GIS数据格式转换的不确定性

从生产实践出发,描述了GIS数据格式转换的不确定性定义,探讨了形成GIS数据格式转换的不确定性原因,提出了避免GIS数据格式转换的不确定性的思路。     

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.     

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.     

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Advances in computer technologies have improved the quality of maps, making map comparison and analysis easier, but uncertainty and error still exist in GIS when overlaying geographic data with multiple or unknown confidence levels. The goals of this research are to review current geospatial uncertainty literature, present the Error‐Band Geometry Model (EBGM) for classifying the size and shape of spatial confidence intervals for vector GIS data, and to analyze the interpretability of the model by looking at how people use metadata to classify the uncertainty of geographic objects. The results from this research are positive and provide important insight into how people interpret maps and geographic data. They suggest that uncertainty is more easily interpreted for well defined point data and GPS data. When data is poorly defined, people are unable to determine an approach to model uncertainty and generate error‐bands. There is potential for using the EBGM to aid in the development of a GIS tool that can help individuals parameterize and model spatial confidence intervals, but more research is needed to refine the process by which people use the decision tree. A series of guiding questions or an “uncertainty wizard” tool that helps one select an uncertainty modeling approach might improve the way people apply this model to real‐world applications.     

对空间数据不确定性研究的思考

对GIS中空间数据不确定性研究中存在的问题进行思考后提出了5点建议。主要包括:1)要区分GIS中确定性目标和不确定性目标;2)要区分用离散点逼近曲线/曲面的逼近误差和离散点自身量测误差及其传播;3)要研究GIS中的几何不确定性,更要重视研究属性和时态不确定性;4)要研究空间数据质量,更要研究空间信息服务的质量;5)空间数据不确定性研究要努力向实际应用转化。     

基于GIS城市规划综合环境质量评价的研究

城市环境受自然因素和社会因素的相互作用愈来愈强烈。本文探讨了利用地理信息系统进行城市环境综合质量评价的技术途径。在GIS支持下,建立了城市规划环境质量评价因子数据库及评价指标体系,并确定了质量评价标准。基于GIS的城市规划综合环境质量评价是进行城市规划评估的一种有效方法,GIS技术为环境质量评价在不同模型条件下提供了有效的技术支持,改善了评价的效率及精度,将城市总体规划的布局及城市规划的三大效益(经济效益、环境效益和社会效益)联系起来,并在此基础上进行最终的决策评估。     

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.     

基于多层次模糊综合评判的GIS质量综合评价

本文用模糊数学中的多层次模糊综合评判原理对GIS质量进行评价。结果表明模糊综合评价方法不仅能够比较全面地考虑各种因素的影响及其相互作用,实现对GIS质量的合理、定量分析,并能在一定程度上反映出影响GIS质量的关键因素,为进一步提高GIS质量提供决策依据,而且该方法使用简单,结果具有科学性和合理性。本文首次提出用多层次模糊综合评判的方法对GIS质量进行评价,并且把对GIS质量评价进行量化处理。     

Geo-questionnaires in urban planning: recruitment methods,participant engagement,and data quality

ABSTRACT

Recent focus on sustainable urban development and livability has increased the demand for new data sourcing techniques to capture experiences and preferences of urban dwellers. At the same time, developments of geospatial technologies and social media have enabled new types of user-generated geographic information and spatially explicit online communication. As a result, new public participation GIS methods for engaging large groups of individuals have emerged. One such method is geo-questionnaire, an online questionnaire with mapping capabilities, which has been used to elicit geographic data in variety of topics and geographical contexts. This article presents two recent cases, in which geo-questionnaires have been used in Polish cities to obtain public input on quality of life and development preferences in local land use planning. The article evaluates participant recruitment methods focusing on sample representativeness, participant engagement, and data quality. Recruitment via social media was found to increase bias towards younger population. Paper questionnaires used along the online version provided for better representation of target population’s age structure, but did not reduce bias related to educational attainment. We discuss how these issues relate to data usability and generalizability in the context of digital divide, and suggest directions for future research.     

