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| data science in environmental science: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data science in environmental science: Environmental Data Analysis with MatLab William Menke, Joshua Menke, 2011-09-02 Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them. |
| data science in environmental science: Introduction to Environmental Data Science William W. Hsieh, 2023-03-31 A comprehensive guide to machine learning and statistics for students and researchers of environmental data science. |
| data science in environmental science: Spatial Data Analysis in the Social and Environmental Sciences Robert P. Haining, Robert Haining, 1993-08-26 Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research workers and final year undergraduates in the fields of geography, environmental sciences and social sciences. |
| data science in environmental science: Data Analysis and Statistics for Geography, Environmental Science, and Engineering Miguel F. Acevedo, 2012-12-07 Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustain |
| data science in environmental science: Introduction to Environmental Data Science Jerry Davis, 2023-03-13 Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics and modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels. Features • Gives thorough consideration of the needs for environmental research in both spatial and temporal domains. • Features examples of applications involving field-collected data ranging from individual observations to data logging. • Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA. • Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors. • All examples and exercises make use of a GitHub package for functions and especially data. |
| data science in environmental science: Introduction to Environmental Data Science Jerry D. Davis, 2023 Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics & modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science. It is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels-- |
| data science in environmental science: Machine Learning Methods in the Environmental Sciences William W. Hsieh, 2009-07-30 A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences. |
| data science in environmental science: Applied Statistics for Environmental Science with R Abbas F. M. Al-Karkhi, Wasin A. A. Alqaraghuli, 2019-09-13 Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. - Includes step-by-step tutorials to aid in understanding the process and implementation of unique data - Presents statistical theory in a simple way without complex mathematical proofs - Shows how to analyze data using R software and provides R scripts for all examples and figures |
| data science in environmental science: Introduction to Environmental Data Analysis and Modeling Moses Eterigho Emetere, Esther Titilayo Akinlabi, 2020-01-03 This book introduces numerical methods for processing datasets which may be of any form, illustrating adequately computational resolution of environmental alongside the use of open source libraries. This book solves the challenges of misrepresentation of datasets that are relevant directly or indirectly to the research. It illustrates new ways of screening datasets or images for maximum utilization. The adoption of various numerical methods in dataset treatment would certainly create a new scientific approach. The book enlightens researchers on how to analyse measurements to ensure 100% utilization. It introduces new ways of data treatment that are based on a sound mathematical and computational approach. |
| data science in environmental science: Data Treatment in Environmental Sciences Valérie David, 2017-05-25 Data Treatment in Environmental Sciences presents the various methods used in the analysis of databases—obtained in the field or in a laboratory—by focusing on the most commonly used multivariate analyses in different disciplines of environmental sciences, from geochemistry to ecology. The book examines the principles, application conditions and implementation (in R software) of various analyses before interpreting them. The wide variety of analyses presented allows users to treat datasets, both large and small, which are often limited in terms of available processing techniques. The approach taken by the author details (i) the preparation of a dataset prior to analysis, in relation to the scientific strategy and objectives of the study, (ii) the preliminary treatment of datasets, (iii) the establishment of a structure of objects (stations/dates) or relevant variables (e.g. physicochemical, biological), and (iv) how to highlight the explanatory parameters of these structures (e.g. how the physico-chemistry influences the biological structure obtained). - Proposes tools that can be used to deal with environmental data - Insists on the adequacy between the scientific objectives and the types of analyses - Present mathematical principles without going into detail - Offers a wide range of important analyses |
| data science in environmental science: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods. |
| data science in environmental science: Environmental Data Analysis Carsten Dormann, 2020-12-20 Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg. |
| data science in environmental science: Analyzing Environmental Data Walter W. Piegorsch, A. John Bailer, 2005-03-04 Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of data evaluation. The text extends beyond the introductory level, allowing students and environmental science practitioners to develop the expertise to design and perform sophisticated environmental data analyses. In particular, it: Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data. Takes a data-oriented approach to describing the various methods. Illustrates the methods with real-world examples Features extensive exercises, enabling use as a course text. Includes examples of SAS computer code for implementation of the statistical methods. Connects to a Web site featuring solutions to exercises, extra computer code, and additional material. Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals. Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology. |
