意大利产业区重组：集团化、创新与国际化

近30 a来,意大利产业区成为西方学术界研究的热点与争论焦点。传统的意大利产业区模式主要强调中小企业为主的＂弹性专业化＂生产系统与本地社区的嵌入性。在全球化与知识经济的宏观背景下,意大利产业区正在经历一系列的以集团化、创新与国际化为主要特征的产业重组过程：产业区的组织结构开始由中小企业为主的＂弹性专业化＂生产系统向＂产...     

Linear interpolation and joint model fitting of experimental transiograms for Markov chain simulation of categorical spatial variables

In Markov chain random field (MCRF) simulation of categorical spatial variables with multiple classes, joint modeling of a large number of experimental auto and cross-transiograms is needed. This can be tedious when mathematical models are used to fit the complex features of experimental transiograms. Linear interpolation can be used to perform the joint modeling quickly regardless of the number and the complexity of experimental transiograms. In this paper, we demonstrated the mathematical validity of linear interpolation as a joint transiogram-modeling method, explored its applicability and limitations, and tested its effect on simulated results by case studies with comparison to the joint model-fitting method. Simulations of a five-class variable showed little difference in patterns for interpolated and fitted transiogram models when samples were sufficient and experimental transiograms were in regular shapes; however, they neither showed large difference between these two kinds of transiogram models when samples were relatively sparse, which might indicate that MCRFs were not much sensitive to the difference in the detail of the two kinds of transiogram models as long as their change trends were identical. If available, expert knowledge might play an important role in transiogram modeling when experimental transiograms could not reflect the real spatial variation of the categorical variable under study. An extra finding was that class enclosure feature (i.e., a class always appears within another class) was captured by the asymmetrical property of transiograms and further generated in simulated patterns, whereas this might not be achieved in conventional geostatistics. We conclude that (i) when samples are sufficient and experimental transiograms are reliable, linear interpolation is satisfactory and more efficient than model fitting; (ii) when samples are relatively sparse, choosing a suitable lag tolerance is necessary to obtain reliable experimental transiograms for linear interpolation; (iii) when samples are very sparse (or few) and experimental transiograms are erratic, coarse model fitting based on expert knowledge is recommended as a better choice whereas both linear interpolation and precise model fitting do not make sense anymore.     

Research relevance, 'knowledge transfer' and the geographies of CASE studentship collaboration

The Co-operative Awards in Science and Engineering (CASE) studentship programme of the UK Research Councils provide one example of wider efforts internationally to encourage so-called 'knowledge transfer' and thereby harness publicly supported university research more closely to the goals of national competitiveness, regional economic development and local regeneration. In this paper we describe the implications of how the various UK research councils have interpreted the objectives and beneficiaries of 'knowledge transfer', both for the relative opportunities available to human and physical geographers for collaboration through CASE and for the sorts of values that their research must serve. Then, we draw on unpublished data from the Economic and Social Research Council (ESRC) to explore the geographies of CASE studentship allocation and participation. The broad regional and institutional patterns of participation we describe have important implications for ongoing debates in the UK about research selectivity and the role of the university as an engine of local development, while the striking disciplinary patterns of CASE participation, and in particular the overwhelming success of geographers in this competitive programme, provide an opportunity to reassess claims about whether and for whom geographical research is relevant.     

中原经济区建设背景下的科技创新体系研究

中原经济区要建设成为全国"三化"协调发展示范区、全国重要的经济增长板块、全国综合交通枢纽和物流中心、华夏历史文明传承核心区,必须加快自主创新体系建设,提高自主创新能力,充分发挥科技创新的有力支撑作用。科技创新是一类开放的复杂巨系统,其专家系统、技术支撑系统、数据信息系统构成了科技创新体系的内核。通过加强人才培养、引进和使用,推进知识创新体系建设;狠抓科技投入和输出,推动技术创新体系建设;强化政府政策扶持引导,推进制度和管理创新体系建设,必然会推动区域发展由要素驱动向创新驱动转变,必然会推动和促进中原经济区的快速、全面、科学和可持续发展。     

基于智能对象的决策支持系统体系结构研究

分析了决策问题及其求解过程,揭示了传统IDSS体系结构的不足,阐述了应用面向对象的结构化知识表达构造智能对象,提供了智能决策过程支持的方法和新的IDSS体系结构,并在农业空间决策信息系统的实践中进行了验证。     

Geographical knowledge and policy: the positive contribution of discourse studies

The 'discursive turn' has been blamed for a lack of engagement between geographers and policymakers. This paper argues that such a view is fundamentally flawed. It ignores the positive contribution that discourse analysis has made to policy studies and the particular role that geographers have played in developing such analyses. It is based on a misconceived view of the policy process and how knowledge can contribute to policy outcomes. A closer attention to policy discourses reveals exactly how knowledge is constructed within the policy process and can help identify how policy may be improved through discursive means.     

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.     

数字地形分析应用适配性知识的案例表达与推理方法

数字地形分析(Digital Terrain Analysis, DTA)在应用时依赖于建模知识,尤其是关于所建的应用模型是否与研究区特点、数据等条件相适配的知识(称为“应用适配性知识”);由于这类知识难以形式化表达,现有的数字地形分析工具对此类知识缺乏利用,从而导致普通用户在应用数字地形分析时建模困难。针对该问题,设计了一套数字地形分析领域应用适配性知识的案例表达与相应的推理方法。以美国32个河网提取案例为例,通过交叉验证,初步表明案例及其推理应用方法适合于数字地形分析领域应用适配性知识的形式化表达与应用,该方法通过与建模环境的集成,可大幅降低数字地形分析应用建模难度。     

A Decision Support System for the Evaluation of Eco-engineering Strategies for Slope Protection

A decision support system (DSS) has been developed to assist expert and non-expert users in the evaluation and selection of eco-engineering strategies for slope protection. This DSS combines a qualitative hazard assessment of erosion and mass movements with a detailed catalogue of eco-engineering strategies for slope protection of which the suitability is evaluated in relation to the data entered. The slope decision support system (SDSS) is a knowledge based DSS in which knowledge is stored in frames containing rules that can evaluate the available information for a project, stored as project specific information (PSI) in a data file. The advantages of such a system are that it accepts incomplete information and that the qualitative nature of the information does not instil the user with a sense of unjustified exactitude. By its multidisciplinary and progressive nature, the DSS will be of value during the initial stages of an eco-engineering project when data collection and the potential of different eco-engineering strategies are considered. The accent of the output of the DSS is on the application of eco-engineering strategies for slope protection as an environmentally-friendly solution aiding sustainable development. For its acceptance within the engineering community, the DSS needs to prove its predictive capacity. Therefore, its performance has been benchmarked against successful and unsuccessful cases of slope stabilisation using eco-engineering. The target audience and the areas of application of this DSS are reviewed and the strategies for further development in this area suggested.     

上海市学生人群防震减灾科普工作现状调查研究——以初中生群体为例

为了全面系统评价上海市学生人群的防震减灾科普效果,准确了解其科普需求,课题组在2014年下半年选取上海市某初级中学的部分学生作为示例,从科普知识的认知程度、需求与认知偏好、对宣传工作满意度评价、科普教育在学校开展的情况这四个方面进行问卷调查,以便及时总结经验,为今后在该人群中更加科学、高效地开展防震减灾科普宣传工作打下坚实的基础。