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Data Assimilation: Making Sense of Observations (Nato Science Series: Iv: Ser. #26)
by William Lahoz Boris Khattatov Richard MenardData assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
by Liang Xu Seon Ki ParkThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III)
by Liang Xu Seon Ki ParkThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV)
by Seon Ki Park Liang XuThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem (Springer Textbooks in Earth Sciences, Geography and Environment)
by Geir Evensen Femke C. Vossepoel Peter Jan van LeeuwenThis open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture
by Syed Nisar Hussain BukhariIn the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Data Driven Guide to the Analysis of X-ray Photoelectron Spectra using RxpsG
by Giorgio SperanzaThis book provides a theoretical background to X-ray photoelectron spectroscopy (XPS) and a practical guide to the analysis of the XPS spectra using the RxpsG software, a powerful tool for XPS analysis. Although there are several publications and books illustrating the theory behind XPS and the origin of the spectral feature, this book provides an additional practical introduction to the use of RxpsG. It illustrates how to use the RxpsG software to perform specific key operations, with figures and examples which readers can reproduce themselves. The book contains a list of theoretical sections explaining the appearance of the various spectral features (core‑lines, Auger components, valence bands, loss features, etc.). They are accompanied by practical steps, so readers can learn how to analyze specific spectral features using the various functions of the RxpsG software. This book is a useful guide for researchers in physics, chemistry, and material science who are looking to begin using XPS, in addition to experienced researchers who want to learn how to use RxpsG. In the digital format, the spectral data and step-by-step indications are provided to reproduce the examples given in the textbook. RxpsG is a free software for the spectral analysis. Readers can find the installation information and download the package from https://github.com/GSperanza/ website. RxpsG was developed mainly by Giorgio Speranza with the help of his colleague dr. Roberto Canteri working at Fondazione Bruno Kessler. Key Features: Simplifies the use of RxpsG, how it works, and its applications. Demonstrates RxpsG using a reproduction of the graphical interface of RxpsG, showing the steps needed to perform a specific task and the effect on the XPS spectra. Accessible to readers without any prior experience using the RxpsG software. Giorgio Speranza is Senior Researcher at Fondazione Bruno Kessler – Trento Italy, Associate Member of the Italian National Council of Research, and Associate Member of the Department of Industrial Engineering at the University of Trento, Italy.
Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning (Green Energy and Technology)
by Saleh Seyedzadeh Farzad Pour RahimianThis book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
Data-Driven Numerical Modelling in Geodynamics: Methods and Applications (SpringerBriefs in Earth Sciences)
by Alik Ismail-Zadeh Alexander Korotkii Igor TsepelevThis book describes the methods and numerical approaches for data assimilation in geodynamical models and presents several applications of the described methodology in relevant case studies. The book starts with a brief overview of the basic principles in data-driven geodynamic modelling, inverse problems, and data assimilation methods, which is then followed by methodological chapters on backward advection, variational (or adjoint), and quasi-reversibility methods. The chapters are accompanied by case studies presenting the applicability of the methods for solving geodynamic problems; namely, mantle plume evolution; lithosphere dynamics in and beneath two distinct geological domains - the south-eastern Carpathian Mountains and the Japanese Islands; salt diapirism in sedimentary basins; and volcanic lava flow. Applications of data-driven modelling are of interest to the industry and to experts dealing with geohazards and risk mitigation. Explanation of the sedimentary basin evolution complicated by deformations due to salt tectonics can help in oil and gas exploration; better understanding of the stress-strain evolution in the past and stress localization in the present can provide an insight into large earthquake preparation processes; volcanic lava flow assessments can advise on risk mitigation in the populated areas. The book is an essential tool for advanced courses on data assimilation and numerical modelling in geodynamics.
