以误差椭圆长半轴表示带宽的线元位置不确定性ε_E模型

考虑实用性和合理性,将线元看成离散点的集合,将线的不确定性看成点的不确定性的聚合体,将线元的位置不确定性模型看成以各点误差椭圆的长半轴E为半径的误差圆的聚合体,建立了以线元上任意点处的误差椭圆的长半轴E为带宽的线元不确定性εE模型。给出了基于该模型衡量线元位置不确定性的三种度量指标:可视化图形、平均误差带宽和误差带的面积。最后,将该模型与εσ模型和εm模型进行了比较。     

2维线元不确定性εσ模型误差带几何特征的代数研究

对地理信息系统（GIS）中2维线元不确定性εσ模型的误差带的几何特征进行研究。运用函数单调性和极值理论,从理论上证明εσ模型的误差带的几何特征;得到线元误差带不仅具有“两端大、中间小”,而且也可能具有“一端大、一端小”的几何形状;给出了误差带的最小带宽及其位置;论证了误差带的最小误差带宽的位置靠近中误差较小的一端。本研究完善了2维线元不确定性εσ模型,使其更具有严密性,同时为GIS空间数据不确定性的研究提供了新的方法。     

以点位误差描述线元位置不确定性的误差带方法

从实用的角度出发,提出按两端点的点位误差描述线元误差带的方法.主要内容包括对各种情况加以解释并给出各种不同情形的统一公式.     

矢量GIS平面随机线元误差模型建模机理

基于随机线元误差分布机理 ,研究了GIS中平面随机线元位置不确定性误差模型的建模原理 ,提出了决定误差模型形状的形状因子与误差模型规模的尺度因子的概念与确定方法 ,结合线元落入其等概率密度误差模型内的概率算法 ,解决了平面随机线元误差模型的形状与规模     

GIS空间数据几何变换的不确定性传播

针对点位误差、线元在对称、旋转变换过程中不确定性的传播规律,该文采用误差椭圆和εm模型描述GIS空间数据对象模型中最常用的点、线元素的误差域,讨论几何变换(如对称、旋转变换)过程中不确定性的积累和传播规律,结合区间算法给出了变换后误差域模型。基于区间运算的INTLAB模拟结果验证了该模型的有效性和实用性。研究结果对进一步探讨其他几何变换过程中不确定性的积累传播规律具有一定参考。     

TIN DEM线性内插不确定性的随机过程模型

基于随机过程模型导出了TIN DEM线性内插的随机过程模型,给出了不规则随机空间三角形的不确定性描述,讨论了TIN节点误差在线性内插中的传播问题。通过理论推导和实际算例,得到了TIN DEM线性内插点的点位方差和误差椭球半轴的解析表达式、线性内插精度最高点坐标的解析表达式,该结论与三角形的形状无关;对DEM线性外推导致精度急剧下降的必然性结论进行了理论证明;得到TIN线性内插的平均点位方差解析式,从理论上说明了本文结论的有效性。     

GIS中多维点数据的误差区间分析法研究

GIS中线面位置的不确定性,归根结底是点的不确定性。对于点位的不确定性可视化,已有很多研究[5-6,8],本文正是在此基础上展开的。在误差理论中,误差椭圆有举足轻重的地位,对点位质量用生动的图形灵活地表现出来。本文则用误差椭圆(椭球)方程求其最小外接矩形(长方体),并给出计算该误差区间置信度的方法,说明在多维联合正态分布的点位误差区间置信度计算上,当且仅当相关系数为零时,也可使用x2分布查表求得。在描述点位精度时,误差区间描述极为简单,也便于参与其他计算,具有良好的实用性和应用价值,这对描述点位精度有一定的积极作用。     

矢量GIS空间随机线元等概率密度误差模型

从空间解析几何学的角度,基于平面随机线元等概率密度误差模型建模原理,研究了矢量GIS空间随机线元位置不确定性误差模型的建模原理,提出并证明了“空间线元上任意点Pt处用以构建空间线元等概率密度误差模型体的误差椭球三轴长在数值上等于相应空间点处标准误差椭球对应三轴长的[m(λA,t)]2倍,且该空间点处误差椭球三轴线各自对应的空间向量方位保持不变”的重要结论,这对于矢量GIS空间线状实体位置不确定性误差模型的建模具有指导意义。     

GIS中线元的误差熵带研究

基于现有的线元位置不确定性模型大多与置信水平的选取有关,而置信水平的选取带有一定程度的主观性,因而不能惟一确定,引入信息熵理论,提出了线元的误差熵带模型,并将它与“E－带”进行了比较,计算了落入其内的概率。该模型根据联合熵惟一确定,与置信水平的选取无关。     

