地理信息的时态性分析及时空数据模型的研究

地理信息系统中 ,时间、空间和属性是信息的三种基本成分 ,理想的地理信息系统应支持信息的时态性 ,对时空数据进行统一的模拟和管理。由于当前的地理信息系统软件难以处理时态现象 ,时空数据模型已成为GIS领域的一个研究热点。本文首先分析了地理信息的时态性 ,之后讨论了当前时空数据模型的研究现状 ,在此基础上 ,提出了一种面向对象时空数据模型 ,并从三个方面描述了该模型的数据结构 ,包括时空对象结构、时空数据组织方式和时空数据存贮方式     

一种面向对象的时空数据模型

地理信息系统中，时间、空间和属性是信息的3种基本成分，理想的地理信息系统应支持信息的时态性，对时空数据进行统一的模拟和管理。由于当前的地理信息系统软件难以处理时态现象，时空数据模型已成为GIS领域的一个研究热点。首先对当前时空数据模型研究现状进行了分析，在此基础上，提出了一种面向对象时空数据模型，并从3个方面描述了该模型的数据结构，包括时空对象结构、时空数据组织方式和时空数据存贮方式。     

Gail Langran时空数据模型的统一

在辩证认知时间和空间的独立与相关、绝对与相对、动态与静态特征的基础上,将地理时空数据模型看作是时态地理信息系统(TGIS)的一种缩影,达到对各种类型时空数据模型的概念统一。提出了利用嵌套立方体、层次复合、高阶差分、变粒度快照四种时空数据模型来丰富Gail Langran时空数据模型的层次结构信息。进一步地,将改进的Gail Langran时空数据模型综合为多尺度时空数据模型并给出其代数定义,达到对近15 a来时态地理信息系统中主流时空数据模型(Gail Langran时空数据模型)及其众多变种的形式统一,使Gail Langran时空数据模型及其若干非规范化变种成为多尺度时空数据模型的不同特例。多尺度时空数据模型对于开发强可伸缩性、尺度透明性和环境自适应时态地理信息系统等具有重要的理论价值。     

ArcGIS支持下的土地利用时空数据库设计与功能实现

时空数据模型是时态地理信息系统(TGIS)的核心,它能够有效组织和管理时态地理数据,但由于其复杂性,目前仍然是一个非常具有挑战性的课题。本文介绍了与时空数据模型相关的研究概况,包括时空数据模型的研究进展、时空数据库中的时间和时空拓扑关系,探讨了土地利用时空数据模型的设计和功能实现,阐述了时空数据模型中变化的概念,在此基础上利用基态修正模型的思想,对模型的框架进行了设计,并给出了变化信息的提取方法,利用ArcGIS丰富的空间分析功能对变化土地利用信息进行了提取,最后给出了时空数据库结构的设计。     

地籍管理中时空数据模型研究

地籍信息系统是一种典型的时态地理信息系统,其技术关键就是在于用适当的时空数据模型来组织地籍数据库。本文简要介绍了几种主流的时空数据模型,重点从建模方法、时空数据组织和时空数据操作三个方面阐述了地籍管理领域时空数据模型的研究进展,探讨了存在的问题,提出了未来发展方向的建议。     

时态GIS在森林资源更新管理方面的研究进展

随着地理信息系统(GIS)应用领域的不断深入,基于时态GIS技术的森林资源更新管理成为一个重要的研究方向。本文在森林资源小班数据更新分析的基础上,对比总结了当前主流的时空数据模型,介绍了时空数据库的构建形式和森林资源数据的更新流程,为时态GIS技术在森林资源管理中的进一步研究提供了方向。     

基于特征-版本的时空数据模型在湿地管理中的应用研究

讨论了传统时空数据模型的特点,设计了Geodatabase支持下的基于特征-版本的时空数据模型,并详细介绍了该模型的时态逻辑关系和存储结构。该模型节约了数据存储空间,有效保持地理现象的完整性,并具有较高的时空查询效率。针对扎龙湿地,构建了湿地时空数据库,并设计扎龙湿地时态地理信息系统,实现历史重构、地理对象发展与回溯、时空复合查询等功能,有效完成湿地时空数据的管理任务。     

