Table Of Content

Computer Science Chapman & Hall/CRC Chapman & Hall/CRC C Data Mining and Knowledge Discovery Series Data Mining and Knowledge Discovery Series o m Going beyond performing simple analyses, researchers involved in p f Computational Intelligent o u the highly dynamic field of computational intelligent data analysis r t design algorithms that solve increasingly complex data problems a S t in changing environments, including economic, environmental, u i Data Analysis for o s and social data. Computational Intelligent Data Analysis for t n a a Sustainable Development presents novel methodologies for i l n Sustainable Development automatically processing these types of data to support rational I a n b decision making for sustainable development. Through numerous t l e case studies and applications, it illustrates important data analysis e l l D i methods, including mathematical optimization, machine learning, g e e signal processing, and temporal and spatial analysis, for quantifying v n and describing sustainable development problems. e t l o D The book shows how modern data analysis can improve the p a research and practical implementation of sustainable development m t a solutions. It first examines how integrated sustainability analysis e A n uniformly measures and reports environmental impacts such as the n t a carbon footprint of global trade. It then addresses climate change, l y biodiversity and wildlife conservation, renewable energy and the s need for smart grids, and economic and sociopolitical sustainability. i s Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that Y u analysts can then use to gain insight into these issues to support , C rational decision making. This volume shows both the data analysis h a w and sustainable development communities how to use intelligent l a data analysis tools to address practical problems and encourages , a n researchers to develop better methods. d S i m o Edited by ff K14261 Ting Yu, Nitesh V. Chawla, and Simeon Simoff K14261_Cover.indd 1 2/22/13 2:45 PM Computational Intelligent Data Analysis for Sustainable Development Chapman & Hall/CRC Data Mining and Knowledge Discovery Series SERIES EDITOR Vipin Kumar University of Minnesota Department of Computer Science and Engineering Minneapolis, Minnesota, U.S.A. AIMS AND SCOPE This series aims to capture new developments and applications in data mining and knowledge discovery, while summarizing the computational tools and techniques useful in data analysis. This series encourages the integration of mathematical, statistical, and computational methods and techniques through the publication of a broad range of textbooks, reference works, and hand- books. The inclusion of concrete examples and applications is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of data mining and knowledge discovery methods and applications, modeling, algorithms, theory and foundations, data and knowledge visualization, data mining systems and tools, and privacy and security issues. PUBLISHED TITLES ADVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, and Ashok N. Srivastava BIOLOGICAL DATA MINING Jake Y. Chen and Stefano Lonardi COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR SUSTAINABLE DEVELOPMENT Ting Yu, Nitesh V. Chawla, and Simeon Simoff COMPUTATIONAL METHODS OF FEATURE SELECTION Huan Liu and Hiroshi Motoda CONSTRAINED CLUSTERING: ADVANCES IN ALGORITHMS, THEORY, AND APPLICATIONS Sugato Basu, Ian Davidson, and Kiri L. Wagstaff CONTRAST DATA MINING: CONCEPTS, ALGORITHMS, AND APPLICATIONS Guozhu Dong and James Bailey DATA CLUSTERING IN C++: AN OBJECT-ORIENTED APPROACH Guojun Gan DATA MINING FOR DESIGN AND MARKETING Yukio Ohsawa and Katsutoshi Yada DATA MINING WITH R: LEARNING WITH CASE STUDIES Luís Torgo FOUNDATIONS OF PREDICTIVE ANALYTICS James Wu and Stephen Coggeshall GEOGRAPHIC DATA MINING AND KNOWLEDGE DISCOVERY, SECOND EDITION Harvey J. Miller and Jiawei Han Chapman & Hall/CRC HANDBOOK OF EDUCATIONAL DATA MINING Cristóbal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan S.J.d. Baker Data Mining and Knowledge Discovery Series INFORMATION DISCOVERY ON ELECTRONIC HEALTH RECORDS Vagelis Hristidis SERIES EDITOR INTELLIGENT TECHNOLOGIES FOR WEB APPLICATIONS Vipin Kumar Priti Srinivas Sajja and Rajendra Akerkar University of Minnesota INTRODUCTION TO PRIVACY-PRESERVING DATA PUBLISHING: Department of Computer Science and Engineering CONCEPTS AND TECHNIQUES Minneapolis, Minnesota, U.S.A. Benjamin C. M. Fung, Ke Wang, Ada Wai-Chee Fu, and Philip S. Yu KNOWLEDGE DISCOVERY FOR COUNTERTERRORISM AND LAW ENFORCEMENT AIMS AND SCOPE David Skillicorn This series aims to capture new developments and applications in data mining and knowledge KNOWLEDGE DISCOVERY FROM DATA STREAMS discovery, while summarizing the computational tools and techniques useful