喀斯特地区生态环境空间数据共享研究

我国西南喀斯特山区是典型的生态环境脆弱区,与其相关的生态环境数据也具有一定的地域特征,因此,在共享数据的组织过程中应当根据其特点进行有效的组织和管理。现从为用户提供共享服务的角度出发,阐述了一种基于数据共享服务中心的空间数据共享组织模式,论述了数据共享中心的服务功能及其特点,实现了喀斯特地区生态环境空间数据的高效组织和安全共享。     

空间co-location模式挖掘算法介绍及应用

当前挖掘空间co-location模式所遇到的困难在于,空间对象的实例分布在连续的空间中并拥有复杂的空间关系,大部分的计算时间需要用来生成co-location模式的表实例.分析了co-location模式挖掘的实质,以及近年来提出的co-location模式挖掘的全连接算法和无连接算法,并对这两种算法在性能上加以比较.在此基础上,结合三江并流国家基金项目,用这两种算法挖掘出了共生植被及其分布情况,为生物学家的科学研究提供了有利的帮助.     

新疆阜康土壤微量元素的空间变异分析

运用地统计方法,研究了新疆阜康地区土壤表层微量元素的空间异质性特征.结果表明:4种微量元素的空间异质性主要是结构性因子和随机因子共同作用的结果.土壤微量元素的变异函数均可很好地拟合成高斯模型,块金值和基台值之比均为25%～75%,为中等强度的空间相关性.研究区土壤微量元素质量分数空间变异具有各向异性特征,表现为二阶趋势效应.Moran's I系数分析表明4种微量元素均呈现一定强度的空间自相关关系.土壤微量元素质量分数的等值线图分析表明,不论在什么方向上,土壤微量元素质量分数呈现一定规律分布.     

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

近40年中国北方农牧交错带气候时空分异特征

目的探讨中国北方农牧交错带气候时空分异特征。方法采用定量方法,分析近40年气温、降水的年与季节的变化特征及气候变化趋势;借助于GIS空间分析技术探讨研究区气候类型与气候时空分异特征。结果通过对气温、降水及气候类型的分析,得出研究区气候各因子及气候的空间分布规律。结论①中国北方农牧交错带包括7种气候类型,东、中、西部分别以中温带森林草原气候、暖温带大陆性森林草原气候和温带草原气候为主;②中国北方农牧交错带气温近40年有升高的趋势,冬季和夏季气温增加,春秋两季气温变化不大;③中国北方农牧交错带降水有减少的趋势,秋季降水减少明显,冬季降水基本不变,夏季和春季降水波动较大;④研究区气候变化在空间上可以划分为三大区域和4种类型。     

空间矢量数据渐进传输研究

文章介绍了使用渐进传输的原因,并具体分析了渐进传输过程、采用渐进传输对网络的影响、空间矢量数据渐进传输的数据结构及客户端与服务器数据交互过程。     

一种基于多属性的空间例外挖掘改进算法

空间例外是指与其邻域内其它数据表现不一致或者是偏离观测值以至使人们认为是由不同体制产生的观测点.传统的例外挖掘是根据一个非空间属性值进行例外判断,这种方法容易引起判断失误.在对多个属性进行考虑的基础上,提出了一种基于多属性的空间例外挖掘算法,并与属性加权算法在正确性和有效性方面进行了比较分析.实验证明算法可以有效地发现例外数据.     

龙眼暖春"冲梢"气象指标及其发生规律研究

利用广西玉林农业气象试验站1987～1992年及2001-2002年的龙眼“冲梢”观测记录资料及同期的气温、风向资料，确定龙眼暖春“冲梢”的气象指标，然后根据这些指标，选取广西龙眼主产区40个气象观测站点1961～2000年2～3月日平均气温、日最高气温以及南风天数资料，分析广西龙眼主产区春季暖害的时空分布特征，提出采用选择龙眼暖春“冲梢”少的区域发展龙眼；根据气候灵活应用调控技术，推行平衡施肥技术和药物控梢的方法来防抗龙眼暖春“冲梢”。     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

A prototype for spatial information services based on J2EE

J2EE (Java 2 Platform , Enterprise Edition) is a kind of architecture that simplifies the development ,design and management of multilevel enterprise solution. The spatial middlewares based on J2EE can easily resolve the problems which the traditional Geographic Information Systems (GIS) now have to face with, such as transplantability,stability, easy maintainability and many more. This paper first introduces some conceptions and principles about J2EE, spatial middleware. then proposes a concrete J2EE-based solution of spatial middleware, namely SISP AS, which develops on JBoss(an open sources application server of J2EE) and uses the SISP spatial database. SISP AS follows Open GIS specifications, and implements a subset of OGC services. it improves interoperability and commonality by using XML/GML for communication and data transmission, resolves the transmission efficiency problem of large spatial data by using raster pyramid and vector pyramid. Finally, the related work will be presented. Notably,the experiments on this prototype system can proves that it is available to manage,distribute and share large scale spatial data by using SISP AS.