冲区的热点区自动识别方法。该方法借鉴模糊集理论,通过计算对象之间的模糊隶属度改进基于密度的聚类算法,用以提取点群的聚集模式;在此基础上,将模糊隶属度作为对象间的影响程度,采用正负缓冲区建立热点边界。以郑州市城区的科研机构点为例进行实验,结果表明,提出的方法既能有效区分空间点的类型（噪声点与非噪声点）,又能生成连续平滑的热点边界,总体效果优于对比方法。     

An Extension of Virtual Globe to Build On‐Demand Geospatial Data Sharing Capability Using Peer‐to‐Peer Technology

In the virtual globe system, ever higher performance is demanded for high quality service, especially when requesting intensive data from large‐scale networks, such as large scene 3D city models with fine textures. Extending the virtual globe platform from the present centralized mode into a decentralized mode can significantly improve platform capacity and service quality without additional investment in hardware, but presents new challenges such as the time cost of finding a peer, unstable offering, and low service capacity. A GeoODP2P network is designed based on specific features of spatial data transmission. Next, we discuss peer selection mechanisms that relieve the negative influence of peers, and propose a pre‐fetch mechanism that takes advantage of the unique characteristics of the P2P environment. This article implements a simulation system for a GeoODP2P network and presents a prototype platform that incorporates P2P functionality. Peer selection and pre‐fetch mechanisms are studied and tested on this simulation system. The prototype platform run in a real scenario demonstrates the effectiveness of applying P2P to a virtual globe platform.     

高光谱影像的改进K-均值监督式聚类分析方法

针对K-均值聚类存在的初始聚类中心不稳定、聚类数目难以确定的问题,提出利用正交投影散度(OPD)优化K-均值算法的初始聚类中心,设计了RD指标函数用于估计聚类数目k。将所提出的算法应用于高光谱影像特征提取与端元提取分析,实验结果表明,所提出算法的性能高于已有的类似算法。     

北京绿化隔离地区绿化而积监测及分析

为有效监督北京市绿化隔离地区的绿化进展情况,北京市测绘设计研究院实施了对绿化隔离地区2000～2003年现状绿地的钉桩.应用地理信息系统叠加(Overlay)技术,产生了不在绿化隔离地区内的绿化地块以及虽在绿化隔离地区内但与规划严重不符的绿地地块;对各区的规划绿地指标、实际绿地指标、不合格地块以及地块合格率进行统计.研究表明2000～2003年全市绿化隔离地区范围内新增绿化面积89 km2,基本完成了政府任务.政府应加强对绿化工作完成数量和质量尤其是质量的监管.各区完成绿地程度不同地存在与规划不一致的现象.     

基于颜色特征利用色矩与BTC法进行影像聚类

影像聚类是一种对影像数据进行分组的方法,在基于内容的影像检索中,如果能够利用较低层次的可视特征进行高效的影像聚类,将会大大提高影像检索的精度.文章分别利用色矩法与分块截短编码(BTC)方法提取影像颜色特征,然后采用K均值聚类算法来对两种方法进行聚类分析.实验结果表明,分块截短编码(BTC)方法的聚类精度优于色矩法.