Data Mining for Geoinformatics: Methods and Applications
by Jessica Lin Guido Cervone Nigel WatersThe rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems. This unique volume contains a collection of chapters on state-of-the-art data mining techniques applied to geoinformatic problems of high complexity and important societal value. Data Mining for Geoinformatics addresses current concerns and developments relating to spatio-temporal data mining issues in remotely-sensed data, problems in meteorological data such as tornado formation, estimation of radiation from the Fukushima nuclear power plant, simulations of traffic data using OpenStreetMap, real time traffic applications of data stream mining, visual analytics of traffic and weather data and the exploratory visualization of collective, mobile objects such as the flocking behavior of wild chickens. This book is designed for researchers and advanced-level students focused on computer science, earth science and geography as a reference or secondary text book. Practitioners working in the areas of data mining and geoscience will also find this book to be a valuable reference.
Data Mining for Global Trends in Mountain Biodiversity
by Eva M. Spehn Christian KörnerThanks to advances in electronic archiving of biodiversity data and the digitization of climate and other geophysical data, a new era in biogeography, functional ecology, and evolutionary ecology has begun. In Data Mining for Global Trends in Mountain Biodiversity, Christian Korner, Eva M. Spehn, and a team of experts from the Global Mountain Biodi
Data Modeling for the Sciences: Applications, Basics, Computations
by Steve Pressé Ioannis SgouralisWith the increasing prevalence of big data and sparse data, and rapidly growing data-centric approaches to scientific research, students must develop effective data analysis skills at an early stage of their academic careers. This detailed guide to data modeling in the sciences is ideal for students and researchers keen to develop their understanding of probabilistic data modeling beyond the basics of p-values and fitting residuals. The textbook begins with basic probabilistic concepts, models of dynamical systems and likelihoods are then presented to build the foundation for Bayesian inference, Monte Carlo samplers and filtering. Modeling paradigms are then seamlessly developed, including mixture models, regression models, hidden Markov models, state-space models and Kalman filtering, continuous time processes and uniformization. The text is self-contained and includes practical examples and numerous exercises. This would be an excellent resource for courses on data analysis within the natural sciences, or as a reference text for self-study.
Data, Models and Analysis: The Highest Impact Articles in 'Atmosphere-Ocean'
by Guoqi Han, Hai Lin and Douw SteynThis volume contains the ten most cited articles that have appeared in the journal Atmosphere-Ocean since 1995. These articles cover a wide range of topics in meteorology, climatology and oceanography. Modelling work is represented in five papers, covering global climate model development; a cumulus parameterization scheme for global climate models; development of a regional forecast modelling system and parameterization of peatland hydraulic processes for climate models. Data rehabilitation and compilation in order to support trend analysis work on comprehensive precipitation and temperature data sets is presented in four papers. Field studies are represented by a paper on the circumpolar lead system. While the modelling studies are global in their application and applicability, the data analysis and field study papers cover environments that are specifically, but not uniquely, Canadian. This book will be of interest to researchers, students and professionals in the various sub-fields of meteorology, oceanography and climate science.
Data Science: Time Complexity, Inferential Uncertainty, And Spacekime Analytics (De Gruyter Stem Series)
by Ivo D. Dinov Milen Velchev VelevThe amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.
Data Science and Machine Learning Applications in Subsurface Engineering
by Daniel Asante OtchereThis book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Data Science for the Geosciences
by Jef Caers Lijing Wang David Zhen YinData Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
The Data Shake: Opportunities and Obstacles for Urban Policy Making (SpringerBriefs in Applied Sciences and Technology)
by Grazia Concilio Paola Pucci Lieven Raes Geert MareelsThis open access book represents one of the key milestones of PoliVisu, an H2020 research and innovation project funded by the European Commission under the call “Policy-development in the age of big data: data-driven policy-making, policy-modelling and policy-implementation”. It investigates the operative and organizational implications related to the use of the growing amount of available data on policy making processes, highlighting the experimental dimension of policy making that, thanks to data, proves to be more and more exploitable towards more effective and sustainable decisions. The first section of the book introduces the key questions highlighted by the PoliVisu project, which still represent operational and strategic challenges in the exploitation of data potentials in urban policy making. The second section explores how data and data visualisations can assume different roles in the different stages of a policy cycle and profoundly transform policy making.
