一种基于场论的空间异常探测方法

从空间数据场的角度,借鉴高斯势函数发展了一种新的空间异常度度量指标。进而,提出了一种基于场论的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的空间簇,并构建合理、稳定的空间邻近域。在此基础上,采用专题属性变化梯度修复策略减弱空间邻近域中潜在异常的影响,并利用空间异常度度量指标计算实体的异常度,从而探测空间异常。实验结果及实例证明了此方法的正确性。     

从GIS数据库中挖掘空间离群点的一种高效算法

根据GIS的空间特性，借鉴已有的定义和概念，定义了空间离群点是和在其非空间属性邻域内其他空间对象在空间位置上差异十分显著的空间对象，并设计了SOD算法。实验结果验证了SOD算法的有效性和优越性，给出了从GIS数据库中挖掘空间离群点的一般步骤。     

一种空间交叉异常显著性判别的非参数检验方法

空间异常探测旨在从海量空间数据中挖掘不符合普适性规律、表现出“与众不同”特性的空间实体集合,对于揭示地理现象的特殊发展规律具有重要价值。现有研究在空间异常度量方面取得了重要进展,但多缺乏对空间异常模式显著性的统计判别,且是针对单一类别数据,没有顾及多类别数据间的相互影响。为此,本文基于空间随机过程的思想,针对两种类别空间点数据,提出了一种空间交叉异常显著性判别的非参数检验方法。首先,针对基本数据集实体,采用约束Delaunay三角网,构建合理、稳定的空间邻近域;然后,统计落在基本数据集实体空间参考邻域半径范围内的参考数据集实体的数目,度量初始异常度;进而,采用α-Shape法构建支撑域,以空间随机过程为基础构建零模型,采用蒙特卡洛模拟检验空间异常的显著性;最后,采用生存距离对异常模式的稳定性进行评价分析。通过试验分析与比较发现,该方法能够有效识别具有统计显著性的空间交叉异常。     

一种面向GIS的时空索引方法

对时空索引的技术要求进行了说明，分析了目前的时空索引方法及其局限性，提出了基于最小限定盒的时空索引结构——STB树，并对其运算算法进行了描述。     

一种顾及邻近域内实体间距离的空间异常检测新方法

空间异常检测已成为空间数据挖掘和知识发现的一个重要研究内容.空间异常蕴含着许多意想不到的知识,现有的空间异常检测方法大多依据空间邻近域的非空间属性差异来计算偏离因子,忽略了邻近域内空间实体间距离的影响.本文首先讨论了空间邻近域内实体间距离对空间异常检测的影响,在此基础上,提出了一种顾及邻近域内实体间距离的空间异常度量方法--SOM法,并分析了它的复杂度.由于该方法是利用实体非空间属性的加权内插值与实测值的差值作为度量空间异常程度的参数,从而顾及了邻近域内所有实体相互间距离对非空间属性偏离的影响,并且克服了现有检测方法在不均匀分布空间实体集内寻找空间异常的缺陷.最后,通过一个实际算例验证了所提方法的可行性和正确性.     

时空变化分类与表达研究

为了解决空间数据库更新过程中地理对象时空变化类型不明晰的问题,利用语义、空间、时态和属性4个组件描述地理对象,并以地理对象集合、地理对象、对象组件以及对象组件属性4个具有层次关系的因子实现地理对象时空变化的分类与表达,通过分析确定了单一对象的17种变化类型、多个对象的4种变化类型,以及每种变化对应的数据库操作。最后,以一个实例说明空间数据变化类型的判定过程。     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

采用聚类技术探测空间异常

提出了一种基于聚类的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的实体集合,分别探测空间异常,给出了一种稳健的空间异常度量指标,提高了异常探测结果的可靠性。通过实例验证以及与SOM方法的比较分析,证明了该方法的正确性和优越性。     

