SARS in Singapore—challenges of a global health threat to local institutions

SARS (Severe Acute Respiratory Syndrome) has been declared by WHO (World Health Organisation) as a global health threat. Within a period of four to five months in 2003, the disease infected some 8,000 people in more than 25 countries and left 774 dead. The many studies that have been done on the spread of SARS in Asia as well as countries as far flung as Germany and Canada have focused on the global dimension of the infectious disease as well as the speed of its spread upon emergence in southern China and then Hong Kong. Less attention has been paid to its spatial distribution at the national and local scales. This discussion focuses on the spread of SARS at the national and local spatial scales. In the process, the study presents the management of a hazard, in this case, an emerging infectious disease by national health care institutions such as the hospitals that ultimately proved to have been wholly unprepared for coping with at least the health aspects of the outcome of a globalised national agenda for growth and economic progress.     

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.     

World Wide Web(WWW)上矢量地图数据的多分辨率传输算法

提出一种用于网络环境下多分辨率传输矢量地图数据的算法,此算法采用"点删除"技术用于多分辨率描述原始的矢量地图数据,而且能够避免空间对象的自相交和维护拓扑关系的一致性.以此算法为基础开发了一个Client/Server结构的实验软件,在实验结果的基础上评估了该算法对提高网络环境下矢量地图数据传输效率的效果.实验结果表明该算法用于矢量地图数据的多分辨率快速传输是有效的.     

GPS基线向量解算相关性研究

通过GPS载波相位测量中i个测站同步观测s颗卫星m个观测历元所在弧段内双差观测值来进行基线向量解算,分别按单基线向量解算和多基线向量解算求得协方差矩阵,并比较两种方案的优缺点。     

栅格数据矢量化及其相关问题的解决方法

矢量结构和栅格结构是GIS的两种主要数据结构形式,而栅格数据不能满足GIS中进行空间分析的要求,因此栅格数据的矢量化在GIS中具有十分重要的意义。本文在总结前人算法的基础上,以拓扑关系的原理为指导,提出了改进的栅格结构数据矢量化方法,即根据4个相邻栅格的异同关系来判定交汇点是坐标点还是结点,并且在提取结点和坐标点过程中,同时提取水平和垂直的所有线段信息,以便快速、有效地实现栅格数据的矢量化。此外,还采用动态打断和粗网格等方法,解决了在栅格数据矢量化过程中,经常出现的诸如多边形自交、孤岛等问题。作者用VisualC⁺⁺语言实现了本文的算法,与ENVI矢量化结果相比,该方法的矢量化结果具有简单方便、编辑工作量小、实用性强等显著优点。因此,该改进方法更有利于遥感图像的信息提取及其与GIS技术的集成。     

Recognizing spatial distribution patterns of grassland insects: neural network approaches

The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network. It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.     

位涡和Q矢量诊断在毛乌素沙地沙尘天气预报中的应用

利用常规的天气图、卫星云图、湿位涡和Q矢量等,对2007年4月30日和2007年5月2日发生在毛乌素沙地的两次沙尘天气进行了综合分析\.结果表明:对流层低层MPV1＜0中尺度对流不稳定区的生成,为干对流的发生提供了不稳定能量条件;来自上游对流层中高层的干侵入和扰动干侵入在沙尘天气发生过程中起着重要作用;对流层低层由Q矢量辐散、辐合激发生成的次级环流为沙尘天气发生、发展和维持提供了动力机制。在对毛乌素沙地中β尺度沙尘暴的预报预警过程中,同时应考虑下沉对流有效位能的作用。     

昆明准静止锋的准地转Q矢量分析

应用Ｑ矢量理论,对１９９５年２月一次与云南寒潮天气过程有关的昆明准静止锋进行了诊断分析。结果表明：在８５０ｈＰａ上,云南东部地区存在明显的的非地转风辐合场,从而引起昆明准静止锋锋生;当７００ｈＰａ上Ｑ矢量锋生函数值增大,说明儿后冷空气加强,昆明准静止锋容易南下;低层Ｑ矢量辐合带与昆明准静止锋雨区有较好的对应关系。     

Q矢量及其在天气诊断分析中应用研究的进展

对Ｑ矢量的理论研究的动态及其主要应用结果作了综合评述,并对该领域的研究前景作了一定的展望。