Databases for Data-Centric Geotechnics: Geotechnical Structures (Challenges in Geotechnical and Rock Engineering)
by Chong Tang and Kok-Kwang PhoonDatabases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This second volume pertains to geotechnical structures. The opening chapter presents a substantial survey of performance databases and the effectiveness of our prediction models in matching the field measurements in these databases, based on (1) full-scale field tests, (2) 39 prediction exercises organized as a part of international conferences, and (3) comparison between numerical analyses and in-situ or field measurements conducted by the French LCPC. The focus is on the evaluation of the statistical degree of confidence in predicting various of quantities of interest such as capacity and deformation. The following 18 chapters then present databases on the performance of shallow foundations, spudcan foundations, deep foundations, anchors and pipelines, retaining systems and excavations, and landslides. The databases were compiled from studies undertaken in many countries such as Australia, Belgium, Bolivia, Brazil, Canada, China, Egypt, France, Germany, Hungary, Iran, Ireland, Japan, Kenya, Malaysia, Netherlands, Norway, Poland, Portugal, South Africa, the United Kingdom and the United States.This volume on geotechnical structures is a companion to the volume on site characterization. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.Chapter 10 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [Attribution (CC BY)] 4.0 license.
Databases for Data-Centric Geotechnics: Site Characterization (Challenges in Geotechnical and Rock Engineering)
by Kok-Kwang Phoon and Chong TangDatabases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods. This first volume pertains to site characterization. The opening chapter presents an in-depth analysis of site data attributes, including the establishment of a new taxonomy of site data under “4S” (site generalizations, spatial features, sampling characteristics, and smart data) to provide a novel agenda for data-driven site characterization. Type 3 machine learning methods (disruptive value) are possible as sensors become more pervasive and more intelligent. A comprehensive overview of site characterization information is also presented with a focus on its availability, coverage, value to decision making, and challenges. The remaining 13 chapters cover databases of soil and rock properties and the application of these databases to rock socket behavior, rock classification, settlement on soft marine clays, permeability of fine-grained soils, and liquefaction among others. The databases were compiled from studies undertaken in many countries including Austria, Australia, Brazil, Canada, China, France, Finland, Germany, India, Iran, Japan, Korea, Malaysia, Mexico, New Zealand, Norway, Singapore, Sweden, Thailand, the United Kingdom, and the United States.This volume on site characterization is a companion to the volume on geotechnical structures. Databases for Data-Centric Geotechnics represents the most diverse and comprehensive assembly of database research in a single publication (consisting of two volumes) to date. It follows from Model Uncertainties for Foundation Design, also published by CRC Press, and suits specialist geotechnical engineers, researchers and graduate students.
Date Palm Genetic Resources and Utilization: Volume 1: Africa and the Americas
by Shri Mohan Jain Jameel M. Al-Khayri Dennis V. JohnsonThis important 2-volume reference book is the first comprehensive resource reflecting the current global status and prospects of date palm cultivation by country. This volume covers Asia and Europe. The Asian countries included are: Iran, Saudi Arabia, Iraq, Pakistan, Oman, Yemen, Israel, Kuwait, Qatar, Bahrain, Syria, Palestine and India. Europe is represented by Spain. Topics discussed are: cultivation practices; genetic resources and breeding; conservation and germplasm banks; cultivar classification and identification based on morphological and molecular markers; micropropagation and progress toward scale-up production; and advances in dates processing and marketing. Chapters are supported by tables and color photographs. Appendixes summarize traits and distribution of major cultivars, commercial resources of offshoots and in vitro plants; and institutions and scientific societies concerned with date palm.
