基于主体人与地学本体的人-地-GIS关系讨论

“人-地-GIS”关系是地理信息科学及相关研究的基础。传统的人-地-GIS双向联系模型正在向以地学本体为核心的本体中心模型演化。通过主体人与客体人的区分,探讨两者在“人-地-GIS”关系中的地位与作用,分析人-地、地-GIS与人-GIS的相关关系。通过地学本体与本体论的分析,讨论地学本体与科学本体、哲学本体论之间的关系,分析地学本体与类、元数据之间的区别与联系。进而构建以地学本体为核心的“人-地-GIS”关系新模型,该模型整体上分为认知与再现两部分,由核心的本体模型及人的认知层、GIS的模型层与“地学”现象及其关系层构成。以主体人的认知为主线,讨论该模型中各模块的功能与相互关系,指出今后的发展方向。     

基于角色的土地利用规划管理信息系统访问控制模型设计与实现

土地利用规划系统中敷据众多,空间数据库权限管理任务繁重。笔者提出以土地利用规划管理为业务主线,面向事务和角色管理的县级土地利用规划管理系统中基于角色的访问控制模型。实践表明,该模型减轻了系统日常维护管理负担,降低了对系统管理员的要求。     

2010年北京市流动人口预测

针对北京市流动人口样本数据量小、不连续等特点,本文采用Logistic曲线拟合和等维递补灰色预测理论,预测“十一五”期间北京流动人口的增长情况。首先,通过Logistic曲线拟合与外推,掌握流动人口增长的总趋势;其次,选定不同长度的人口序列以建立多个等维递补灰色模型,检验并分析各种预测结果的合理性与不足;最终确定北京市流动人口增长预测的高、中、低方案。实践表明,灰色等维递补预测法对“小样本”、“贫信息”的人口预测是切实可行的。     

人类活动影响下营养盐向河口/近海的输出和模型研究

人类活动（人口增长和经济发展所带来的对粮食和能源的需求和消费等）已经大大改变了地表圈层（土壤圈-水圈-大气圈）营养盐（主要是氮、磷、硅）的生物地球化学循环,结果是许多陆地、淡水和海洋生态系统受到显著影响并导致了严重的环境质量退化。本文对2002²005年由联合国教科文组织（UNESCO）和美国国家自然科学基金委员会资助的“全球营养盐输出和模型”（Global NEWS）项目进行了总结。     

面向实体的空间数据模型

空间数据模型是地理信息系统(GIS) 研究的基础内容.为适应新一代大型GIS的数据量大, 拓扑关系复杂, 数据种类多等特点, 提出了一种新型GIS数据模型---面向地理实体的空间数据模型.它以实体为基本表达对象, 具有较强的空间和语义表现力, 支持语义表达和转换, 便于实现等优点.从空间数据管理, 概念数据模型等方面详细地介绍了该种数据模型.实践表明, 采用该数据模型, 系统对真实世界的实体表现力将大大增强, 数据组织更加合理.      

城市三维地质调查数据库

将城市地质调查中的基础地理、工程地质、地球物理、地球化学等数据综合起来进行一体化管理, 对城市规划、建设、环境综合评价、公众信息发布具有重要的意义.在通过分析城市地质调查数据的广泛性、多样性、多维性等特点的基础上, 对数据进行了分类整理, 提出了适合城市三维地质信息管理与服务系统建设的基于勘测点的数据结构模型, 这种结构具有简单、易维护、可以满足多层次查询需求等特点.在此基础上结合城市三维地质应用设计了C/S (Client/Server) 和B/S (Browse/Server) 混合的数据库体系结构, 达到了应用的要求.      

区域土壤侵蚀定量研究的国内外进展

由于水土保持宏观决策的需要、土壤侵蚀学科自身的进步和全球变化研究的促进,过去的10多年来,国内外研究者对区域尺度土壤侵蚀研究给予了高度重视。已经开展的主要研究包括:全球和区域（包括国家尺度）土壤侵蚀调查、区域土壤侵蚀过程和尺度效应、区域土壤侵蚀因子和区域土壤侵蚀模型等。将区域土壤侵蚀作为现代陆地地表过程的一部分,充分考虑全球变化的影响,集成土壤侵蚀研究成果与遥感和GIS技术,开发分布式区域土壤侵蚀模型,成为区域土壤侵蚀定量评价研究的基本趋势。在对国内外区域土壤侵蚀定量评价研究评述的基础上,提出我国近期在区域土壤侵蚀方面研究的重点问题为：区域土壤侵蚀过程及其尺度效应的量化描述、区域土壤侵蚀模型开发、区域土壤侵蚀动态模拟与趋势预测、区域土壤侵蚀与全球变化关系研究和区域土壤侵蚀数据处理与管理方法。     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Wind-driven, double-gyre, ocean circulation in a reduced-gravity, 2.5-layer, lattice boltzmann model

A coupled lattice Boltzmann （LB） model with second-order accuracy is applied to the reduced-gravity, shallow water, 2.5-layer model for wind-driven double-gyre ocean circulation. By introducing the secondorder integral approximation for the collision operator, the model becomes fully explicit. The Coriolis force and other external forces are included in the model with second-order accuracy, which is consistent with the discretization accuracy of the LB equation. The feature of the multiple equilibria solutions is found in the numerical experiments under different Reynolds numbers based on this LB scheme. With the Reynolds number increasing from 3000 to 4000, the solution of this model is destabilized from the anti-symmetric double-gyre solution to the subtropic gyre solution and then to the subpolar gyre solution. The transitions between these equilibria states are also found in some parameter ranges. The time-dependent variability of the circulation based on this LB simulation is also discussed for varying viscosity regimes. The flow of this model exhibits oscillations with different timescales varying from subannual to interannual. The corresponding statistical oscillation modes are obtained by spectral analysis. By analyzing the spatiotemporal structures of these modes, it is found that the subannual oscillation with a 9-month period originates from the barotropic Rossby basin mode, and the interarmual oscillations with periods ranging from 1.5 years to 4.6 years originate from the recirculation gyre modes, which include the barotropic and the baroclinic recirculation gyre modes.     

Orthorectification of IKONOS and impact of different resolution DEM

IKONOS image has been wildly used in city planning, precision agriculture and emergence response. However, the accuracy of IKONOS Geo product is limited due to distortion caused by terrain relief. Orthorectification was performed to remove the distortion and the impact of different DEM on orthorectification were evaluated. 38 ground control points （GCPs） and 25 independent check points （ICPs） were collected. DEMs were generated from 1 ： 10 000 and 1 ： 50 000 topographic maps. Results show that RMS error at the check points is 1. 554 0 m using DEM generated from 1 ： 10 000 topographic map, which can meet the accuracy requirement of IKONOS Precision product （1.9 m RMSE）. While RMS error is 2. 572 4 m using DEM generated from 1 ： 50 000 topographic map.