Public environmental information: understanding requirements and patterns of likely public use

Public access to environmental information received much attention in the last decade. Recent developments promote access to this information through public telecommunication networks. This paper describes a Web-based survey that explored the requirements and needs of likely users of public environmental information systems for London: educated middle-class members of the public, with high interest in environmental issues and computer literacy. The survey demonstrated current information consumption patterns and the reasons to gather and use such information.     

On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong

This paper presents a statistical approach to study the spatial relationship between landslides and their causative factors at the regional level. The approach is based on digital databases, and incorporates such methods as statistics, spatial pattern analysis, and interactive mapping. Firstly, the authors propose an object-oriented conceptual model for describing a landslide event, and a combined database of landslides and environmental factors is constructed by integrating the various databases within such a conceptual framework. The statistical histogram, spatial overlay, and dynamic mapping methods are linked together to interactively evaluate the spatial pattern of the relationship between landslides and their causative factors. A case study of an extreme event in 1993 on Lantau Island indicates that rainfall intensity and the migration of the center of the rainstorm greatly influence the occurrence of landslides on Lantau Island. A regional difference in the relationship between landslides and topography is identified. Most of the landslides in the middle and western parts of the island occurred on slopes with slope angles of 25–35°, while in the eastern part, the corresponding range is 30–35°. Overlaying landslide data with land cover reveals that a large number of landslides occurred in the bareland and shrub-covered area, and in the transition zones between different vegetation types. The proposed approach can be used not only to analyze the general characteristics of such a relationship, but also to depict its spatial distribution and variation, thereby providing a sound basis for regional landslide prediction.     

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.     

信息产业发展对宁波城市内部结构的影响

该文从信息产业的概念和分类人手，着重分析信息产业发展对城市产业结构、社会结构和空间结构的影响。对宁波市信息产业与城市产业结构的内在关系进行了量化研究；探讨信息产业对城市内部社会结构分异的变化趋势；提出了信息产业对城市内部空间结构功能置换的两种模式和地域扩张的促动机制。     

基于网格的空间信息模型与服务技术研究

空间信息基础设施是实现数字城市的核心与基础，现有的万维网已经不能满足空间信息访问与处理的需要。该文对以空间信息为核心和基础的数据网格——空间信息网格的基础理论及网格中的空间信息模型与服务技术：进行研究。提出了空间信息网格的若干关键技术及网格中的空间信息模型的特点与要求，并对空间信息网格服务进行了初步的探讨。     

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。     

2500年来艾比湖的环境演变信息

通过对艾比湖缘1，8m浅孔的沉积相和孢粉组合，结合14C测年资料分析。指出近2500年来艾比湖的沉积环境总体是比较稳定的，但由于气候波动引起艾比湖水位曾发生较明显的变化。约在公元前300～400年，是艾比湖面积缩小时期；约公元前300—公元300年，即东周末至西晋，是艾比湖水位较高时期；约公元300—1400年，即东晋至15世纪初，是艾比湖的高水位时期；约15世纪初至17世纪中是艾比湖的水位下降期，但水位比现代仍然高；约17世纪中至19世纪初的小冰期是艾比湖的水位上升期。研究还提供了历史时期湖泊的盐度变化和湖周发生大火的信息。     

多维植被信息系统及其应用前景

3S(地理信息系统GIS、遥感RS、全球定位系统GPS)及其相关技术的发展，使得植被信息系统的构建和应用成为可能。早期的植被图没有涉及到高度维和时间维，其应用受到很大的限制。因此有必要建立一种基于时空的多维植被信息系统，它的组成包括地理信息系统GIS、数据处理模块DPM、数据存取模块DAM、植被分析模块VAM和交互显示模块IDM，可用于植被的时空分析、三维景观显示、植被—地形—气候关系研究以及植被综合管理等方面，特别适合于山地植被的研究。多维植被信息系统具备立体直观的用户友好界面和强大的时空数据处理能力，并可通过数据库和网络进行资源共享，使植被研究和管理自动化、无纸化，其应用前景广阔。     

云南省防洪空间信息系统构建

通过讨论应用地理信息系统（GIS）技术，立足现实对相关空间数据进行集成和整合处理，实现防洪空间信息的数字化、标准化，为优化防汛指挥工作手段提供基础。系统数据库饭知不同比例尺精度的空间基础数据以满足不同目的的需求，对相关水文、防洪设施、全省洪涝风险和重点区风险图、土地利用等图件进行系统整合。系统有较强的管理功能和分析功能，可提高云南省防洪空间信息的共享度和系统有效管理，为政府统一组织和指挥抗洪救灾提供重要的辅助决策支持手段。