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。     

基于粗集理论的GIS属性数据挖掘

粗集理论作为一种新的处理含糊和不确定性问题的数学工具，可以有效地分析和处理不完备信息，已经在模式识别、机器学习、决策支持、过程控制、预测建模等众多科学与工程领域得到成功的应用，并具有相当的发展潜力。本文在深入研究粗集理论基础之上，讨论了基于粗集的知识表达系统，提出了属性约简的数据分析法，并利用此法对信息表中的海量信息进行简化，提取出有用特征，并相应评价各因素的重要性。本文最后以GIS属性挖掘为例进行实例分析，考察出它们的相互依赖性与相对重要性，取得了明显的效果。     

基于灰色聚类分析方法的生态环境质量综合评价模型

本文解释了生态环境质量评价的概念与现状,说明了灰色聚类分析的原理和一般过程,阐述了生态环境质量综合评价的基本步骤。以中国贵州省遵义县为研究区域,采用该县2003年的资源环境、人口社会经济及灾害数据,针对区域主要生态问题,选取评价指标体系,利用灰色系统理论的灰色聚类分析法建立了生态环境质量综合评价模型。评价结果显示东部生态环境质量较好,而西部较差。这与实际情况相吻合,说明该评价模型具有科学合理性与客观准确性。该评价方法也可用于其他区域的生态环境质量评价。     

GIS质量控制中不确定度理论

阐述了ＧＩＳ质量控制中不确定度的概念和定义,交 分为随机的和综合的不确定度两类,分别给出了其估计方法,并指出在ＧＩＳ数据中如何估计系统方差和置信系数,是需要深入研究和解决的难题。     

基于地形三维阴影图的等高线数据库后处理的质量控制方法研究

等高线矢量化完成后的数据质量检查是等高线数据库建设的关键环节。本文提出了一种简便、实用的等高线数据库后处理的质量控制方法。文章深入分析了天山公路GIS等高线数据库建设过程中错误的表现形式,分析了错误来源,并基于地形三维阴影图和地形图快速检测和修改这些错误,保证了等高线数据库的质量和精度。对其他GIS工程基础地理信息数据库建设具有参考价值和借鉴意义。     

GIS图上地理区域空间不确定性的分析

区域是GIS空间分析的基本单元。它是环境科学、土壤科学等领域中广泛存在的一类地理实体。这类实体抽象概括为GIS中空间目标时不可避免地带有模糊性。利用模糊集这个强有力的数学工具，首先基于场模型提出了表达GIS中模糊区域目标的方法，分析了其空间不确定性。继而，建立了模糊区域的形态描述模式，即模糊区域边界、内部和外部，并指出了Cohn给出的定义中的不足。在此基础上，对Egenhofer提出的九元组进行了扩展，构建了适合于描述确定和模糊区域目标间拓扑空间关系的模型。     

论GIS产品的模糊不确定性

本文从主观和客观两个方面论证了GIS产品的模糊不确定性,客观因素主要从GIS产品所需要的数据含有的模糊不确定性来论述;主观因素主要是从人对GIS产品质量观出发来论证。本文还给出了论证GIS产品的模糊不确定性的意义;本文最后对全文进行总结并指出GIS产品的模糊不确定性还有待研究。     

基于模糊重心综合评判的GIS数据产品质量评价研究

数据质量的好坏,直接影响着GIS应用分析结果的可靠性及应用目标的实现;而缺少数据质量衡量指标的GIS产品和其他产品一样,将无法得到用户的信任。因此,本文针对GIS数据产品的质量评价问题,基于模糊推理为主的重心综合评判法思想,提出应用模糊重心综合评判法对GIS数据产品的质量进行评价。同时结合实例与模糊评判法进行比较,证明模糊重心综合评判法评判的结果更为合理、可靠,认为其在GIS数据产品的质量评价方面是一种较为科学的方法。     

Assessing uncertainty due to elevation error in a landslide susceptibility model

A landslide susceptibility model, employing a digital elevation model (DEM) and geological data, was used in a GIS to predict slope stability in a region of the H J Andrews Long-Term Research Forest, located in the Western Cascade Range in Oregon, USA. To evaluate the contribution of error in elevation to the uncertainty of the final output of the model, several different, but equally probable, versions of the input DEM were created through the addition of random, spatially autocorrelated noise (error) files. The realized DEMs were then processed to produce a family of slope stability maps from which the uncertainty effects of elevation error upon landslide susceptibility could be assessed. The ability to assess this uncertainty has the potential to help us better understand the inherent strengths and weaknesses of applying digital data and spatial information systems to this application, and to facilitate improved natural resource management decisions in relation to timber harvesting and slope stability problems.