Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation

A new method of cartographic line simplification is presented. Regular hexagonal tessellations are used to sample lines for simplification, where hexagon width, reflecting sampling fidelity, is varied in proportion to target scale and drawing resolution. Tesserae constitute loci at which new sets of vertices are defined by vertex clustering quantization, and these vertices are used to compose simplified lines retaining only visually resolvable detail at target scale. Hexagon scaling is informed by the Nyquist–Shannon sampling theorem. The hexagonal quantization algorithm is also compared to an implementation of the Li–Openshaw raster-vector algorithm, which undertakes a similar process using square raster cells. Lines produced by either algorithm using like tessera widths are compared for fidelity to the original line in two ways: Hausdorff distances to the original lines are statistically analyzed, and simplified lines are presented against input lines for visual inspection. Results show that hexagonal quantization offers advantages over square tessellations for vertex clustering line simplification in that simplified lines are significantly less displaced from input lines. Visual inspection suggests lines produced by hexagonal quantization retain informative geographical shapes for greater differences in scale than do those produced by quantization in square cells. This study yields a scale-specific cartographic line simplification algorithm, following Li and Openshaw's natural principle, which is readily applicable to cartographic linework. Open-source Java code implementing the hexagonal quantization algorithm is available online.     

Cities On the Plains: The Evolution of Urban Kansas

The process of congressional redistricting, delineating boundaries for districts in which voters elect members to the U.S. House of Representatives, has always been an expensive and controversial process. Congressional districts (CDs) are redrawn due to changes in population reflected by the decennial census to ensure equal representation. Laws and regulations literature identifies eight criteria that may be considered when determining the boundaries of CDs and this article focuses on one of those criteria, maintaining communities of interest (COIs). This criterion requires states to preserve these boundaries when delineating CDs but fails to define a COI. This research proposes and evaluates two approaches to define a COI and examines the extent to which this criterion has been adhered to. One definition uses Thiessen polygons and census designated places to delineate COIs based on known cultural places, whereas the other definition uses cluster analysis to group together people with similar sociodemographic characteristics. The results show that the two definitions are feasible for defining a COI. Furthermore, the states largely maintain the COI boundaries within their CDs by only splitting, at most, 17.1 percent of the COIs defined. Existing literature shows that maintaining COIs within CDs leads to higher voter participation and engagement, as well as better representation. The results show that if either definition was adopted, states could comply with this criterion with relative ease. Furthermore, a standard definition could help reduce the cost and controversy surrounding the redistricting process.     

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This research proposes and evaluates a family of six hierarchical regionalization methods. The six methods are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), and the complete linkage (CLK), each of which is constrained with spatial contiguity in two different ways (i.e. the first‐order constraining and the full‐order constraining). It is discovered that both the Full‐Order‐CLK and the Full‐Order‐ALK methods significantly outperform existing methods across four quality evaluations: the total heterogeneity, region size balance, internal variation, and the preservation of data distribution. Moreover, the proposed algorithms are efficient and can find the solution in O(n ²log n) time. With such data scalability, for the first time it is possible to effectively regionalize large data sets that have 10 000 or more spatial objects. A detailed comparison and evaluation of the six methods are carried out with the 2004 US presidential election data.     

中国大陆若干地震构造带的地震准周期丛集复发行为

以青藏高原东南缘和东北缘若干地震构造带的历史地震活动资料为基本数据，通过地震复发间隔的计算，分类和统计检验等，研究了大陆板内地震的复发行为。认为：大陆板内地震既不是简单地准周期重复发生，也不是简单地丛集复发，而可能是一种准周期的丛集复发行为，即地震丛是准周期重复发生的，而在某一地震丛内，地震又是丛集复发的。存在两类地震复发间隔，即地震丛复发间隔和地震丛内的地震复发间隔，它们分别满足对数正态分布和指数分布。在进行大陆板内活动断裂地震危险性定量计算和评估时，不能简单地套用N．B．实进模型中给出的地震复发间隔概率密度函数或累积分布函数，而应该根据当前地震的活动水平，判断当前是属于丛内的活跃期还是丛间的平静期，据此分别选用地震丛内地震复发间隔分布函数或者地震丛复发问题隔分布函数，这样将大大提高活动断裂地震危险性计算和定量评估的水平。最后，简要探讨了大陆板内地震准周期丛集复发行为的可能物理机制。     

Heavy minerals clustering analysis in application of provenance analysis of Kong 2 Member in Kongnan area

The main task of provenance analysis is to determine the source of sediments and the position of parent rocks.Provenance analysis may find out the relationship between erosion districts and sediment zone,between the uplift and the depression in the process of basin development.The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member.Research shows that there were five provenance areas of Kong 2 Member in Kongnan area.They are western(Shenusi),northwestern(Cangzhou),eastern(Ganhuatun),northeastern and southeastern.The main provenance areas were northwestern and western,while the southern provenance could not be ruled out.And these areas are consistent with the known provenance areas.     

Rule reduction in fuzzy logic for better interpretability in reservoir operation

Decision‐making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering‐based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine‐tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd.     

An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.     

多层次空间同位模式自适应挖掘方法

空间同位模式挖掘旨在从空间数据中发现频繁发生在邻近位置的事件集合,对于揭示地理现象间的共生规律具有重要价值。由于地理现象的空间异质特质,空间同位模式也存在区域性分异的特点,在不同空间层次上的分析结果各异。然而,现有方法仅从全局视角挖掘空间同位模式,发现局部空间同位模式依然是一个亟待解决的难题。为此,本文基于由整体到局部的思想,提出了一种多层次空间同位模式自适应挖掘方法。首先,从全局视角提取频繁的空间同位模式,将全局不频繁的空间同位模式作为候选的局部空间同位模式;然后,通过对候选局部同位模式进行自适应聚类自动识别其局部分布区域,并在这些局部区域内度量候选模式的频繁程度;进而,提出了一种叠置推绎的方法,从频繁子模式的局部区域中进一步推绎获得超模式的局部分布区域,最终生成所有频繁的局部空间同位模式集合。通过试验分析与比较发现,本文方法不仅可以发现全局的空间同位模式,还能有效提取具有区域性分布特征的局部空间同位模式,可以从多个空间层次上反映地理事件间的共生规则。     

一种可见光影像稠密匹配点云的单栋建筑物提取方法

建筑物点云提取是城市快速三维建模的基础。针对城区中建筑物和树木空间距离较近导致建筑物点云误提取的问题,提出一种颜色约束的欧式聚类算法。该方法利用低空拍摄可见光影像进行三维重建、获取点云数据,在建立点云K邻域索引和表面估计的基础上,以曲率最小的点作为欧式聚类的种子点,将点云的RGB值转换成Lab颜色模型,对建筑物点云的聚类提取进行约束。实验表明,该方法可以有效地解决可见光影像匹配点云中建筑物提取时将树木误提取的问题。     

基于地理国情普查高分辨率卫星遥感数据正射影像制作

随着卫星遥感技术的不断发展,卫星影像成图精度越来越高,利用高分辨率卫星影像获取地理国情信息是地理国情普查工作的重要技术手段。本文以QuickBird、WorldView卫星遥感数据处理为例,描述了将Geoway CIPS集群式遥感影像处理软件和Inpho软件相结合,进行正射影像图制作中纠正、融合、匀光匀色等技术流程和工作方法。此方法可提高影像整体质量,有效地减少影像调色工作,从而提高影像生产效率。