基于VisIt的全球科学数据并行可视化——以大气温度场为例

针对当前大规模全球科学数据可视化中存在的单机可视化数据量有限、从底层开发并行可视化系统难度大等问题,该文基于分布式环境和VisIt,提出了一种简便、开放而又有效的大规模全球科学数据可视化方法。介绍了VisIt的体系结构及运行机制,给出了自定义数据的并行可视化方法;并基于NCEP数据集及全球空间格网,在小规模集群环境下实现了小粒度适应性球体退化八叉树格网(SDOG)下的全球大气温度场的并行可视化。VisIt的并行可视化性能测试结果表明:通过增加计算节点,VisIt能有效摆脱传统单机可视化对数据量的限制,可实现大规模全球科学数据的并行可视化。     

基于作息空间思想的人口统计数据空间化方法

探讨了基于作息空间思想的人口统计数据空间化方法。定义居住和工作的活动空间范围为作息空间,并结合土地利用类型数据和泰森多边形构建了作息空间,以作息空间人口密度作为人口空间分布的表达方式。对辽宁省2005年人口统计数据进行了实例分析,精度优于传统的面积权重内插法,结果的地图渲染能够表现受自然因素和社会经济因素影响的人口分布特征。该方法不涉及复杂的权重计算过程,也可以避免主观因素的干扰,简单易行,有较强的实用性。     

辽宁沿海经济带工业环境效率分析

环境效率是生态效率的重要组成部分。基于环境效率对于指导区域经济与环境协调发展的重要意义,以重工业基地辽宁沿海经济带为例作环境效率研究,应用数据包络分析(DEA)和随机前沿分析(SFA)测算辽宁沿海经济带2001~2009年工业环境效率和产出弹性。在此基础上利用Tobit模型构建多元线性回归,分析工业环境效率的关键影响因素。结果表明:2001~2009年辽宁沿海经济带工业环境效率有大幅提高,但与理想状况仍有一定差距。其中大连的工业经济增长主要依靠劳动力推动,对环境成本的依赖正在减小,丹东、营口和葫芦岛3市的工业增长需要牺牲更多的环境成本。重工业化、资本密集化和高新技术化不利于工业环境效率提高,经济发展水平的提高则极大地促进了工业环境效率的提高。所有制结构、生产规模、外商投资和环境管理力度等因素因其作用的两面性,对工业环境效率无有效影响。     

用NCEP／NCAR再分析辐射资料估算月平均地表反照率

本文利用１９７９年￣１９９５年１７年平均的ＮＣＥＰ／ＮＣＡＲ（Ｎａｔｉｏｎａｌ Ｃｅｎｔｅｒ ｆｏｒ Ｅｎｖｉｒｏｎｍｅｎ－ｔａｌ Ｐｒｅｄｉｃｔｉｏｎ／Ｎａｔｉｏｎａｌ Ｃｅｎｔｅｒ ｆｏｒ Ａｔｍｏｓｐｈｅｒｉｃ Ｒｅｓｅａｒｃｈ,美国国家环境预报中心／美国国家大气研究中心）再分析辐射资料估算了全球月平均地表反照率．从所得结果的时空分布来看,用ＮＣＥＰ／ＮＣＡＲ辐射资料得到的全球地表反照率基本     

The knowledge–space dynamic in the UK bioeconomy

The loss of manufacturing employment to lower cost economies has meant that countries like the UK have sought to promote innovation in areas such as biotechnology. The emergence of the 'bioeconomy', however, has been highly uneven, with concentrations of activity in certain countries and particular regions in those countries. In the UK, for example, there are four major concentrations of the bioeconomy. Each of these concentrations exhibit distinct patterns of knowledge and spatial inputs into the innovation process, meaning that it is important to consider the knowledge–space dynamic in and of each region.     

基于对象关系数据库的矢栅一体化查询与操作研究

在分析矢量栅格数据特点的基础上,提出一系列矢栅一体化查询函数,基于PostgreSQL/PostGIS数据库,实现了支持栅格数据存储与组织的扩展数据类型,并对栅格数据类型与PostGIS矢量数据一体化查询函数进行了扩展实现。最后,通过实例验证了矢栅一体化查询函数的表达能力和解决实际问题的应用前景。     

Assessing the applicability of OpenStreetMap data to assist the validation of land use/land cover maps

The validation of land use/land cover (LULC) maps is usually performed using a reference database consisting of a sample of points or regions to which the ‘real’ class is assigned. This assignment is usually performed by specialists using photointerpretation (PI) of high-resolution imagery and/or field visits, which are time consuming and expensive processes. The aim of this article is to assess if the data available in the collaborative project OpenStreetMap (OSM) may be used as a source of data to assist the creation of these reference databases, reducing the time spent and costs associated with their generation. For this aim, two case studies were used, where the validation of the Global Monitoring for Environment and Security Urban Atlas (UA) was performed. The used methodology requires the harmonization of the data available in OSM with the UA nomenclature, and the subsequent creation of a LULC map from the OSM data. This map was then compared to UA to assess the similarity of the regions mapped in both. To test the usefulness of OSM data to assess the accuracy of UA, a sample of points was created and two reference databases generated, one assigning the data extracted automatically from OSM to the points where these data were available, and PI for the remaining points, and the other using only PI. The accuracy assessment of UA for the two case studies was then made building confusion matrixes and computing accuracy indicators. The results showed that for the two study areas, only low percentages of points had to be photo interpreted in the first reference database (respectively, 12% and 2% for the two study areas), decreasing the work load considerably. The results obtained with both reference databases are comparable for level 1 classes. For level 2 classes, worse results were obtained for some classes, showing that the OSM data used are not enough to create reliable reference data.     

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.     

Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.