面要素空间信息量的度量方法研究
收稿日期: 2012-11-01
修回日期: 2012-12-01
网络出版日期: 2012-12-25
基金资助
国家自然科学基金项目(41171351);中央高校基本科研业务费青年助推项目;江西省数字国土重点实验室2012年度开放基金资助项目(DLLJ201204)。
An Approach to Measuring the Spatial Information Content of an Area Feature
Received date: 2012-11-01
Revised date: 2012-12-01
Online published: 2012-12-25
地图是空间信息的载体,地图空间信息的度量是地图信息传输理论的一个基础问题。地图空间信息主要包括地图要素的空间信息和要素分布的空间信息。地图空间信息是由要素的几何形态结构来体现,即要素的空间信息通过其几何形态特征描述。为此,本文以面要素为研究对象,提出一种以几何形态结构特征的面要素空间信息量度量方法。首先,从空间认知角度对面要素结构进行凸包分解,构建凸包树的面要素表达方法。然后,采用层次化策略,分别从结点的元素、邻域和整体三个层次来描述面要素几何形态结构,将面要素空间信息分解为几何形态信息和分布结构信息,结合面要素空间信息量的认知分析,给出了几何形态和分布结构特征的定量描述指标,并发展了基于几何形态结构特征的面要素空间信息量计算模型。最后,通过一组实验计算,进行了案例验证分析。
刘慧敏, 邓敏, 何占军, 徐震 . 面要素空间信息量的度量方法研究[J]. 地球信息科学学报, 2012 , 14(6) : 744 -750,774 . DOI: 10.3724/SP.J.1047.2012.00744
Map is a visualization representation of geospatial entities and their distribution. Users often can obtain large amount of information through reading a map. The measurement of map information content is one of the most important basic research issues in the theory of map information transmission. It has been preliminarily applied to map generalization and many other aspects of map applications. Spatial information of a map contains that of features and their distributions. Existing methods of measuring spatial information content only consider the information content of spatial distribution among the features. In other words, the information content of spatial features is not involved. Therefore, the results of the information content obtained by existing methods are inaccurate. For this purpose, in this paper we focused on the development of a methodology for the information content measurement of individual spatial features, where area features are chosen as an example. As a matter of fact, it has been extensively accepted that geometric shape is deemed to be the carrier of geospatial information content of an area feature. As a result, the convex hull is firstly used for shape decomposition of individual area features and a hierarchical structure called convex hull tree is proposed to represent an area feature from the view of spatial cognition. Secondly, geometric shape of area features is analyzed according to the nodes of convex hull tree at three levels, namely, node level, neighborhood level and global level. Moreover, quantitative indicators at each level are defined and utilized for the description of geometric shape, including edge number as the indicator of shape complexity, and convexity as that of shape pattern at node level, out-degree at neighborhood level and layer at global level as indicators of geometry distributions. Sequentially, the corresponding computational models are respectively developed based on geometry characteristics at three levels, which are further used to measure spatial information content of individual area features. At last, an example is provided to illustrate the rationality and the accuracy of the proposed methods.
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