GIS中平面面位误差环的解析模型

本文基于随机场理论,导出了随机面元的分布函数和概率密度函数。为了衡量随机面元的位置不确定性,将点位误差椭圆和线位误差带进一步扩展到面位误差环指标。根据推求包括线的原理,导出了多边形面位误差环边界线的解析表达式,并分析了面位误差环的构成机理,证明了误差环边界线为连续闭合曲线的结论。最后通过实例绘制了面位误差环的可视化图形。     

Entropy error model of planar geometry features in GIS

Positional error of line segments is usually described by using “g-band”, however, its band width is in relation to the confidence level choice. In fact, given different confidence levels, a series of concentric bands can be obtained. To overcome the effect of confidence level on the error indicator, by introducing the union entropy theory, we propose an entropy error ellipse index of point, then extend it to line segment and polygon, and establish an entropy error band of line segment and an entropy error donut of polygon. The research shows that the entropy error index can be determined uniquely and is not influenced by confidence level, and that they are suitable for positional uncertainty of planar geometry features.     

考虑参数误差的3维空间直线不确定性模型

由于线元上任一点坐标的误差不仅受端点误差的影响,还会受到长度误差的影响,故不确定性模型要考虑各种影响位置精度的参数误差,对3维空间直线不确定性模型作了进一步研究。不但考虑了端点误差的影响,还顾及了长度误差的影响,使模型在理论上更为严密。理论和实验研究表明,长度误差影响了直线方向的精度。     

考虑参数误差的3维空间直线不确定性模型

由于线元上任一点坐标的误差不仅受端点误差的影响,还会受到长度误差的影响,故不确定性模型要考虑各种影响位置精度的参数误差,对3维空间直线不确定性模型作了进一步研究.不但考虑了端点误差的影响,还顾及了长度误差的影响,使模型在理论上更为严密.理论和实验研究表明,长度误差影响了直线方向的精度.     

Relationship of Uncertainty and Line Segment for Between Polygon Segment Spatial Data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.     

矢量GIS中随机折线定位不确定性的可视化模型

折线是ＧＩＳ中表达线形空间实体的基本制图要素。本文针对由随机折线点构成的折线要素建立了一种可视化误差模型。首先引入了随机折线要素误差带的基本概念,并导了误差带的边界线数学方程;然后针对开折线和闭折线两种情况绘出了误差带的可视化图形,并分析了形状特征,从而将单一随机折线元的误差带理论进一步扩展到一整条随机折线的一般情况。     

多尺度表达中线状要素的综合不确定性评价模型

在考虑节点化简的基础上建立了节点数据不确定性评价模型,基于曲线光滑模型建立了线元模型不确定性评价模型,在此基础上,根据不确定性传播律构建了由数据不确定性和模型不确定性合成的线状要素多尺度表达不确定性的综合评价模型。实验表明,综合不确定性指标值作为线状要素多尺度表达不确定性的量化指标是有效的。可将其用于计算线元不确定带的宽度,解决线状要素多尺度表达不确定性空间分析和推理问题;并用于线状要素多尺度表达的质量评价与控制。     

Relationship of Uncertainty Between Polygon Segment and Line Segment for Spatial Data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of εp is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator, The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.     

Relationship of uncertainty between polygon segment and line segment for spatial data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of ?σ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of ?m and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.     

AUSGeoid2020 combined gravimetric–geometric model: location-specific uncertainties and baseline-length-dependent error decorrelation

AUSGeoid2020 is a combined gravimetric–geometric model (sometimes called a “hybrid quasigeoid model”) that provides the separation between the Geocentric Datum of Australia 2020 (GDA2020) ellipsoid and Australia’s national vertical datum, the Australian Height Datum (AHD). This model is also provided with a location-specific uncertainty propagated from a combination of the levelling, GPS ellipsoidal height and gravimetric quasigeoid data errors via least squares prediction. We present a method for computing the relative uncertainty (i.e. uncertainty of the height between any two points) between AUSGeoid2020-derived AHD heights based on the principle of correlated errors cancelling when used over baselines. Results demonstrate AUSGeoid2020 is more accurate than traditional third-order levelling in Australia at distances beyond 3 km, which is 12 mm of allowable misclosure per square root km of levelling. As part of the above work, we identified an error in the gravimetric quasigeoid in Port Phillip Bay (near Melbourne in SE Australia) coming from altimeter-derived gravity anomalies. This error was patched using alternative altimetry data.