国外儿童地理学研究进展与启示

20世纪70至80年代,在地理学“空间转向”的背景下,国外儿童地理学研究兴起。2003年Children’s Geographies诞生,成为儿童地理学的旗帜性刊物,标志着儿童地理学的发展进入新阶段。借助可视化工具,系统梳理国外儿童地理学研究的核心主题、研究热点以及研究方法。研究发现：（1）国外儿童地理学的科研人员、科研机构以及研究所属区域都呈现高度集中性,欧美国家占据主导地位;（2）核心研究主题包括移民与流动身份、伦理与地区差异、教育与青年志向、童年与多元空间以及游戏与情感体验等;（3）亚洲儿童地理研究成为近期研究热点,聚焦于“跨国儿童移民”以及“亚洲青年教育及就业问题”两部分;（4）研究方法历经探索期、发展期、繁盛期以及信息技术广泛应用时期的演进,技术手段随时代发展不断更新。     

Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

基于引文网络的Cyberspace研究进展

以Web of Science数据库收录的有关研究Cyberspace的大量文献为数据源,采用文献计量学的分析方法,借助信息可视化软件CiteSpace Ⅱ对所采集的数据进行统计分析;获取1993 ～2013年检索数据,通过可视化图谱的方法挖掘出该领域的研究重点和学科分布;从地理学角度,揭示了该领域的研究热点和方向.     

知识经济条件下的高校图书馆文献信息服务

知识经济时代文献信息资源是知识创新的源泉，信息服务业成为知识经济的先导产业。以高校高科技产业为例，说明了高校图书馆要立足于知识经济社会，只有拓宽文献信息服务的内容，同时为教学、科研服务，发展高科技产业。并对知识经济时代的高校图书馆文献信息服务提出了一些建议。     

知识经济时代图书馆人力资源的开发与管理

在知识经济条件下，如何提高图书馆人力资源开发与管理的质量，调动广大图书工作者的积极性发挥他们的创造力，将直接影响到图书馆的发展和服务能力。     

EXPLORING SPATIAL FAMILIARITY*

Spatial familiarity is a natural language concept that has no well-defined technical interpretation. We explore here some possible dimensions of the concept, provide subjective measurements of the dimensions, and evaluate their independence. The four dimensions examined are locational knowledge, visual recognition, name identification, and interaction frequency. We suggest that both cognitive and behavioral dimensions are embedded in the concept of spatial familiarity and must be part of the meaning of the concept and of any attempt to make it operational.     

区域地质调查文本中文命名实体识别

作为我国地质调查领域最重要的数据源之一,地质调查报告中蕴含着丰富的地学知识及地质体描述等关键信息,准确高质量地抽取地质命名实体为地学知识图谱构建、知识推理及知识演化提供基础。笔者等在阐述地质命名实体识别任务基础上,分析地质实体不仅包含大量专业术语,还存在实体嵌套、大量长实体等领域特性,进一步增加了地质命名实体识别难度。笔者等提出一种基于轻量级预训练模型(ALBERT)—双向长短时记忆网络(BiLSTM)—条件随机场(CRF)模型的地质命名实体识别方法。首先利用ALBERT对输入字符上下文特征进行建模,并采用BiLSTM对其进行进一步上下文特征表征,最后采用CRF实现标注序列预测。实验结果表明,在构建的地质命名实体识别数据集上,相比于主流的命名实体识别模型算法,本文所提出的方法具有更好的抽取性能,提出的命名实体识别模型能为领域实体识别提供借鉴,同时为地学领域实体关系抽取和地学知识图谱构建提供有力方法支撑。     

培养德识双馨的国家栋梁之才：《普通地质学》课程教学有感

《普通地质学》是引领新生进入地学殿堂、了解地球科学知识的一门专业启蒙课,也是新形势下地学通识教育中的一门基础理论课。其内容涉及地质学全部基础知识中的最必要部分,具有衔接和统领地学各门专业基础知识的特点。根据多年教学体会,笔者认为在精英教育与通才教育并举的新形势下,必须心系学生,与时俱进,转变教育理念,优化集课堂、实验、野外教学为一体的教学体系,推进教学内容的改革与建设,摈弃不适应时代发展与国家需求的教育模式,采用现代化的教学方法和教育技术;对学生开展基础理论传授、专业基础技能训练、创新科研培育的系统教育,拓宽学生的专业知识面,激发学生专业兴趣和创造性思维。在传授科学知识的同时,目标明确地开展思政教育,引导学生认清学问与道德的关系,建立德识双馨的价值取向,提高基础地质课程教学质量和学生的综合素质,促进教学改革,为国家培养五育兼备的优秀人才。     

石油地质勘探智能化信息支撑框架设计

石油勘探是高风险行业,勘探地质研究业务需要复杂的逻辑思维,也需要创新性的抽象思维。“智慧油田”和“智能勘探”理论体系的提出,顺应了油气勘探的智能、快速和高效的需求。通过剖析油气勘探的信息支撑框架设计,提出了未来智能化勘探的4层设计概念,形成了层次之间的衔接和沟通的关键技术,并针对此理论体系探索了石油行业中石油地质勘探智能化信息框架实施策略。     

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.