By synthesizing scholarly work at the intersection of political ecology, digital geography, and science and technology studies, The Nature of Data analyzes how new digital technologies affect environments and their control.

SAGE Course Companions are an exciting new series from SAGE offering students an insider's guide into how to make the most of their undergraduate courses and extend their understanding of key concepts covered in their course. Social Research Methods provides student readers with essential help with their research project, with revising for their course exams, preparing and writing course assessment materials, and enhancing and progressing their knowledge and thinking skills in line with course requirements on Research Methods courses. This Course Companion is designed to augment, rather than replace, existing textbooks for the course, and will provide: " Helpful summaries of the course curriculum to aid essay and project planning " Key summaries of the approach taken by the main Methods textbooks " Guidance on the essential study skills required " Help with developing critical thinking " Route-maps to aid the development of wider learning above and beyond the textbook " Pointers to success in course exams and written assessment exercises " A tutor's-eye view of what course examiners are looking for " An insider's view of what key course concepts are really all about SAGE Course Companions are much more than revision guides for undergraduate; they are an essential tool to taking your course learning and understanding to new levels and in new directions that are the key to success in undergraduate courses.

When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise “revolutionary” advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.

By synthesizing scholarly work at the intersection of political ecology, digital geography, and science and technology studies, The Nature of Data analyzes how new digital technologies affect environments and their control.

This book provides thorough and comprehensive coverage of most of the new and important quantitative methods of data analysis for graduate students and practitioners. In recent years, data analysis methods have exploded alongside advanced computing power, and it is critical to understand such methods to get the most out of data, and to extract signal from noise. The book excels in explaining difficult concepts through simple explanations and detailed explanatory illustrations. Most unique is the focus on confidence limits for power spectra and their proper interpretation, something rare or completely missing in other books. Likewise, there is a thorough discussion of how to assess uncertainty via use of Expectancy, and the easy to apply and understand Bootstrap method. The book is written so that descriptions of each method are as self-contained as possible. Many examples are presented to clarify interpretations, as are user tips in highlighted boxes.

This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.

The power of mapping: principles for visualizing knowledge, illustrated by many stunning large-scale, full-color maps. Maps of physical spaces locate us in the world and help us navigate unfamiliar routes. Maps of topical spaces help us visualize the extent and structure of our collective knowledge; they reveal bursts of activity, pathways of ideas, and borders that beg to be crossed. This book, from the author of Atlas of Science, describes the power of topical maps, providing readers with principles for visualizing knowledge and offering as examples forty large-scale and more than 100 small-scale full-color maps. Today, data literacy is becoming as important as language literacy. Well-designed visualizations can rescue us from a sea of data, helping us to make sense of information, connect ideas, and make better decisions in real time. In Atlas of Knowledge, leading visualization expert Katy Börner makes the case for a systems science approach to science and technology studies and explains different types and levels of analysis. Drawing on fifteen years of teaching and tool development, she introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. To exemplify the framework, the Atlas features striking and enlightening new maps from the popular “Places & Spaces: Mapping Science” exhibit that range from “Key Events in the Development of the Video Tape Recorder” to “Mobile Landscapes: Location Data from Cell Phones for Urban Analysis” to “Literary Empires: Mapping Temporal and Spatial Settings of Victorian Poetry” to “Seeing Standards: A Visualization of the Metadata Universe.” She also discusses the possible effect of science maps on the practice of science.

A wake-up call for America to create a new framework for democratizing data. Public data are foundational to our democratic system. People need consistently high-quality information from trustworthy sources. In the new economy, wealth is generated by access to data; government's job is to democratize the data playing field. Yet data produced by the American government are getting worse and costing more. In Democratizing Our Data, Julia Lane argues that good data are essential for democracy. Her book is a wake-up call to America to fix its broken public data system.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.