一种基于位置的移动服务系统的设计与实现

基于位置的移动服务需要设计高效、稳定和可扩展的体系结构与开发方式，提出了一种基于Web服务的LBS体系结构（WS-LBS），并设计了WS-LBS系统框架，实现了空间信息服务的构建与发布机制，扩展了终端用户，实现了一个WS-LBS原型系统。     

DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.     

DCAD: a dual clustering algorithm for distributed spatial databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.     

1951—2010年中国土壤温度时空变化特征及其影响因素

土壤温度状况对于研究气候变迁、地球物质能量循环以及土壤性质演变具有重要意义,但目前对国家尺度上土壤温度状况的长期序列和空间变化缺少研究。因此,本文基于土壤温度内插法和地理加权回归（GWR）模型,使用1951—2010年中国880个气象站点的观测数据,研究了中国土壤温度状况时空变化特征及其影响因素。结果表明：① 中国60年来土壤温度变化整体趋势为东北地区升温,西南地区少部分地区降温;② 中国土壤温度状况可划分为冷性土壤温度状况（东北地区、青藏高原地区和内蒙古东部）、温性土壤温度状况（新疆南部、内蒙古和山西南部以及山东）和热性土壤温度状况（华中、华东、华南以及西南的云南、贵州和四川）;③ 经纬度和气温与土壤温度具有良好的响应关系,其中气温是最重要的影响因素;④ 中国60年来整体呈现温性土壤向北迁移（约46.5 km）、冷性土壤向南迁移（约43.4 km）的趋势。研究结果可为地理学、土壤学等相关领域深入研究提供一定参考,并为土壤系统分类研究提供理论依据。