Virtual vegetables and adopted sheep: ethical relation, authenticity and Internet-mediated food production technologies

Associating ideas related to cultural geographies of 'quality' food production and consumption with recent discussion of Internet technologies, virtuality and simulation, this paper examines two newly constituted Internet enterprises offering customers 'virtual' experiences of food production, and 'quality' food products in which they have invested. The paper shows how these enterprises are associated with particular types of geographical and ethical relation, and a search for 'authentic', connected, relationships with food, farming, locality and 'nature'. These relationships are constituted in an assemblage of things held together by flows of food, products, money and electronic communication. The paper ends by suggesting a number of theoretical ideas that might usefully be explored in empirical research into Internet-mediated food production.     

Identification of geomorphic process units in Kärkevagge, northern Sweden, by remote sensing and digital terrain analysis

Sediment transport processes in the Kärkevagge are investigated concerning their spatial and temporal characteristics due to long–term monitoring. Within this study remote sensing techniques and GIS modelling in connection with geomorphic mapping are applied for identification and characterization of geomorphic process units. Relationships between geomorphometric parameters and slope processes like solifluction, talus creep and rockfall have been analysed. Multitemporal Landsat–TM5 scenes are used as source for landcover characteristics (Normalized Difference Vegetation Index) after preprocessing involving orthorectification and topographic normalization in order to remove possible terrain–induced effects. Additionally, a digital elevation model with a resolution of 20 m for the Kärkevagge catchment is developed and parameters like slope gradient, slope aspect and profile curvature are extracted as input for the analysis of the sediment transport system. The combination of landcover information, geomorphometrical and topological features allows the definition of areas for single process activities. They show specific sediment displacement characteristics depending on material conditions, topological and geometrical features. Geomorphic process units, which show a homogenous composition, are extracted from these available layers.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

GIS专题数据综合的研究

随着GIS技术在各种应用领域的推广使用，各领域的专业数据与G1S的空间数据相结合，形成描述特定地理现象的各种专题数据。与GIS空间数据一样，专题数据的应用也由微观的、较为详细地描述小区域地理现象特征，转向宏观的、较为概略地描述较大范围地理现象特征，这是专题数据综合的主要任务。该文在讨论GIS专题数据类型及特征的基础上，着重分析了专题数据综合的基本类型，并提出了专题数据综合的概念模型，为GIS专题数据自动综合的实施提供理论依据和实施方案。     

空间数据库数据约束系统及其实现

空间数据库模型是构造地理信息系统的关键，如何在空间数据库中定义和实现空间实体间的约束关系直接影响到空间数据的完整性和一致性，是实现通用空间数据库约束系统的关键技术。该文讨论了传统GIS空间数据约束关系实现方法的局限性，提出了一个可扩展的空间数据约束描述和数据更新规则系统，并实现了系统原型。这一可扩展的约束系统将有利于解决GIS的多用户同步操作机制及分布式事务处理的实现问题。     

基于空间信息技术的城市应急救援联动系统研究

该文分析国内外城市现代救援体系及其发展趋势，对基于统一的空间信息基础设施平台分布式城市应急救援联动系统(体系结构与系统实现)的关键技术进行研究，以成都市城市应急救援联动系统为例，探讨了城市应急救援联动系统的构成、功能、业务流程、系统平台、用户界面等。     

设备树在路灯AM/FM/GIS系统中的应用研究

该文提出了AM／FM／GIS中基于“设备树”的设备设施管理模式，运用“设备树”来组织路灯电力网络中的设备设施对象；阐述了如何从设备设施的物理模型抽象出具有树状层次结构的逻辑网络模型，并根据该逻辑模型设计相应的数据结构来构建“设备树”；并介绍了设备树在南京市路灯AM／FM／GIS系统中的设计与实现。     

“面向区域”的GIS控件的设计与使用

本文提出了一个“面向区域”GIS控件的概念———即以特定的地理区域为对象,将基础地理信息连同有关的GIS功能操作融合为一个可重用的程序模块,以Microsoft的COM(即“组件对象模型”)技术为基础,组织为“GIS化”的ActiveX控件。这种GIS控件,将面向对象技术中的“对象”扩展到了整个特定的地理区域空间,其核心思想是不仅封装了GIS的数据模型,必要的功能操作,而且封装了所有的基础地理信息。由于省去了对空间数据的处理,因而这种GIS控件可被广大非GIS专业的信息系统开发者灵活、简便地用于各种管理信息系统、办公自动化系统和决策支持系统的GIS功能扩充,也可用于构建一般的地理信息系统,因而有利于GIS技术应用的普及。文章最后给出了有关陕西省的应用实例     

基于GIS的黄河上游龙羊峡库区生态环境遥感监测研究

本项研究以黄河上游生态环境严重退化的龙羊峡库区为研究区，应用遥感与GIS一体化的方法，建立了库区生态环境动态监测信息系统。在系统的支持下，利用多时相、多波段TM遥感数据，对库区近十年来生态环境变化进行监测分析，揭示了库区土地沙漠化、水土流失、土地利用／土地覆盖的现状，以及建库近十年来生态环境时空演化的过程与发展趋势，为库区生态环境治理提供科学依据。本项研究利用遥感和GIS技术，采用生态环境综合分类系统，在一次分类处理中通过多层次不同级别的组合获得了基于同一信息源的土地沙漠化、草场盖度、土地利用处地覆盖等多种环境类型信息，并利用环境信息模型对库区的土壤侵蚀进行了评价。基于GIS的分类后再处理使遥感分类精度有了一定程度的提高。研究表明，在GIS的支持下充分利用遥感信息监测与评价生态环境，是区域生态环境定量化研究的有效途径。