矢量GIS中模糊线元的位置不确定性研究

地理信息不确定性是GIS基础理论中一个重要的研究方面,当前对于地理信息不确定性的研究主要集中在确定性地理实体上,而对于没有明确空间范围定义的模糊地理实体则研究较少。同时,地理信息不确定性根据GIS中的数据组织形式大致有位置不确定性和属性不确定性两个方面,而由于在GIS的数据组织中属性数据和位置数据的密切联系,属性不确定性往往取决于位置不确定性,因此研究位置不确定性是研究地理信息不确定性的关键所在。基于矢量GIS,利用模糊数学、测量平差等知识,提出一个“扩展ε-band”方法来建立模糊线元的位置不确定性模型。     

遥感与地理信息系统数据的信息量及不确定性

讨论了遥感、GIS数据的不确定性与信息论中的不确定性间的联系，导出了GIS图形数据与遥感影像数据的信息量估算式，提出了位置疑义度和属性疑义度等概念。在统一的数学基础上，估算几何位置误差和属性正确率不足引起的不确定性，从而建立起遥感影像与GIS图形数据的信息量及不确定性的统一量度。     

论地理数据定义不确定性和量测不确定性的二象性

在将ＧＩＳ数据不确定性分类为定义不确定性和量测不确定性的基础上,采用辩证法和认识论的观点讨论了二者之间的关系和适用范围。定义不确定性是指客观实体特征向地理信息系统空间目标转化过程中引起的不稳定性;量测不确定性是指对空间目标赋值的不确定性。最后给出了地理数据总体不确定性的度量模型。     

利用信息扩散估计处理属性不确定性的研究

随着GIS应用的日益广泛，GIS数据的不确定性问题也越来越被人们所重视。经典的数据处理理论更多地讨论了位置的不确定性问题，而对属性数据的不确定性的研究较少。本文针时按照某种趋势在空间或时间上连续变化的定量属性，讨论了利用信息扩散估计对其进行处理的可能性。     

空间数据不确定性研究现状分析

归纳总结了位置不确定性、属性不确定性、时域不确定性、逻辑不一致性等方面的研究现状,重点归纳了位置不确定研究成果;最后指出了GIS不确定性研究存在的问题,并对GIS数据质量控制研究重点进行了分析。     

GIS不确定性研究与现状

空间数据及其质量客观上决定了GIS不确定性的产生和存在。描述了客观属性的空间数据的不确定性,以GIS空间线状实体位置不确定性理论为研究核心,采用不确定性引出GIS不确定性论题,阐述了若干相关概念,指出了空间数据与GIS的相互关系,以及研究进展。     

地理信息系统数据的不确定性问题

在总结当前GIS数据不确定性问题的研究进展和动态的基础上,论述GIS数据不确定性的框架体系,并分析探讨GIS数据不确定性的核心理论和主要研究内容,特别是位置不确定性、属性不确定性、时域不确定性、不确定性传播和管理等问题,最后对GIS数据不确定性的数学研究方法进行分析、归纳和阐述。     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.     

矢量GIS中属性数据的不确定性分析

本文从属性区域分类不确定性、边界定位误差和区域内部定量属性数据的抽样误差出发,综合进行属性数据不确定性的度量和传播分析。针对由边界定位误差引起的属性分类不确定性,在顾及区域面积与边界线数等因素下,提出了一种定量度量指标。对于属性区域内部的定量属性抽样值,则建立了抽样值的相关函数表达式。针对ＧＩＳ中常见的两大类操作,即逻辑操作和算术操作,分别根据模糊集合论和概率统计理论建立了相应的属性不确定性传播模     

GIS分析中的空间数据不确定性问题

摘要：地理信息系统使用参照空间、时间和属性的多维坐标来描述空间现象,而所有的空间模型的表示方法都存在着不确定性问题,并通过GIS的分析操作而传播。从空间数据质量、精度和应用的量级概念等方面探讨了空间数据不确定性问题的由来和发展,认为关注不确定性和误差应从“适合使用’’出发。     

The Visualisation of Uncertainty for Spatially Referenced Census Data Using Hierarchical Tessellations

This paper explains why it is vital to account for uncertainty when utilising socioeco‐nomic data in a GIS, focusing on a novel and intuitive method to visually represent the uncertainty. In common with other data, it is not possible to know exactly how far from the truth socioeconomic data are. Therefore, when such data are used in a decision‐making environment an approximate measure given for correctness of data is an essential component. This is illustrated, using choropleth mapping techniques on census data as an example. Both attribute and spatial uncertainty are considered, with Monte Carlo statistical simulations being used to model attribute uncertainty. An appropriate visualisation technique to manage certain choropleth issues and uncer‐tainty in census type data is introduced, catering for attribute and spatial uncertainty simultaneously. This is done using the output from hierarchical spatial data structures, in particular the region quadtree and the HoR (Hexagon or Rhombus) quadtree. The variable cell size of these structures expresses uncertainty, with larger cell size indicating large uncertainty, and vice versa. This technique is illustrated using the New Zealand 2001 census data, and the TRUST (The Representation of Uncertainty using Scale‐unspecific Tessellations) software suite, designed to show spatial and attribute uncertainty whilst simultaneously displaying the original data.     

GIS中属性不确定性的处理方法及其发展

属性数据的不确定直接影响决策的准确性和可靠性,特别是对侧重于属性分析的领域,在研究属性不确定性的基础上,分析了GIS中的主要处理方法及其研究进展,具体地就基于GIS的模型,概率论及数理统计学,模糊集合,云理论,粗集等方法及进展进行讨论.     

基于Monte Carlo方法的不确定性地理现象可视化

应用Monte Carlo方法伪随机数模拟表达随机过程现象的数学方法，并结合粒子系统模型提出了一种地理现象空间分布不确定性特征的动画可视化方法。通过栅格单元的随机运动，从视觉上表达现象分布在空间定位、属性特征上的不确定性、模糊性，同时由Monte Carlo方法控制随机过程中现象分布的统计规律。     

General entropy model of line segments uncertainty in GIS

Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomicity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total uncertainty caused by stochastic uncertainty and fuzzy uncertainty in GIS.     

The Nature of Uncertainty in Historical Geographic Information

While the presence of uncertainty in the geometric and attribute aspects of geographic information is well known, it is also present in temporal information. In spatiotemporal GIS databases and other formal representations, uncertainty in all three aspects of geography (space, time, and theme) must often be modeled, but a good data model must first be based on a sound theoretical understanding of spatiotemporal uncertainty. The nature of both uncertainty inherent in a phenomenon (often termed indeterminacy) and uncertainty in assertions of that phenomenon can be better understood through the Uncertain Temporal Entity Model , which characterizes the cause, type, and form of uncertainties in the spatial, temporal, and attribute aspects of geographic information. These uncertainties are the result of complexities and problems in two processes: the process of conceptualization, by which humans make sense of an infinitely complex reality, and measurement, by which we create formal representations (e.g. GIS) of those conceptual models of reality. Based on this framework, the nature and form of uncertainty is remarkably consistent across various situations, and is approximately equivalent in the three aspects, which will enable consistent solutions for representation and processing of spatiotemporal data.     

General Entropy Model of Line Segments Uncerta,nty in GIS

Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomicity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total uncertainty caused by stochastic uncertainty and fuzzy uncertainty in GIS.     

General Entropy Model of Line Segments Uncertainty in GIS

Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomic-ity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total un-certainty caused by stochastic uncertainty and fuzzy uncertainty in GIS.