版本差量式时空数据模型研究

时空数据模型是时态地理信息系统的关键和核心。本文在分析国内外已有研究成果的基础上,结合国家基础测绘项目,提出了“版本-差量”式时空数据模型,采用“版本管理-动态关联”以及“动态分级索引”技术实现时态地理数据的存储管理和查询检索。该模型能有效降低数据冗余,提高数据访问效率。     

GIS中的时空数据模型研究

状态与事件是时态地理信息系统(TemporalGIS ,TGIS)的一对基本概念,目前时空数据模型研究侧重于将两个概念相分离,表现为基于状态模型和基于事件模型,无法兼顾两种模型的优点。本文提出了基于状态-事件的时空数据模型,并系统阐述各时空数据模型之间的相互转换条件。     

空间信息服务时态数据的管理与应用

本文面向当前空间信息应用的实际需求,以传统GIS为基础,研究建立实用的、易于实现的集矢量与栅格数据为一体的时空数据模型,增加GIS对时态属性的管理和操作。本文研究解决了时态拓扑构建的关键技术,采用面向对象方法开发了一套时态空间数据库管理系统,实现了时空数据的采集、存储、查询和显示,并以中国多个历史时期的行政界线为基础数据,进行了时空数据模型的应用研究,为TGIS发展探索了一条实用的技术路线。     

Temporal Zooming

Spatio-temporal knowledge representation often requires changing from one level of detail or granularity to another so users can carry out a desired task. Meteorological occurrences, geological processes, or population movements, for example, can be examined at different granularities. This includes different spatial perspectives as well as temporal views where phenomena may be examined under real time conditions, or over hourly, daily, weekly, or longer periods. Moving back and forth among granularities is a necessary routine for many domain scientists. Changing to a more detailed view uncovers information that otherwise is unknown. Conversely, moving to a simpler view can improve our understanding of phenomena. Although people routinely abstract information from their environment at different granularities and perform mental shifts that increase or decrease detail, formalization of these alterations for use in information systems has proved difficult. Geographic information systems typically treat changes in granularity from the perspective of changes to the geometric properties of objects through graphic zooming. None of the current approaches to zooming offer support for performing this operation over time. This work focuses on the temporal aspects of changing granularity or temporal zooming . The approach is based on a model of change to identifiable objects. In this paper, temporal zooming involves expanding or collapsing the transitions among identity states of the same object as well as revealing or omitting other objects that are linked through transitions. A set of operations to support refining and coarsening the evolution of objects over time is presented. This work offers a new perspective on zooming important for spatio-temporal query languages and for users of large spatio-temporal databases who require the means to shift back and forth among simpler or summarized views of data and more detailed views.     

基于GML的地籍时空数据组织方法

空间地理数据由于内容和来源的差异导致数据之间转换十分困难,而基于不同空间数据模型、不同软件平台的数据,由于通常都具有各自的数据标准和数据格式,并且软件平台相对独立而封闭,因此,也无法直接进行系统之间的数据共享。本文对GML的空间信息表达和时空拓扑分析进行研究,将GML技术与时空表达相融合,并以地籍信息管理为例,将GML应用在地籍数据建模上,对于数据的交换与传输、共享与集成都是一种新的尝试。     

时态GIS及其应用

介绍时态GIS在国内外的研究概况,在此基础上介绍时态GIS的核心——时空数据模型,给出了时态GIS在地籍管理中的应用实例。并对时态GIS研究作出了一些展望。     

时间动态地图模型

就时间动态地图模型进行了研究，认为动态地图由于表达空间维数的增加，使得地图作为客观现实世界的概念模型更加完善，并且作为物质模型，动态地图的可视化应与空间认知的计算机表达紧密融合。在制作时间动态地图时，不同的视觉变量具有不同的动态表现能力，根据时态现象的特征进行选取。     

Temporal Interpolation of Spatially Dynamic Polygons

In this study we compare two methods of interpolating simple metrics from polygons that change over time. Conventional methodology involves computing values of the metrics at points in time where the polygon is observed, and interpolating between these observed values to estimate values at other points in time. An alternative method applies interpolation techniques to the locations of vertices composing polygons are interpolated between observations and metrics are computed from the interpolated polygon shapes. We conducted a Monte Carlo simulation of both techniques using simulated polygons composed of varying numbers of vertices, which were allowed to move randomly over varying numbers of discrete time periods. We observed differences in the success of the two methods when applied to three metrics: Area, Perimeter, and Area/Perimeter Ratio. We found the two techniques to perform differently depending upon the metric being interpolated. Full results are presented and possible reasons why the techniques succeeded or failed under various conditions are discussed.     

