Understanding governance and networks: EU-US interactions and the regulation of genetically modified organisms

EU-US (European Union-United States) interactions in relation to the regulation of genetically modified organisms (GMOs) have been examined in detail in recent years. To do this scholars have tended to focus on a small number of high profile processes, such as the formal complaint of the US to the World Trade Organisation regarding the regulation of GMOs in the Europe. It is important to analyse developments of this kind but overemphasis on them has also lead to a distorted view of the case and a failure to appreciate the significance of other, less visible, types of interaction. In this paper we focus instead on trying to understand the roles played by various EU-US transnational networks—The Transatlantic Business Dialogue, The Transatlantic Economic Partnership, The Transatlantic Consumer Dialogue, The Transatlantic Environmental Dialogue and the EU-US Consultative Forum on Biotechnology. These networks have been trying to shape the regulation of GMOs in both jurisdictions since the mid 1990s. By analysing them through a ‘governance lens’ we find that we can better understand EU-US interactions and the dynamics and influences around the regulation of GMOs. This gives us valuable insights into processes of contemporary governance.     

A surface network based method for studying urban hierarchies by night time light remote sensing data

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

A network theory approach for a better understanding of overland flow connectivity

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub‐) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub‐Mediterranean environment by using a combination of high‐resolution digital‐terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi‐natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.     

中国重点产业创新产出时空分异及影响因素

采用Mann-Kendall、Theil指数、空间马尔科夫链等方法,对1994-2014年中国十大重点产业创新产出时空演化进行分析,并对创新发展空间分异成因进行了探讨。研究表明：① 研究期内,中国重点产业创新产出分为两个时段,成果呈指数型增长;② 创新产出差异先增后减,东、中、西、东北板块间差异小于板块内部,创新产出发展呈现出传染扩散与等级扩散的双重特征;③ 邻域环境影响创新发展,创新产出水平发生类型转移的单元集中在东、中部地区,且活跃度不断提升;④ 经济社会与政策条件、高等教育基础条件对重点产业创新发展有明显的正向驱动作用,而工业化程度影响微弱,未来加强对高等教育基础与智力资本的投入可进一步增强重点产业创新发展竞争力。     

Remote sensing of water depths in shallow waters via artificial neural networks

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.     

顾及邻域居民地群组相似性的道路网匹配方法

现有多源同比例尺道路网匹配方法中,大多只利用道路自身特征进行匹配,而较少顾及道路周边要素对匹配过程的影响和约束,从而影响了道路网匹配效果的进一步提高,特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时,这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点,提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组,进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于:对存在位置或旋转偏差的道路数据匹配,以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配,实际上是增加了周边居民地群组对道路匹配过程的约束,更具鲁棒性。试验及对比分析表明,本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题,提高匹配的正确率。     

Relations between professional groups in the Atlantic and Mediterranean fishing enclaves of Andalusia (Spain): A personal networks approach with clustered graphs

This paper proposes a new network-based approach to analyse intergroup relations in fishing ports. The technique of clustered graphs is applied to the case of the Andalusian fishing ports to assess the balance between intra and inter-professional relationships. The patterns of sociability in Mediterranean and Atlantic fishing enclaves in the southern region of Spain were compared, examining their implications for participatory governance of marine resources. The personal networks of 53 fishermen, ship owners or skippers and key individuals of 18 Andalusian fisheries were analysed. The personal networks were compared in terms of fishing ground (Atlantic versus Mediterranean) and port type (by size and form of participation). The data of 45 individuals with whom each respondent usually interacts in the harbour was summarised in clustered graphs of intra-group and inter-group relationships between 8 professional roles in the harbour. Results show primarily that personal networks of Mediterranean ports are overall denser, in comparison with those of the Atlantic, which are more centralised and have a higher average betweenness. Secondly, in the Atlantic a clear difference of roles between ship owners and skippers is observed. A strong link between ship owners and the crew was found, and also between the ship owner and commercial roles in the Mediterranean. Small ports seem to be more apt for the artisanalisation of fisheries, as well as for the European Union’s Common Fisheries Policy.     

中国装备制造业产学研合作创新网络初探

产学研合作是区域创新的主要途径和重要来源.以中国装备制造产业为例,基于中国知识产权局1985-2012年间的合作发明专利数据,借助SPSS,UCINET,ArcGIS等定量分析工具,对中国装备制造产业合作网络的创新主体结构,空间结构及其演变,创新合作的空间尺度的影响因素进行了分析.研究发现,民营企业,高校在中国装备制造产业创新网络中的地位不断上升,数量不断增加,且已经成为重要的创新源泉;市域空间合作成为发达地区城市产学研创新合作最重要的空间单元,国家空间是欠发达地区城市产学研创新合作的主要空间载体;理工科高校等科技资源的空间集聚态势是导致创新网络层级特征的主要因子,科技资源富集的行政中心如直辖市,省会城市等发达城市成为最重要的资源集聚地,创新源泉和创新合作对象.