农村集体土地所有权数据库建设研究

旨在探讨农村集体土地所有权数据库建设方法.以西吉县为例,基于2011年度土地变更调查数据库,在野外实地权属调查基础上,以GIS技术为手段建立标准化农村集体土地所有权确权登记发证数据库,并对该数据库建设流程进行了介绍.科学建立数据库可以更好地实现集体土地所有权数据科学、灵活、安全和高效管理,为保护农民集体土地合法权益提供更为有效的依据和手段.     

OGC CSW规范实现探讨

OGC CSW规范的实现,主要包括元数据信息模型建立、属性项映射、结果编码格式定义和软件实现,实现过程中需要注意参数格式标准化、参数约束条件、参数变化、元数据信息模型、名称空间以及该规范的局限性。介绍OGC CSW规范与目录服务标准及相关规范的关系,回顾该规范实现情况,结合全国测绘成果目录服务系统研发建立的工作实践,对OGC CSW规范的实现步骤和需要注意的问题进行讨论,并以全国测绘成果目录服务系统为例,介绍OGC CSW规范的应用效果。     

平面监测网的动态滤波

在假定形变区域为分块连续弹性体的基础上,本文提出一个所谓虚拟形变速率参数形变模型,由它的特例可推出常见的单点位移形变模型。之后又讨论和研究了平面监测网的观测数据滤波问题,设计出一个虚拟形变速率参数卡尔曼滤波器,具体做了这几项工作:(1)在所提出的形变模型下,推导出平面监测网的状态方程;(2)解决了初始状态及其方差阵的估计问题;(3)在极大似然的原则下,得到了动态噪声方差阵的逐次修正公式。在对两个模拟网的几种不同方案进行试算之后,本文提出了几点结论和建议。     

基于GIS的物流配送中心选址模型研究

配送中心是物流网络中的重要节点,对于优化企业物流系统,合理配置库存资源,提高物流的共同化程度发挥着重要作用。本文应用G IS网络分析方法和改进P中心选址算法,建立了配送中心选址优化模型。该模型由几何网络确定配送中心与需求点间距离、并引入租金、坡度、库存量等因素参与模型计算,通过总费用最小化确定仓库的最佳位置。因采用多因素参与决策和算法的改进,提高了配送中心选址精度,降低了用户选择的盲目性。     

遗传算法在组分温度反演中的应用

介绍了遗传算法的主要内容和工作原理。在连续植被热辐射方向性模型的基础上,从热红外多角度遥感数据中,同时反演混合像元组分温度、土壤比辐射率以及叶面积指数。大量试验表明,利用遗传算法反演组分温度效果非常好。在宽松的先验知识条件下,该方法可以解决不确定性反演问题     

模拟退火算法在动态建立基准地价模型中的应用

阐述了传统基准地价评估模型建立流程将地价样本检验与模型建立分离的不足,引入了模拟退火算法思想,对目标函数、初始状态、新状态产生、新状态接受、初温、温度更新函数以及抽样准则、算法终止准则等关键问题进行设计,将地价样本检验、优化组合与模型建立有机结合,并设计了基于模拟退火算法的动态建立基准地价模型的流程,最后应用实例验证了方法的可行性。     

基于关系数据库的邻接域处理

本文就跨图幅的地物(邻接域)的管理问题作了一些有益的探讨，为了达到有效管理这个目的，本文采用Oracle公司提供的空间数据插件Spatial将空间数据和属性数据存入Oracle数据库中，这样不仅可以通过标准SQL语句可下裁任意区域的数据，而且数据库还会自动维护数据的完整性。因此，用空间数据库的管理方式使邻接域问题就不存在了。     

Similar single-difference model and its algorithm for solving GPS monitoring deformation directly at single epoch

A new similar singledifference mathematical model ( SS-DM) and its corresponding algorithm are advanced to solve the deformation of monitoring point directly in single epoch. The method for building the SSDM is introduced in detail, and the main error sources affecting the accuracy of deformation measurement are analyzed briefly, and the basic algorithm and steps of solving the deformation are discussed. In order to validate the correctness and the accuracy of the similar single-difference model, the test with five dual frequency receivers is carried out on a slideway which moved in plane in Feb. 2001. In the test, five sessions are observed. The numerical results of test data show that the advanced model is correct.     

Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis

The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405–3421, 2006) in case of no missing data in the time series and to the extended ‘stacking’ approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999–2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data.     

Variance components in errors-in-variables models: estimability,stability and bias analysis

Although total least squares has been substantially investigated theoretically and widely applied in practical applications, almost nothing has been done to simultaneously address the estimation of parameters and the errors-in-variables (EIV) stochastic model. We prove that the variance components of the EIV stochastic model are not estimable, if the elements of the random coefficient matrix can be classified into two or more groups of data of the same accuracy. This result of inestimability is surprising as it indicates that we have no way of gaining any knowledge on such an EIV stochastic model. We demonstrate that the linear equations for the estimation of variance components could be ill-conditioned, if the variance components are theoretically estimable. Finally, if the variance components are estimable, we derive the biases of their estimates, which could be significantly amplified due to a large condition number.