Data Science Applied to Sustainability Analysis

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

Produk Detail:

  • Author : Jennifer Dunn
  • Publisher : Elsevier
  • Pages : 310 pages
  • ISBN : 0128179775
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis
  • Author : Jennifer Dunn,Prasanna Balaprakash
  • Publisher : Elsevier
  • Release : 11 May 2021
GET THIS BOOKData Science Applied to Sustainability Analysis

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

Computational Intelligent Data Analysis for Sustainable Development

Computational Intelligent Data Analysis for Sustainable Development
  • Author : Ting Yu,Nitesh Chawla,Simeon Simoff
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKComputational Intelligent Data Analysis for Sustainable Development

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure

CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure
  • Author : Cuong Ha-Minh,Anh Minh Tang,Tinh Quoc Bui,Xuan Hong Vu,Dat Vu Khoa Huynh
  • Publisher : Springer Nature
  • Release : 28 October 2021
GET THIS BOOKCIGOS 2021, Emerging Technologies and Applications for Green Infrastructure

This book highlights the key role of green infrastructure (GI) in providing natural and ecosystem solutions, helping alleviate many of the environmental, social, and economic problems caused by rapid urbanization. The book gathers the emerging technologies and applications in various disciplines involving geotechnics, civil engineering, and structures, which are presented in numerous high-quality papers by worldwide researchers, practitioners, policymakers, and entrepreneurs at the 6th CIGOS event, 2021. Moreover, by sharing knowledge and experiences around emerging GI technologies and policy issues, the

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab
  • Author : William Menke,Joshua Ephraim Menke
  • Publisher : Elsevier
  • Release : 18 May 2022
GET THIS BOOKEnvironmental Data Analysis with MatLab

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

Research Handbook on Big Data Law

Research Handbook on Big Data Law
  • Author : Roland Vogl
  • Publisher : Edward Elgar Publishing
  • Release : 28 May 2021
GET THIS BOOKResearch Handbook on Big Data Law

This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains.

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19
  • Author : Victor Chang,Mohamed Abdel-Basset,Muthu Ramachandran,Nicolas Green,Gary Wills
  • Publisher : Academic Press
  • Release : 05 April 2022
GET THIS BOOKNovel AI and Data Science Advancements for Sustainability in the Era of COVID-19

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug

Data Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry
  • Author : Ali Soofastaei
  • Publisher : CRC Press
  • Release : 12 November 2020
GET THIS BOOKData Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at

Sustainability Metrics and Indicators of Environmental Impact

Sustainability Metrics and Indicators of Environmental Impact
  • Author : Eduardo Jacob-Lopes,Leila Queiroz Zepka,Mariany Costa Depra
  • Publisher : Elsevier
  • Release : 16 July 2021
GET THIS BOOKSustainability Metrics and Indicators of Environmental Impact

Sustainability Metrics and Indicators of Environmental Impact: Industrial and Agricultural Life Cycle Assessment covers trending topics on the environmental impact of systems of production, putting emphasis on lifecycle assessment (LCA). This methodology is one of the most important tools of analysis, as mathematical models are applied that will quantify the systematic inputs and outputs of the processes in order to evaluate the sustainability of industrial processes and products. In this sense, LCA is mainly a tool to support environmental decision-making

Computers in Earth and Environmental Sciences

Computers in Earth and Environmental Sciences
  • Author : Hamid Reza Pourghasemi
  • Publisher : Elsevier
  • Release : 22 September 2021
GET THIS BOOKComputers in Earth and Environmental Sciences

Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications

Big Data Science and Analytics for Smart Sustainable Urbanism

Big Data Science and Analytics for Smart Sustainable Urbanism
  • Author : Simon Elias Bibri
  • Publisher : Springer
  • Release : 30 May 2019
GET THIS BOOKBig Data Science and Analytics for Smart Sustainable Urbanism

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

Data Science Without Makeup

Data Science Without Makeup
  • Author : Mikhail Zhilkin
  • Publisher : CRC Press
  • Release : 02 November 2021
GET THIS BOOKData Science Without Makeup

Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has

Methods in Sustainability Science

Methods in Sustainability Science
  • Author : Jingzheng Ren
  • Publisher : Elsevier
  • Release : 05 August 2021
GET THIS BOOKMethods in Sustainability Science

Methods in Sustainability Science: Assessment, Prioritization, Improvement, Design and Optimization presents cutting edge, detailed methodologies needed to create sustainable growth in any field or industry, including life cycle assessments, building design, and energy systems. The book utilized a systematic structured approach to each of the methodologies described in an interdisciplinary way to ensure the methodologies are applicable in the real world, including case studies to demonstrate the methods. The chapters are written by a global team of authors in a

Routledge Handbook of the History of Sustainability

Routledge Handbook of the History of Sustainability
  • Author : Jeremy L. Caradonna
  • Publisher : Routledge
  • Release : 18 October 2017
GET THIS BOOKRoutledge Handbook of the History of Sustainability

The Routledge Handbook of the History of Sustainability is a far-reaching survey of the deep and contemporary history of sustainability. This innovative resource will help to define the history of sustainability as an identifiable field. It provides a unique resource for advanced undergraduates, graduate students, and scholars, and delivers essential context for understanding the current state and future path of the sustainability movement. The history of sustainability is an increasingly important domain within the discipline of history, which draws on

Statistical Data Analysis Explained

Statistical Data Analysis Explained
  • Author : Clemens Reimann,Peter Filzmoser,Robert Garrett,Rudolf Dutter
  • Publisher : John Wiley & Sons
  • Release : 31 August 2011
GET THIS BOOKStatistical Data Analysis Explained

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use