| data science in environmental science: Deep Learning for Hydrometeorology and Environmental Science Taesam Lee, Vijay P. Singh, Kyung Hwa Cho, 2021-01-27 This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model. |
| data science in environmental science: Environmental Statistics and Data Analysis Wayne R. Ott, 2018-12-13 This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting importa |
| data science in environmental science: Environmental Data Exchange Network for Inland Water Palle Haastrup, Jorgen Würtz, 2011-08-11 Understanding and protecting our environment is a key component of environmental development, yet access to a wide range of high-quality information is currently based on very limited data due to lack of the exchange of data between source and recipient. This three part book that first discusses the importance of data exchange and describes why it is essential for gathering data in the environmental sciences. Part Two takes the results of the Environmental Data Exchange Network for Inland Water project (EDEN-IW), and addresses its objectives for ensuring that the needs of citizens and enterprises of the environmental sciences community are met. Finally, Part Three takes a look at the wide variety of data policies and addresses how environment administrators in Europe can enhance their efficiency, openness and accountability. - Discusses the importance of data exchange, as well as database integration and distribution of data with software agents - Provides the results, objectives, and focus of the EDEN-IW project for sharing knowledge - Addresses current data exchange policies and its future impact within the environmental fields |
| data science in environmental science: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science in environmental science: Statistics for Geography and Environmental Science Richard Harris, Claire Jarvis, 2014-05-01 Statistics are important tools for validating theory, making predictions and engaging in policy research. They help to provide informed commentary about social and environmental issues, and to make the case for change. Knowledge of statistics is therefore a necessary skill for any student of geography or environmental science. This textbook is aimed at students on a degree course taking a module in statistics for the first time. It focuses on analysing, exploring and making sense of data in areas of core interest to physical and human geographers, and to environmental scientists. It covers the subject in a broadly conventional way from descriptive statistics, through inferential statistics to relational statistics but does so with an emphasis on applied data analysis throughout. |
| data science in environmental science: Environmental Data Analysis Zhihua Zhang, 2016-11-21 Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index |
| data science in environmental science: Introduction to Python in Earth Science Data Analysis Maurizio Petrelli, 2021-09-16 This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book. |
| data science in environmental science: Statistics for Environmental Science and Management Bryan F.J. Manly, 2008-10-21 Presenting a nonmathematical approach to this topic, Statistics for Environmental Science and Management introduces frequently used statistical methods and practical applications for the environmental field. This second edition features updated references and examples along with new and expanded material on data quality objectives, the generalized linear model, spatial data analysis, and Monte Carlo risk assessment. Additional topics covered include environmental monitoring, impact assessment, censored data, environmental sampling, the role of statistics in environmental science, assessing site reclamation, and drawing conclusions from data. |
| data science in environmental science: Environmental Science For Dummies Alecia M. Spooner, 2012-06-22 The easy way to score high in Environmental Science Environmental science is a fascinating subject, but some students have a hard time grasping the interrelationships of the natural world and the role that humans play within the environment. Presented in a straightforward format, Environmental Science For Dummies gives you plain-English, easy-to-understand explanations of the concepts and material you'll encounter in your introductory-level course. Here, you get discussions of the earth's natural resources and the problems that arise when resources like air, water, and soil are contaminated by manmade pollutants. Sustainability is also examined, including the latest advancements in recycling and energy production technology. Environmental Science For Dummies is the most accessible book on the market for anyone who needs to get a handle on the topic, whether you're looking to supplement classroom learning or simply interested in learning more about our environment and the problems we face. Presents straightforward information on complex concepts Tracks to a typical introductory level Environmental Science course Serves as an excellent supplement to classroom learning If you're enrolled in an introductory Environmental Science course or studying for the AP Environmental Science exam, this hands-on, friendly guide has you covered. |
| data science in environmental science: New Ecoinformatics Tools in Environmental Science Vladimir F. Krapivin, Costas A. Varotsos, Vladimir Yu. Soldatov, 2015-01-24 This book provides new insights on the study of global environmental changes using the ecoinformatics tools and the adaptive-evolutionary technology of geoinformation monitoring. The main advantage of this book is that it gathers and presents extensive interdisciplinary expertise in the parameterization of global biogeochemical cycles and other environmental processes in the context of globalization and sustainable development. In this regard, the crucial global problems concerning the dynamics of the nature-society system are considered and the key problems of ensuring the system’s sustainable development are studied. A new approach to the numerical modeling of the nature-society system is proposed and results are provided on modeling the dynamics of the system’s characteristics with regard to scenarios of anthropogenic impacts on biogeochemical cycles, land ecosystems and oceans. The main purpose of this book is to develop a universal guide to information-modeling technologies for assessing the function of environmental subsystems under various climatic and anthropogenic conditions. |
