The Beach Crowding Index: A Tool for Assessing Social Carrying Capacity of Vulnerable Beaches

The aim of this article is to introduce the Beach Crowding Index (BCI), a procedure to assess the social carrying capacity of vulnerable beaches. The study uses the people at one time (PAOT) approach and data gathered weekly throughout the bathing season regarding the number of beachgoers in 100 m² cells of the beach to assess how many beachgoers it can comfortably hold. The procedure is based on fieldwork, interviews with beachgoers, and geographic information system (GIS) analysis and has been tested on four beaches in protected areas on the Spanish Mediterranean coast. On a scale from 0 to 4, minimum scores throughout the bathing season are 0.7 and maximum 3.7, although results showed wide variation between the beaches, the section of the beach, and the time of day. This study suggests that determining the location of beachgoers and collecting a long-term series of data is fundamental to assessing social carrying capacity and that the BCI procedure can be used for a large number of applications.     

Using Web Maps to Analyze the Construction of Global Scale Cognitive Maps

Game-based Web sites and applications are changing the ways in which students learn the world map. In this study, a Web map-based digital learning tool was used as a study aid for a university-level geography course in order to examine the way in which global scale cognitive maps are constructed. A network analysis revealed that clicks were negatively correlated with familiarity, but were positively correlated with area. More significantly, well-known countries did not act as central nodes within network-based mental models, but served a more complex mediating role in structuring the space between subregions.     

地理信息技术支撑下的南海岛礁资源环境研究进展与展望

面积辽阔的南海是中国未来重要的能源接续区与资源基地,也是涉及国家海洋权益最为集中的区域。本文针对地理信息技术支撑下的南海岛礁研究现状,从南海岛礁现有基础数据成果、遥感手段岛礁基础地理信息提取方法及提取成果所做的岛礁分析与评价3方面,系统地回顾了国内外涉及南海岛礁资源环境研究的相关进展,评述了现有研究技术方法存在的不足,并对未来的研究进行了展望,在此基础上,提出了3个主要方向作为未来研究的重点：①促进遥感数据获取多源化,构建海量多源、多尺度南海岛礁遥感数据仓库;②加强技术协同创新,结合地理信息技术的发展,提升岛礁信息提取与监测的准确性与可靠性;③提升遥感分析智能化,研究并建立综合分析与情势推演平台及战略决策辅助支撑系统,以期为南海岛礁的进一步深入研究提供参考。     

Big Data Resource Planning for Food Safety: a Preliminary Exploration of the “Environment,Food and Health” Information Chain

Environmental pollution, food safety and health are closely linked. A key challenge in addressing the problem of food safety and protecting public health is building an integrated knowledge base to inform policy and strengthen governance. This requires breaking down the trans-departmental information barrier across the environment, food and health domains to ensure the effective flow of data and the efficient utilization of resources, and facilitate the collaborative governance of food safety. Achieving this will be crucial for the development of health and medical care in China in the era of big data. Currently, the information resources commanded by various departments are incomplete and fragmented. Data resources are also organized in vertical silos and there is a lack of data sharing within and across policy streams. To provide the basis for more effective integrated collection and analysis of data in future, this study summarizes the information resources of various departments whose work relates to interactions between environment, food and health, and presents measures to strengthen top-down design, and establish unified data standards and a big data sharing platform. It also points to the need for increased training of data analysts with interdisciplinary expertise.     

基于KL-ISOMAP的高光谱图像彩色可视化

标志点等距映射（L-ISOMAP）作为一种降维方法,在高光谱图像可视化中极具潜力.针对L-ISOMAP算法标志点代表性不足以及计算代价较高的问题,提出了基于K-medoids聚类算法的改进型L-ISOMAP算法（KL-ISOMAP）,进而形成可视化方法.该可视化方法由以下几部分组成：1）通过改进型K-medoids算法选择标志点;2）根据相似度剔除相似像元;3）实现剩余像元的非线性降维;4）完成降维结果的可视化.实验结果表明,KL-ISOMAP算法一方面有效地提高了标志点的内在结构代表性,进而取得了更好的可视化效果;另一方面可通过对相似阈值的设置,加快算法的运算速度.由此形成的可视化方法有着较好的视觉效果、距离保持特性以及像元可分性.     