The Dating Game: One Man's Search for the Age of the Earth
by Cherry LewisFeaturing shedworkers and shedbuilders from around the world who are leading the alternative workplace revolution, Shedworking looks at why having a shed office is a greener way of working, improves the work-life balance, and accelerates one's productivity. Inspired by the author#145;s Shedworking website, which has been internationally acclaimed for the groundbreaking scale of its architectural coverage, the book features many previously unpublished images of garden offices and shed-like atmospheres: offices on roofs, sheds inside "traditional" offices, and even sheds on wheels, as well as cutting-edge Le Corbusier-designed models for the back garden, all-glass shed offices, and buildings "built" using living trees. Along the way it offers a whistle-stop tour of famous sheds from Pliny the Younger#145;s summerhouse and the retreats of 19th-century composers Edvard Grieg and Gustav Mahler to award-winning 21st-century fantasy writer Neil Gaiman's gazebo. In short, Shedworking offers a manifesto for those wanting to change their working lives for the better and go to work in the garden.
Dating Torrential Processes on Fans and Cones: Methods and Their Application for Hazard and Risk Assessment (Advances in Global Change Research #47)
by Florian Rudolf-Miklau Markus Stoffel Michelle Schneuwly-BollschweilerThis book provides a detailed overview on methods used for the dating of past torrential activity on fans and cones and fosters the discussion on the impact of past and potential future climate change on torrential processes. The book has a clear focus on the practical applications of these methods, complemented by case studies. The limits of each dating method in case of excessive natural and human interventions on fans and cones are shown.
The Daunting Climate Change: Science, Impacts, Adaptation & Mitigation Strategies, Policy Responses
by Jayarama Reddy PuthalpetThe book starts with an overview of Climate Science. It discusses the signs of Warming, the impacts and consequences on several sectors - terrestrial and coastal ecosystems, water resources, ocean systems, agriculture, food production and food security, human health and safety, livelihoods and poverty, Arctic populations, low-lying States, so on. Mathematical models to project future climate and the resulting concerns, global adaptation experiences, and opportunities for future execution are explained. The mitigation approaches, chiefly decarbonizing the energy sector by developing and applying clean/low carbon energy sources and improving energy efficiency, and the evolving geoengineering schemes are dealt. Carbon pricing, an economic tool to ensure emissions reductions, and transition to a low carbon economy to stimulate sustainable growth are described. The continued global efforts under the UN or otherwise until the recent Paris Agreement to arrive at policy responses to tackle this intriguing but daunting problem of climate change are vividly expounded. Note: T&F does not sell or distribute the hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.
David Attenborough Lines to Live By: Embrace the wonder of your world
by Pop Press'I am hopeful for the future, because although nature is in crisis, now is the time for action.'For 70 years, David Attenborough's soothing voice has brought the magic of nature to our TV screens, teaching millions around the world about beautiful animals and the climate crisis that endangers them.David Attenborough Lines to Live By is a celebration of Sir David's impact on the natural world and will inspire everyone to appreciate and protect the fragile world around us.
David Harvey: A Critical Introduction to His Thought
by Noel Castree Greig Charnock Brett ChristophersDavid Harvey is among the most influential Marxist thinkers of the last half century. This book offers a lucid and authoritative introduction to his work, with a structure designed to reflect the enduring topics and insights that serve to unify Harvey’s writings over a long period of time. Harvey’s writings have exerted huge influence within the social sciences and the humanities. In addition, his work now commands a global readership among Left political activists and those interested in current world affairs. Harvey’s central preoccupation is capitalism and the impacts of its growth-obsessed, contradictory dynamics. His name is synonymous with key analytical concepts like ‘the spatial fix’ and ‘accumulation by dispossession’. This critical introduction to his thought is an essential companion for both new and more experienced readers. The critique of capitalism is one of the most important undertakings of our time, and Harvey’s work offers powerful tools to help us see why a ‘softer’ capitalism is insufficient and a post-capitalist future is necessary. This book is an important resource for scholars and graduate students in geography, politics and many other disciplines across the social sciences and humanities.