一种分布式计算的空间离群点挖掘算法

针对现有空间离群点挖掘算法无法适应大规模空间数据挖掘的需求,该文提出了一种分布式条件下的空间离群点挖掘算法。首先,该文针对集群上分布式计算和存储的特点提出使用空间填充曲线来划分数据集,加速寻找目标点的近似空间最近邻居。其次,使用信息熵的理论来定义空间离群系数,考虑到多维数据中不同属性对离群系数的影响具有差异性,该算法能够自动根据数据原有特点,计算各属性的权重;同时使用反距离权定义空间因素对离群系数的影响。最后,实验结果表明该算法在大规模的空间数据集中挖掘离群点的效率远高于传统算法,离群点的挖掘精度在90%以上。     

遥感影像变化检测样本自动抽样

在遥感影像结合矢量数据先验信息的变化检测中,需要从分割后的影像对象中抽取一定数量、具有相同类别属性的样本,其中不可避免地抽到类别属性不一致的样本,如何剔除这些样本是抽样过程中必须解决的重点问题,在目前已有的方法中,一般是通过人工目视判别完成的。样本的自动提取是实现自动变化检测的关键环节,本文提出一种变化检测样本自动抽样方法,主要包括样本的空间布设和异常样本自动检测两个环节。该方法首先利用矢量数据提取抽样图层,用抽样图层分割遥感影像,获取影像对象。其次是根据抽样区域范围、影像对象分布特征和地形特征布设变化检测样本。然后根据样本的先验类别属性构建特征空间向量,计算样本在特征空间的局部可达密度,由局部可达密度计算样本的异常度指数,并根据特征空间密度异常指数剔除异常样本,完成变化检测样本自动提取。最后以耕地、林地和居民地为例进行了抽样试验。结果表明,邻域参数k按样本布设总数的1/5—1/3取值、异常度阈值设定为80%时,可以实现异常样本0漏检率,能够准确、高效实现变化检测样本的自动提取。     

GIS中面向对象时空数据模型

由于当前的地理信息系统软件难以处理时态现象，时态数据模型已忧为ＧＩＳ领域的一个研究热点。许多学者提出了多种时态数据模型。本文作者在提出了矢量栅格一体化的面向对象数据模型之后，再一次对时态问题进行了分析研究，净面向对象的数据模型扩充到时间维。有三种方法表达空间对象的历史变化。第一种是将版本信息记录在关系表上；第二种是将版本信息标记在记录上；第三种是将版本信息标记在属性上。本文采用面向对象的方法，将版     

Detection of clusters in traffic networks based on spatio‐temporal flow modeling

Spatio‐temporal clustering is a highly active research topic and a challenging issue in spatio‐temporal data mining. Many spatio‐temporal clustering methods have been designed for geo‐referenced time series. Under some special circumstances, such as monitoring traffic flow on roads, existing methods cannot handle the temporally dynamic and spatially heterogeneous correlations among road segments when detecting clusters. Therefore, this article develops a spatio‐temporal flow‐based approach to detect clusters in traffic networks. First, a spatio‐temporal flow process is modeled by combining network topology relations with real‐time traffic status. On this basis, spatio‐temporal neighborhoods are captured by considering traffic time‐series similarity in spatio‐temporal flows. Spatio‐temporal clusters are further formed by successive connection of spatio‐temporal neighbors. Experiments on traffic time series of central London's road network on both weekdays and weekends are performed to demonstrate the effectiveness and practicality of the proposed method.     

时空一体化框架下时空异常探测

：提出一种时空一体化的时空异常探测方法,首先基于时空统计学与聚类分析构建一体化时空邻近域。进而, 发展兼顾时空相关与异质性的时空异常度量方法。最后,采用一种3步骤的策略探测时空异常。应用本文方法探测中国 陆地区域33年(1970年—2002年)的年平均气温时空数据中的时空异常,探测结果具有较好的可靠性,反映时空数据的时 空一体化特征。同时,对时空异常的产生机理与实际意义进行分析和解释。     