基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究

卫星资料凭着卫星遥感的全球性、连续性和高频次观测等优势,成为一种重要的非常规资料源,但卫星观测仍然存在各种各样的观测误差,其中包含由于观测偶然性所造成的统计学上的随机误差及仪器本身和辐射传输模式等造成的系统性偏差,这些误差在很大程度上影响了卫星资料的质量。文中提出了一种能有效订正卫星观测资料系统性偏差的梯度信息同化算法,该方法用一个梯度算子进行模式变量与观测变量的梯度变换,从而达到订正系统性偏差的目的。本文利用WRF（Weather Research Forecast）模式及其同化模式WRFDA（WRF Data Assimilation system）,以及AIRS（Atmospheric Infrared Sounder）资料,对台风“圆规”进行了实际的数值模拟和同化试验,数值结果表明,梯度信息同化方法能明显改善台风路径的模拟,在处理可信度较低的资料时仍然适用。另外,通过同化诊断分析,发现卫星资料的系统性偏差对于台风数值模拟有较大影响,而文中提出的梯度信息同化方法能较好的解决此类问题。     

Python在FY 2G定量业务产品自动可视化中的应用

利用Python语言实现了每日对CMACast下发的最新风云二号G星(FY-2G)云导风(AMV)和射出长波辐射(OLR)两个定量业务产品的实时自动解码和可视化出图,并与国家卫星气象中心下发的卫星天气应用平台(SWAP)的显示进行了对比。结果表明:两者显示一致。在显示所有AMV文件风矢量数据的前提下,本文可视化产品较目前版本SWAP的显示更为疏密均匀,没有出现重叠现象;Python出图自带的高洛德(Gouraud)明暗处理渲染方式则在对OLR格点产品可视化显示中提供了一种更为平滑,细腻的方法。利用本研究方法对风云二号卫星定量产品进行可视化具备自动化和视觉美观的特点,值得在基层气象部门业务中推广。     

The geometry of space-time prisms with uncertain anchors

Space-time prisms envelop all spatio-temporal locations that moving objects may have visited between two of their known spatio-temporal locations, given a bound on their travel speed. In this context, the known locations are often the result of observations or measurements, and they are called ‘anchor points’. The classic space-time prism, in isotropic two-dimensional space, as well as in transportation networks, assumes that the measurements of these anchor points are exact. Whereas, in many applications, we can assume that time can be measured fairly precisely, this assumption is unrealistic for the spatial components of measured locations (we think of Global Positioning System (GPS) errors, for instance). In this paper, we extend the classical prism from anchor points to circular ‘anchor regions’ that capture the uncertainty or error on their measurement. We define the notion of a space-time prism with uncertain anchor points, called uncertain prism, for short. We study the geometry of uncertain prisms in an arbitrary metric space to make this concept as widely applicable as possible. We also focus on the rims of uncertain space-time prisms, which demarcate the area that a moving object can have visited between two anchor regions (given some local speed limitations).     

PolarGlobe: A web-wide virtual globe system for visualizing multidimensional,time-varying,big climate data

The increasing research interest in global climate change and the rise of the public awareness have generated a significant demand for new tools to support effective visualization of big climate data in a cyber environment such that anyone from any location with an Internet connection and a web browser can easily view and comprehend the data. In response to the demand, this paper introduces a new web-based platform for visualizing multidimensional, time-varying climate data on a virtual globe. The web-based platform is built upon a virtual globe system Cesium, which is open-source, highly extendable and capable of being easily integrated into a web environment. The emerging WebGL technique is adapted to support interactive rendering of 3D graphics with hardware graphics acceleration. To address the challenges of transmitting and visualizing voluminous, complex climate data over the Internet to support real-time visualization, we develop a stream encoding and transmission strategy based on video-compression techniques. This strategy allows dynamic provision of scientific data in different precisions to balance the needs for scientific analysis and visualization cost. Approaches to represent, encode and decode processed data are also introduced in detail to show the operational workflow. Finally, we conduct several experiments to demonstrate the performance of the proposed strategy under different network conditions. A prototype, PolarGlobe, has been developed to visualize climate data in the Arctic regions from multiple angles.     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.