一种数据网格的元数据分类管理机制研究

网格环境下数据发现和访问的关键问题是应用元数据管理和拷贝元数据管理，针对这一问题，提出了一种元数据分类管理机制。该机制定义了数据模型和应用元数据模式，采用了分类存储的方式对不同的元数据进行管理，并设计了一个基于向导的查询智能体。     

基于OpenGL的多层三维空间实体叠加建模

多层三维空间实体普遍存在于现实生活中。借助于目前国际最广泛应用的三维图形接口OpenGL将其真实地显示出来，具有重要的现实意义。其中多层三维空间实体建模技术是显示过程的关键步鼻。在实现单层空间实体三维建模的基础上，利用OpenGL实现了多层三维空间实体叠加建模，叠加实现的过程及其属性数据的表示方法。     

GIS空间过程建模系统初探

阐述了GIS空间过程和空间过程建模的概念，提出了空间过程建模系统的概念框架，分析了其功能需求和主要的支撑技术。     

地理空间元数据和基于网络的数据分发技术

首先介绍了元数据、地理空间元数据的概念，探讨了在互联网时代研究地理空间元数据的迫切性和必要性；其次，论述了构建基于网络的数据分发方案的可行性，并就其中的关键技术实现做了深入的探讨；最后，提出了一个以地理空间元数据标准为依据，以Internet为依托的数据分发站点的整体设计方案以及实现这项技术还应考虑的问题。     

Reservoir Diagenesis Sequence and Framework in Intracontinent Rift Basin,East China

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自适应空间信息移动服务

空间信息移动服务系统是空间信息系统继由传统PC计算环境向有线Web分布式计算环境扩展后，向移动计算环境的新发展。文中在分析移动计算环境概念和特点的基础上，讨论了空间信息移动服务的若干特点及关键技术，研究了自适应空间信息移动服务方案。     

基于受限汉语的GIS路径重建研究

主要研究基于自然语言 (汉语 )的GIS路径重建问题 ,通过分析带有路径表述信息的汉语文本 ,建立了汉语的NLRP句法模型 ,它是由带有空间语义的动作以及作为动作对象的地理要素构成的集合。考虑到自然语言理解实现的需求 ,论文基于NLRP句法模型定义了受限汉语的NLRP文法 ,在此基础上 ,描述了路径重建算法PRA ,并探讨了算法实现中由于空间认知原因带来的不确定性问题以及其解决方案。最后 ,基于该算法进行了相关实例研究 ,从而验证了该算法的正确性     

Identifying geospatial services across heterogeneous taxonomies

Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach.     

Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques

Tropical cyclones and their devastating impacts are of great concern to coastal communities globally. An appropriate approach integrating climate change scenarios at local scales is essential for producing detailed risk models to support cyclone mitigation measures. This study developed a simple cyclone risk-modelling approach under present and future climate change scenarios using geospatial techniques at local scales, and tested using a case study in Sarankhola Upazila from coastal Bangladesh. Linear storm-surge models were developed up to 100-year return periods. A local sea level rise scenario of 0.34?m for the year 2050 was integrated with surge models to assess the climate change impact. The resultant storm-surge models were used in the risk-modelling procedures. The developed risk models successfully identified the spatial extent and levels of risk that match with actual extent and levels within an acceptable limit of deviation. The result showed that cyclone risk areas increased with the increase of return period. The study also revealed that climate change scenario intensified the cyclone risk area by 5–10% in every return period. The findings indicate this approach has the potential to model cyclone risk in other similar coastal environments for developing mitigation plans and strategies.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.