Bayesian methods for outliers detection in GNSS time series

This article is concerned with the problem of detecting outliers in GNSS time series based on Bayesian statistical theory. Firstly, a new model is proposed to simultaneously detect different types of outliers based on the conception of introducing different types of classification variables corresponding to the different types of outliers; the problem of outlier detection is converted into the computation of the corresponding posterior probabilities, and the algorithm for computing the posterior probabilities based on standard Gibbs sampler is designed. Secondly, we analyze the reasons of masking and swamping about detecting patches of additive outliers intensively; an unmasking Bayesian method for detecting additive outlier patches is proposed based on an adaptive Gibbs sampler. Thirdly, the correctness of the theories and methods proposed above is illustrated by simulated data and then by analyzing real GNSS observations, such as cycle slips detection in carrier phase data. Examples illustrate that the Bayesian methods for outliers detection in GNSS time series proposed by this paper are not only capable of detecting isolated outliers but also capable of detecting additive outlier patches. Furthermore, it can be successfully used to process cycle slips in phase data, which solves the problem of small cycle slips.     

Modelling the spatial evolution of map objects by map agents

To overcome the weakness of modelling the relationships between map objects that represent the same geographic entities or phenomena at different times and scales, an agent-based approach is presented to modelling of the spatial evolution of map objects for better understanding of the evolutional process of different maps. Map agents are used to establish and manage the many-to-many corresponding relationships between map objects, and they render map objects active rather than traditionally passive. The evolutions of map objects are modelled by map agents using association and generation relationships to model the connectivity between map objects. This effort reduces the workload of multi-scale map updating by avoiding update of the entire map; instead, it simply allows map agents to detect, perceive and choose suitable behaviours on which to operate and update a spatially evolving object. Experiments of specific examples are presented to demonstrate the feasibility and the effectiveness of our approach.     

粗集方法在地图综合中的应用

将粗集理论与方法应用于地图综合时,根据地图中空间信息的特性及其生成属性决策表的特殊性,提出先利用无监督的离散化方法对各连续型条件属性分别离散化后再根据属性决策表的应用目的对空间目标进行模糊分类添加决策属性的思路;然后利用粗集属性约简的方法计算各条件属性的重要性,并采取动态排序的方法对空间目标的重要性进行判断。该方法可以很好地解决将粗集应用于地图综合中无法预先确定决策属性的问题,对空间点目标的选取实验结果表明该方法是合理有效的。     

顾及属性空间分布不均的空间聚类方法——以城市商业中心的提取为例

针对Delaunay三角网空间聚类存在的不足,提出一种顾及属性空间分布不均的空间聚类方法。首先将Delaunay三角网空间位置聚类作为约束条件,采用广度优先搜索方法,以局部参数"属性变化率"作为阈值识别非空间属性相似簇的聚类过程。以城市商业中心为例,验证了该方法能够更客观地识别非空间属性相似的簇,且自适应属性阈值可以满足不同聚类需求,为城市商业中心等空间实体的提取提供了一种有效方法。     

Development of a GIS data model with spatial, temporal and attribute components based on object-oriented approach

1 lntroductionA Mraphical object normal1y poSSeSSes threecomPOnents: spatiaI, temporal and attribute asPeCts.The first comPOnent describes the spatial extent ofan object, such as the boundary of a Iand parcel.The second describes timesrelated information, fOrinstance, the beginning and end time of a land Par-cel. The third describes the attribute characteristicsof obects, fOr example, the type of land cover.These three comPOnents constitute a complete im-age of an object.In most of the…     

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

The dispersion of communicable diseases in a population is intrinsically spatial. In the last several decades, a range of spatial approaches has been devised to model epidemiological processes; and they differ significantly from each other. A review of spatially oriented epidemiological models is necessary to assess advances in spatial approaches to modeling disease dispersion and to help identify those most appropriate for specific research goals. The most notable difference in the design of these spatially oriented models is the scale and mobility of the modeling unit. Using two criteria, this review identifies six types of spatially oriented models. These include: (1) population‐based wave models, (2) sub‐population models, (3) individual‐based cellular automata models, (4) mobile sub‐population models, (5) individual‐based spatially implicit models, and (6) individual‐based mobile models. Each model type is evaluated in terms of its design principles, assumptions, and intended applications. For the evaluation of design, four aspects of design principles are discussed: the modeling unit, the interaction between the modeling units, the spatial process, and the temporal process utilized in a design. Insights gained from this review can be useful for devising much‐needed spatially and temporally oriented strategies to forecast, prevent, and control communicable diseases.