利用约束D-TIN进行建筑物多边形凹部结构识别与渐进式化简

分析了位于建筑物多边形外侧的凹部结构特征及其类型,探讨了一种基于凹部层次结构的建筑物多边形渐进式化简方法,即以三角形为形状基元,用约束Delaunay三角化方法对建筑物多边形进行空间剖分,从凹部三角形树中提取三角形序列,通过匹配特征序列识别凹部的基本模式,以确定和实施相应的凹部化简方法,在此基础上迭代执行识别-化简过程,以实现对建筑物复杂凹部的渐进式化简。实验分析表明,该方法具有结构化和渐进综合的特点。     

Effect of spatial and temporal scales on habitat suitability modeling: A case study of Ommastrephes bartramii in the northwest pacific ocean

Temporal and spatial scales play important roles in fishery ecology, and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution. The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling, with the western stock of winter-spring cohort of neon flying squid(Ommastrephes bartramii) in the northwest Pacific Ocean as an example. In this study, the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature(SST) from remote sensing during August to October of 2003–2008 were used. We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales(0.5°, 1° and 2°), four longitude scales(0.5°, 1°, 2° and 4°), and three temporal scales(week, fortnight, and month). The coefficients of variation(CV) of the weekly, biweekly and monthly suitability index(SI) were compared to determine which temporal and spatial scales of SI model are more precise. This study shows that the optimal temporal and spatial scales with the lowest CV are month, and 0.5° latitude and 0.5° longitude for O. bartramii in the northwest Pacific Ocean. This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts. We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.     

面向地形可视化的多种投影方式研究

为了获得形式多样的地形可视化效果,投影方式的选择至关重要,通过对正投影、透视投影、渐进式投影和平斜投影等多种投影方式的原理与方法进行研究,分析了各种投影方式的适用性及其获得的地形可视化效果,为应用实践提供了有益借鉴。     

一种分布式空间数据的服务聚合模型

数据源的异构、GIS服务规范的自定义以及分布式存放等问题制约了空间信息的广泛共享和应用。本文基于空间信息共享平台建设,围绕服务动态聚合的思想,结合OGC的标准规范和技术思想,在具有REST风格的GIS服务的基础之上,提出了一个服务聚合模型,通过该模型实现了分布式存放的异构瓦片数据的无缝集成显示以及网络要素服务的功能共享。     

Spatial object aggregation based on data structure,local triangulation and hierarchical analyzing method

This paper focuses on the methods and process of spatial aggregation based on semantic and geometric characteristics of spatial objects and relations among the objects with the help of spatial data structure (Formal Data Structure), the Local Constrained Delaunay Triangulations and semantic hierarchy. The adjacent relation among connected objects and unconnected objects has been studied through constrained triangle as elementary processing unit in aggregation operation. The hierarchical semantic analytical matrix is given for analyzing the similarity between objects types and between objects. Several different cases of aggregation have been presented in this paper.     

一种可配置的富客户端地图聚合应用模式研究

Web GIS技术的诞生给GIS应用开发带来了前所未有的革新,尤其是富客户端地图聚合技术的产生,给基于Web的GIS应用带来了很大的灵活性。传统的Web GIS应用通常都是按照特定客户的数据环境和应用需求进行定制开发的,为了客户操作的方便而在应用程序中对数据结构及其内容进行了固化,具备了方便地满足该客户在特定时间的需求的能力。然而,这类应用往往很难在无须更新代码的情况下满足用户随着时间的推移而需要加入新的地图数据或业务数据、配置新的专题应用地图的需求,尤其无法满足在无须调整代码的情况下将同样的程序应用在一个需求相似、数据不同的新用户那里。本文介绍了一种新型的可配置扩展的富客户端地图聚合应用模式设计及其应用情况。结果表明,这种新型的应用模式充分发挥了富客户端地图聚合技术的优势,结合基于统一数据库设计的后台配置管理系统,完全能够在零编程的情况下快速地响应客户对于应用新数据的需求,以及将同一应用快速移植、应用到新用户场景的需求。     

甘肃敦煌西湖荒漠-湿地生态系统优势植物种群分布格局及种间关联性

基于25个样地、375个样方和150条样线的调查资料,采用扩散系数(C)、负二项参数(K)、平均拥挤度(m*)、丛生指标(D、聚块性指标(PI)、Green指数(GI)、Cassie指标(GA)和Moristia指数(Ⅰδ)等8个指标以及方差比率(VR)法,对甘肃敦煌西湖荒漠-湿地生态系统优势植物种群的空间分布格局、聚集强度和种间总体关联性进行了研究.结果表明:①研究区8种优势种群均为聚集分布(p＜0.01).其中,多枝柽柳群落、多枝柽柳沙包群落和胡杨群落均具有较高聚集程度,而疏叶骆驼刺群落和芦苇群落均具有较低聚集程度.②研究区优势种群中灌(乔)木层均为聚集分布(p＜0.05).③研究区优势种群在各海拔梯度上均为聚集分布(p＜0.05).随海拔梯度的增加,种群聚集强度增加,且种群聚集程度变化较为平缓.④研究区植物群落中,灌(乔)木层主要种群的种间联结表现为显著的正关联,乔灌草和草本层的主要种群的种间联结表现为显著的负关联.应用2×2联列表x2统计量、联结系数(AC)和共同出现百分率(PC)等方法,综合分析了敦煌西湖主要种群的种间联结显著性和关联强度.结果表明:研究区大多数种群种间关联程度不显著(p＞0.05),不关联的种对数明显要多于显著关联种对数,显著负关联的种对数多于正关,说明种群对生境和资源要求不一样,与总体种间相关性检验的方差比率VR值的结果一致,群落中优势物种处于不稳定阶段.     

Area aggregation in map generalisation by mixed-integer programming

Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time.     

Detecting negative spatial autocorrelation in georeferenced random variables

Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon.