Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light

Satellite\|derived nighttime light (NTL)data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity.This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables.The recently composited cloud\|free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS)provides spatially detailed observations of human settlements.We quantitatively estimated socioeconomic development inequalities across 30 provinces andmunicipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP)and population census data.We quantitatively investigated relations between NTL,GDP,and population using a linear regression model.Our results suggest that NTL have significant positive correlations with GDP and population at different levels.Several inequality coefficients were derived from VIIRS data and statistical data at multiple spatial scales.NTL\|derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels.Our study of provinces and municipalities revealed that county\|level inequality was more significant than city\|level.The results of population\|weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with eastern regions.Our findings suggest that given the timely and spatially explicit advantages of VIIRS,NTL data are capable of providing comprehensive information regarding inequality at multiple levels,which is not possible through the use of traditional statistical sources.     

中国移动在车联网运营中发展策略探析

本文介绍了车联网概念及原理,详细分析车联网为运营商带来的机遇和挑战,结合中国移动发展实际,对中国移动在车联网行业发展策略进行探析。     

基于工程管理的南海地质数据库

从南海地质数据资源、工程实例出发,结合高级语言设计和开发了基于工程管理的南海地质信息数据库系统.该系统设计实现了南海地质信息数据共享、数据融合、数据分析,简单快捷的结合到工程实例中,并应用ADO、DAO、OLE等计算机技术实现的数据资源快速导入、导出便于工程应用的目的.对工程中土的性质做详细的介绍,包括随深度的下降有效重度、抗剪强度、单位表面摩阻力、单位桩端阻力的变化情况.可以按层号、深度、土体性质等不同的条件进行查找.汇总功能可以绘制曲线,比较同一工程土体参数和不同工程之间的参数等,对工程设计和施工进行指导.     

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km²) to map four stem volume classes (0-20, 20-50, 50-80, and > 80 m³/ha).     

Simulating multiple class urban land-use/cover changes by RBFN-based CA model

Land use systems are complex adaptive systems, and they are characterized by emergence, nonlinearity, feedbacks, self organization, path dependence, adaptation, and multiple-scale characteristics. Land use/cover change has been recognized as one of the major drivers of global environmental change. This paper presents a coupled Cellular Automata (CA) and Radial Basis Function Neural (RBFN) Network model, which combines Geographic Information Systems (GIS) to contribute to the understanding of the complex land use/cover change process. In this model, GIS analysis is used to generate spatial drivers of land use/cover changes, and RBFN is trained to extract model parameters. Through the RBFN-CA model, the conversion probabilities of each cell from its initial land use state to the target type can be generated automatically. Future land use/cover scenarios are projected by using generated parameters in the model training process. This RBFN-CA model is tested based on the comparison of model output and the real data. A BPN-CA model is also built and compared with the RBFN-CA model by using a variety of calibration metrics, including confusion matrix, figure of merit, and landscape metrics. Both the location and landscape metrics based assessment for model simulation indicate that the RBFN-CA model performs better than the BPN-CA model for simulating land use changes in the study area. Therefore the RBFN-CA model is capable of simulating multiple classes of land use/cover changes and can be used as a useful communication environment for stakeholders involved in land use decision-making.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

Development and application of a simulation model for changes in land-use patterns under drought scenarios

In arid and semiarid areas of northern China, one of the most vulnerable regional environments, water resources are a key constraint on socioeconomic development. We constructed a simulation model for land-use patterns under a drought transition (i.e., the increased frequency and duration of drought since the late 1970s in the Yongding River Basin study area) to account for the complexity of both the driving factors behind land-use change and the micro-level changes in land-use patterns. This model was a combination of the “top-down” system dynamics model, the “bottom-up” cellular automaton model, and the artificial neural network model. In this model, we considered the socioeconomic development and water resource restrictions, as well as the balance between the land-use requirements and the land supply. We then verified the model through a case study. The results demonstrated the value of constructing a simulation model driven by water resource constraints under the influence of drought. The spatial distribution of land uses in future scenarios will help support decision-making for sustainable regional development.     

“计白当黑”对现代视觉设计的启示

随着现代艺术设计的迅猛发展,现代视觉设计的文化交流更加频繁和深入,"计白当黑"这种表现艺术的空灵精神在现代视觉设计中体现出了重要的设计启示和研究价值。本文以此为切入点,抛砖引玉,旨在探讨中国传统文化的精髓之一"计白当黑"在现代视觉设计中的发展潜力及新气象。     

双肌臀大约克猪选育与杂交利用的初步研究

描述了滑刃线虫属（Aphelenchoides Fischer,1894)的2个国内新记录种：游泳滑刃线虫(Aphelenchoides hechaleos)和内卷滑刃线虫(A.involutus)。游泳滑刃线虫采自辽宁省盖州市熊岳镇莴苣(Lactuca sativa)的根际土壤中,它与近似种拟游泳滑刃线虫(A.paranechaleos)的主要区别是雌虫尾较长,尾的形状是腹面凸（而拟游泳滑刃线虫的尾较短,尾的形状是背面凸,腹面凹）,雄虫交合刺背杆的弯曲程度较小。内卷滑刃线虫采自辽宁省凌海市苍耳(Xanthium sibiricum)及山东济南大花金鸡菊(Coreopsis grandiflora)的根际土壤中,它与近似种A.jacobi的主要区别是雌虫尾向腹面弯曲程度较大,尾后部呈钩状,尾较长。     

深海天然气及其水合物开发模式与钻采技术探讨

中国南海的石油天然气资源十分丰富,但其大多数都埋藏于深水区,油气勘探开发工作面临着许多难题和挑战。历经多年的探索与实践,我国海洋油气钻探工程已经实现了从浅水（水深300 m以内）到超深水（水深超过1 500 m）的跨越,并在南海发现了丰富的天然气与海域天然气水合物（以下简称水合物）资源,亟待进行安全高效开发,因而对相应的天然气及其水合物高效开发模式与技术支撑体系提出了迫切的需求。为此,针对深海天然气及其水合物安全高效开发的重大课题,提出了适用于常规天然气的“水平井或复杂结构井浮式钻完井+水下钻采系统+浮式生产、集输与浮式液化天然气生产储卸装置（FLNG）处理系统+船运外输”的开发模式及其技术支撑体系,以及适用于非常规天然气——海域天然气水合物的“水平井或复杂结构井浮式钻完井+水合物原位分解开采+水下或浮式生产与集输处理系统+管道或船运外输”的开发模式及其技术支撑体系;论述了大位移井、“U”形井等先进井型的开发模式及其适用的海洋地质环境,并给出了“U”形井的连通控制模型;此外,还介绍了与深水钻井力学和设计控制技术相关的研究进展。结论认为,建立先进适用的工程模式及其技术支撑体系,寻求实现“地质—工程—市场”一体化的解决方案,同时加强相关的信息化与智能化建设,是深海天然气及其水合物安全高效开发的关键之所在。