Temporal correlation for network RTK positioning

Temporal correlation in network real-time kinematic (RTK) data exists due to unmodeled multipath and atmospheric errors, in combination with slowly changing satellite constellation. If this correlation is neglected, the estimated uncertainty of the coordinates might be too optimistic. In this study, we compute temporal correlation lengths for network RTK positioning, i.e., the appropriate time separation between the measurements. This leads to more realistic coordinate uncertainty estimates, and an appropriate surveying strategy to control the measurements can be designed. Two methods to estimate temporal correlation lengths are suggested. Several monitor stations that utilize correction data from two SWEPOS^TM Network RTK services, a standard service and a project-adapted service with the mean distance between the reference stations of approximately 70 and 10–20 km, are evaluated. The correlation lengths for the standard service are estimated as 17 min for the horizontal component and 36–37 min for the vertical component. The corresponding estimates for the project-adapted service are 13–17 and 13–16 min, respectively. According to the F test, the proposed composite first-order Gauss–Markov autocovariance function shows a significantly better least-squared fit to data compared to the commonly used one-component first-order Gauss–Markov model. A second suggested method is proposed that has the potential of providing robust correlation lengths without the need to fit a model to the computed autocovariance function.     

时空地统计学方法研究及进展

随着理论技术的不断发展,对环境因子仅进行空间估计和量化已不再满足精准农业等地理分支学科的精度要求,因此人们在传统地统计方法的基础上衍生出时空地统计学。该方法将时间域和空间域紧密联系起来,运用时空插值和随机模拟,更客观全面地描述地物的区域性时空分布。本文首先阐明了时空地统计学的主要概念及历史应用领域,随后介绍了当前主流的时空地统计方法及其各自的精度与适用领域,最后总结了时空地统计方法研究的不足和方向。     

草地光谱分类最佳时相选择分析

利用2003年5-10月在环青海湖地区获取的典型天然草地与人工草地多时相地面高分辨率光谱数据，首先分析了最大似然分类法、支持向量机分类法、光谱角分类法、最小距离分类法和人工神经网络分类法所对应的最佳光谱变换方案；通过16个时相光谱数据的分类对比实验，分别确定了天然草地与人工草地分类、人工草地分类、天然草地分类的最佳时相；最后利用TM遥感数据对地面光谱数据分析结果进行了补充验证。     

Toward a Differential Calculus for Temporal Map Analysis

Investigators in many fields are analyzing temporal change in spatial data. Such analyses are typically conducted by comparing the value of some metric (e. g., area, contagion, or diversity indices) measured at time T₁ with the value of the same metric measured at time T₂ . These comparisons typically include the use of simple interpolation models to estimate the value of the metric of interest at points in time between observations, followed by applications of differential calculus to investigate the rates at which the metric is changing. Unfortunately, these techniques treat the values of the metrics being analyzed as if they were observed values, when in fact the metrics are derived from more fundamental spatial data. The consequence of treating metrics as observed values is a significant reduction in the degrees of freedom in spatial change over time. This results in an oversimplified view of spatio-temporal change. A more accurate view can be produced by (1) applying temporal interpolation models to observed spatial data rather than derived spatial metrics; (2) expanding the metric of interest's computational equation by replacing the terms relating to the observed spatial data with their temporal interpolation equations; and (3) differentiating the expanded computational equation. This alternative, three-step spatio-temporal analysis technique will be described and justified. The alternative technique will be compared to the conventional approach using common metrics and a sample data set.     

时态对象结构的代数模型

论文引入元组、集合、序列和时态4个基本构造子来建立时态对象结构的代数模型,同时例证了各种传统时态关系模型向时态对象模型的转化。研究表明,时态对象模型在复杂结构和时态语义建模方面比时态关系模型更具优势。