| data science in environmental science: Studies on Time Series Applications in Environmental Sciences Alina Bărbulescu, 2016-03-12 Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. |
| data science in environmental science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science in environmental science: Statistical Methods in Water Resources D.R. Helsel, R.M. Hirsch, 1993-03-03 Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences. |
| data science in environmental science: Modeling and Data Analysis: An Introduction with Environmental Applications John B. Little, 2019-03-28 Can we coexist with the other life forms that have evolved on this planet? Are there realistic alternatives to fossil fuels that would sustainably provide for human society's energy needs and have fewer harmful effects? How do we deal with threats such as emergent diseases? Mathematical models—equations of various sorts capturing relationships between variables involved in a complex situation—are fundamental for understanding the potential consequences of choices we make. Extracting insights from the vast amounts of data we are able to collect requires analysis methods and statistical reasoning. This book on elementary topics in mathematical modeling and data analysis is intended for an undergraduate “liberal arts mathematics”-type course but with a specific focus on environmental applications. It is suitable for introductory courses with no prerequisites beyond high school mathematics. A great variety of exercises extends the discussions of the main text to new situations and/or introduces new real-world examples. Every chapter ends with a section of problems, as well as with an extended chapter project which often involves substantial computing work either in spreadsheet software or in the R statistical package. |
| data science in environmental science: Managing Environmental Data Gerald A. Burnette, 2021-12-21 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. |
| data science in environmental science: Environmental Software Systems. Data Science in Action Ioannis N. Athanasiadis, Steven P. Frysinger, Gerald Schimak, Willem Jan Knibbe, 2020-01-29 This book constitutes the refereed proceedings of the 13th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2020, held in Wageningen, The Netherlands, in February 2020. The 22 full papers and 3 short papers were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics on environmental informatics, including data mining, artificial intelligence, high performance and cloud computing, visualization and smart sensing for environmental, earth, agricultural and food applications. |
| data science in environmental science: Big Data Science and Analytics for Smart Sustainable Urbanism Simon Elias Bibri, 2019 We are living at the dawn of what has been termed 'the fourth paradigm of science, ' a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power-manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data-intensive science and its application, particularly in relation to sustainability. |
| data science in environmental science: Vis Enviro Science EPUB High School 6 Year Access David M. Hassenzahl, Linda R. Berg, Mary Catherine Hager, 2017-11-06 |
| data science in environmental science: Citizen Science Janis L. Dickinson, Richard E. Bonney, Jr., 2012-04-07 Citizen science enlists members of the public to make and record useful observations, such as counting birds in their backyards, watching for the first budding leaf in spring, or measuring local snowfall. The large numbers of volunteers who participate in projects such as Project FeederWatch or Project BudBurst collect valuable research data, which, when pooled together, create an enormous body of scientific data on a vast geographic scale. In return, such projects aim to increase participants' connections to science, place, and nature, while supporting science literacy and environmental stewardship. In Citizen Science, experts from a variety of disciplines—including scientists and education specialists working at the Cornell Lab of Ornithology, where many large citizen science programs use birds as proxies for biodiversity—share their experiences of creating and implementing successful citizen science projects, primarily those that use massive data sets gathered by citizen scientists to better understand the impact of environmental change. This first and foundational book for this developing field of inquiry addresses basic aspects of how to conduct citizen science projects, including goal-setting, program design, and evaluation, as well as the nuances of creating a robust digital infrastructure and recruiting a large participant base through communications and marketing. An overview of the types of research approaches and techniques demonstrates how to make use of large data sets arising from citizen science projects. A final section focuses on citizen science's impacts and its broad connections to understanding the human dimensions and educational aspects of participation. Citizen Science teaches teams of program developers and researchers how to cross the bridge from success at public engagement to using citizen science data to understand patterns and trends or to test hypotheses about how ecological processes respond to change at large geographic scales. Intended as a resource for a broad audience of experts and practitioners in natural sciences, information science, and social sciences, this book can be used to better understand how to improve existing programs, develop new ones, and make better use of the data resources that have accumulated from citizen science efforts. Its focus on harnessing the impact of crowdsourcing for scientific and educational endeavors is applicable to a wide range of fields, especially those that touch on the importance of massive collaboration aimed at understanding and conserving what we can of the natural world. |
