The book comprises innovative research presented at the 14th Conference of the Association of Geographic Information Laboratories in Europe (AGILE), held in 2011 in Utrecht, The Netherlands. The scientific papers cover a large variety of fundamental research topics as well as applied research in Geoinformation Science including measuring spatiotemporal phenomena, quality and semantics, spatiotemporal analysis, modeling and decision support as well as spatial information infrastructures. The book is aimed at researchers, practitioners and students who work in various fields and disciplines related to Geoinformation Science and technology.

Many of the challenges of the next century will have physical dimensions, such as tsunamis, hurricanes, and climate change as well as human dimensions such as economic crises, epidemics, and emergency responses. With pioneering editors and expert contributors, Advanced Geoinformation Science explores how certain technical aspects of geoinformation

Science is increasingly driven by data, and spatial data underpin the science directions laid out in the 2007 U.S. Geological Survey (USGS) Science Strategy. A robust framework of spatial data, metadata, tools, and a user community that is interactively connected to use spatial data in an efficient and flexible way-known as a spatial data infrastructure (SDI)-must be available for scientists and managers to find, use, and share spatial data both within and beyond the USGS. Over the last decade, the USGS has conducted breakthrough research that has overcome some of the challenges associated with implementing a large SDI. Advancing Strategic Science: A Spatial Data Infrastructure Roadmap for the U.S. Geological Survey is intended to ground those efforts by providing a practical roadmap to full implementation of an SDI to enable the USGS to conduct strategic science.

A revision of Openshaw and Abrahart’s seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field’s development, this new edition takes a broader view and provides comprehensive coverage across the field of GeoComputation. See What’s New in the Second Edition: Coverage of ubiquitous computing, the GeoWeb, reproducible research, open access, and agent-based modelling Expanded chapter on Genetic Programming and a separate chapter developed on Evolutionary Algorithms Ten chapters updated by the same or new authors and eight new chapters added to reflect state of the art Each chapter is a stand-alone entity that covers a particular topic. You can simply dip in and out or read it from cover to cover. The opening chapter by Stan Openshaw has been preserved, with only a limited number of minor essential modifications having been enacted. This is not just a matter of respect. Openshaw’s work is eloquent, prophetic, and his overall message remains largely unchanged. In contrast to other books on this subject, GeoComputation: Second Edition supplies a state-of-the-art review of all major areas in GeoComputation with chapters written especially for this book by invited specialists. This approach helps develop and expand a computational culture, one that can exploit the ever-increasing richness of modern geographical and geospatial datasets. It also supplies an instructional guide to be kept within easy reach for regular access and when need arises.

This book is the result of invited and competitive submissions to a 2015 academic institute on Advancing Geographic Information Science: The Past and Next Twenty Years. A core goal of the institute was to review the research challenges of the past twenty years and discuss emerging challenges of the next twenty.

This guide offers a comprehensive but concise resource based on extensive, carefully analysed examples from the published literature. It enables students and researchers in science and engineering to write and present material to a professional modern standard, efficiently and painlessly, and with maximum impact.

Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.