When your environmental project reaches the point where the amount of data seems overwhelming, you will need a robust tool to help you manage it. Written by a recognized expert and software author with over 25 years of industry experience, Relational Management and Display of Site Environmental Data begins with an overview of site data management c

Twelve papers, some of which are drawn from a June 2001 symposium of the same name as the text, address issues the use of geographic information systems and spatial modeling software to environmental or hydrologic problems. The major themes of the papers are: accuracy and uncertainty in spatial data

When your environmental project reaches the point where the amount of data seems overwhelming, you will need a robust tool to help you manage it. Written by a recognized expert and software author with over 25 years of industry experience, Relational Management and Display of Site Environmental Data begins with an overview of site data management concepts, then progresses through relational data management theory, the design of the database tool, and implementing a data management system. It includes detailed information on data output including mapping and GIS applications, practical suggestions about working with laboratories, and concludes with pitfalls, horror stories, and successes in site data management. Current topics such as Internet data delivery and eXtensible Markup Language (XML) are also covered. The text provides you with the skills needed to effectively implement and operate an environmental data management system. The concepts covered can be applied to any system, from stand-alone through client-server to Web-based. Relational Management and Display of Site Environmental Data combines the fundamentals of data management and display with the author's many years of experience to help you create your own data management system or make a better-informed decision when selecting a commercial solution.

The three-volume set IFIP AICT 368-370 constitutes the refereed post-conference proceedings of the 5th IFIP TC 5, SIG 5.1 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2011, held in Beijing, China, in October 2011. The 189 revised papers presented were carefully selected from numerous submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas. The 62 papers included in the first volume focus on decision support systems, intelligent systems, and artificial intelligence applications.

Focused on the mechanics of managing environmental data, this book provides guidelines on how to evaluate data requirements, assess tools and techniques, and implement an effective system. Moving beyond the hypothetical, Gerald Burnette illustrates the decision-making processes and the compromises required when applying environmental principles and practices to actual data. Managing Environmental Data explains the basic principles of relational databases, discusses database design, explores user interface options, and examines the process of implementation. Best practices are identified during each portion of the process. The discussion is summarized via the development of a hypothetical environmental data management system. Details of the design help establish a common framework that bridges the gap between data managers, users, and software developers. It is an ideal text for environmental professionals and students. The growth in both volume and complexity of environmental data presents challenges to environmental professionals. Developing better data management skills offers an excellent opportunity to meet these challenges. Gaining knowledge of and experience with data management best practices complements students’ more traditional science education, providing them with the skills required to address complex data requirements.