| data science in environmental science: Artificial Intelligence and Data Science in Environmental Sensing Mohsen Asadnia, Amir Razmjou, Amin Beheshti, 2022-02-09 Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. - Presents tools, connections and proactive solutions to take sustainability programs to the next level - Offers a practical guide for making students proficient in modern electronic data analysis and graphics - Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery |
| data science in environmental science: Environmental Science for a Changing World (Canadian Edition) Karen Ing, Susan Karr, Marnie Brankreun, 2013-09-02 Environmental Science for a Changing World captivates students with real-world stories while exploring the science concepts in context. Engaging stories plus vivid photos and infographics make the content relevant and visually enticing. The result is a text that emphasizes environmental, scientific, and information literacies in a way that engages students. |
| data science in environmental science: Health, Safety, and Environmental Data Analysis Anthony J. Joseph, 2020-07-09 Professionals in environmental health and safety (EHS) management use statistics every day in making decisions. This book was created to provide the quantitative tools and techniques necessary to make important EHS assessments. Readers need not be statistically or mathematically inclined to make the most of this book-mathematical derivations are kept to a minimum and subjects are approached in a simple and factual manner, complemented with plenty of real-world examples. Chapters 1-3 cover knowledge of basic statistical concepts such as presentation of data, measurements of location and dispersion, and elementary probability and distributions. Data gathering and analysis topics including sampling methods, sampling theory, testing, and interference as well as skills for critically evaluating published numerical material is presented in Chapters 4-6. Chapters 7-11 discuss information generation topics-regression and correlation analysis, time series, linear programming, network and Gnatt charting, and decision analysis-tools that can be used to convert data into meaningful information. Chapter 12 features six examples of projects made successful through statistical approaches being applied. Readers can use these approaches to solve their own unique problems. Whether you are a EHS professional, manager, or student, Health, Safety, and Environmental Data Analysis: A Business Approach will help you communicate statistical data effectively. |
| data science in environmental science: Environmental Systems Science Daniel A. Vallero, 2021-05-27 Environmental Systems Science: Theory and Practical Applications looks at pollution and environmental quality from a systems perspective. Credible human and ecological risk estimation and prediction methods are described, including life cycle assessment, feasibility studies, pollution control decision tools, and approaches to determine adverse outcome pathways, fate and transport, sampling and analysis, and cost-effectiveness. The book brings translational science to environmental quality, applying groundbreaking methodologies like informatics, data mining, and applications of secondary data systems. Multiple human and ecological variables are introduced and integrated to support calculations that aid environmental and public health decision making. The book bridges the perspectives of scientists, engineers, and other professionals working in numerous environmental and public health fields addressing problems like toxic substances, deforestation, climate change, and loss of biological diversity, recommending sustainable solutions to these and other seemingly intractable environmental problems. The causal agents discussed include physical, chemical, and biological agents, such as per- and polyfluoroalkyl substances (PFAS), SARS-CoV-2 (the COVID-19 virus), and other emerging contaminants. - Provides an optimistic and interdisciplinary approach, underpinned by scientific first principles and theory to evaluate pollutant sources and sinks, applying biochemodynamic methods, measurements and models - Deconstructs prior initiatives in environmental assessment and management using an interdisciplinary approach to evaluate what has worked and why - Lays out a holistic understanding of the real impact of human activities on the current state of pollution, linking the physical sciences and engineering with socioeconomic, cultural perspectives, and environmental justice - Takes a life cycle view of human and ecological systems, from the molecular to the planetary scale, integrating theories and tools from various disciplines to assess the current and projected states of environmental quality - Explains the elements of risk, reliability and resilience of built and natural systems, including discussions of toxicology, sustainability, and human-pollutant interactions based on spatial, biological, and human activity information, i.e. the exposome |
| data science in environmental science: Insights from Data with R Owen L. Petchey, Andrew P. Beckerman, Natalie Cooper, Dylan Z. Childs, 2021-02-24 Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data. |
| data science in environmental science: Geostatistics for Environmental Scientists Richard Webster, Margaret A. Oliver, 2007-10-22 Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved. |
| data science in environmental science: Data Science, Classification, and Related Methods Chikio Hayashi, Keiji Yajima, Hans H. Bock, 2014-01-15 |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