in data analysis. This João Gama series encourages the integration of mathematical, statistical, and computational methods and MACHINE LEARNING AND KNOWLEDGE DISCOVERY FOR techniques through the publication of a broad range of textbooks, reference works, and hand- ENGINEERING SYSTEMS HEALTH MANAGEMENT books. The inclusion of concrete examples and applications is highly encouraged. The scope of the Ashok N. Srivastava and Jiawei Han series includes, but is not limited to, titles in the areas of data mining and knowledge discovery MINING SOFTWARE SPECIFICATIONS: METHODOLOGIES AND APPLICATIONS methods and applications, modeling, algorithms, theory and foundations, data and knowledge David Lo, Siau-Cheng Khoo, Jiawei Han, and Chao Liu visualization, data mining systems and tools, and privacy and security issues. MULTIMEDIA DATA MINING: A SYSTEMATIC INTRODUCTION TO CONCEPTS AND THEORY Zhongfei Zhang and Ruofei Zhang PUBLISHED TITLES MUSIC DATA MINING ADVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY Tao Li, Mitsunori Ogihara, and George Tzanetakis Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, and Ashok N. Srivastava NEXT GENERATION OF DATA MINING BIOLOGICAL DATA MINING Hillol Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani, and Vipin Kumar Jake Y. Chen and Stefano Lonardi RELATIONAL DATA CLUSTERING: MODELS, ALGORITHMS, AND APPLICATIONS COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR SUSTAINABLE DEVELOPMENT Bo Long, Zhongfei Zhang, and Philip S. Yu Ting Yu, Nitesh V. Chawla, and Simeon Simoff SERVICE-ORIENTED DISTRIBUTED KNOWLEDGE DISCOVERY COMPUTATIONAL METHODS OF FEATURE SELECTION Domenico Talia and Paolo Trunfio Huan Liu and Hiroshi Motoda SPECTRAL FEATURE SELECTION FOR DATA MINING CONSTRAINED CLUSTERING: ADVANCES IN ALGORITHMS, THEORY, AND APPLICATIONS Zheng Alan Zhao and Huan Liu Sugato Basu, Ian Davidson, and Kiri L. Wagstaff STATISTICAL DATA MINING USING SAS APPLICATIONS, SECOND EDITION CONTRAST DATA MINING: CONCEPTS, ALGORITHMS, AND APPLICATIONS George Fernandez Guozhu Dong and James Bailey SUPPORT VECTOR MACHINES: OPTIMIZATION BASED THEORY, ALGORITHMS, DATA CLUSTERING IN C++: AN OBJECT-ORIENTED APPROACH AND EXTENSIONS Guojun Gan Naiyang Deng, Yingjie Tian, and Chunhua Zhang DATA MINING FOR DESIGN AND MARKETING TEMPORAL DATA MINING Yukio Ohsawa and Katsutoshi Yada Theophano Mitsa DATA MINING WITH R: LEARNING WITH CASE STUDIES TEXT MINING: CLASSIFICATION, CLUSTERING, AND APPLICATIONS Luís Torgo Ashok N. Srivastava and Mehran Sahami FOUNDATIONS OF PREDICTIVE ANALYTICS THE TOP TEN ALGORITHMS IN DATA MINING James Wu and Stephen Coggeshall Xindong Wu and Vipin Kumar GEOGRAPHIC DATA MINING AND KNOWLEDGE DISCOVERY, SECOND EDITION UNDERSTANDING COMPLEX DATASETS: Harvey J. Miller and Jiawei Han DATA MINING WITH MATRIX DECOMPOSITIONS David Skillicorn Computational Intelligent Data Analysis for Sustainable Development Edited by Ting Yu, Nitesh V. Chawla, and Simeon Simoff MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2013 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20130304 International Standard Book Number-13: 978-1-4398-9595-5 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. 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Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Acknowledgments, xi About the Editors, xiii List of Contributors, xv Chapter 1 ◾ Computational Intelligent Data Analysis for Sustainable Development: An Introduction and Overview 1 ting Yu, nitesh V. Chawla, and simeon simoff seCtion i integrated sustainabilitY analYsis Chapter 2 ◾ Tracing Embodied CO in Trade Using 2 High- Resolution Input–Output Tables 27 daniel moran and arne gesChke Chapter 3 ◾ Aggregation Effects in Carbon Footprint Accounting Using Multi- Region Input–Output Analysis 53 Xin Zhou, hiroaki shirakawa, and manfred lenZen seCtion ii Computational intelligent data analYsis for Climate Change Chapter 4 ◾ Climate Informatics 81 Claire monteleoni, gaVin a. sChmidt, franCis aleXander, aleXandru niCulesCu- miZil, karsten steinhaeuser, miChael tippett, arindam banerjee, m. benno blumenthal, auroop r. gangulY, jason e. smerdon, and marCo tedesCo vii viii ◾ Contents Chapter 5 ◾ Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty 127 auroop r. gangulY, eVan kodra, snigdhansu Chatterjee, arindam banerjee, and habib n. najm seCtion iii Computational intelligent data analYsis for biodiVersitY and speCies ConserVation Chapter 6 ◾ Mathematical Programming Applications to Land Conservation and Environmental Quality 159 jaCob r. fooks and kent d. messer seCtion iV Computational intelligent data analYsis for smart grid and renewable energY Chapter 7 ◾ Data Analysis Challenges in the Future Energy Domain 181 frank eiChinger, daniel pathmaperuma, harald Vogt, and emmanuel müller Chapter 8 ◾ Electricity Supply without Fossil Fuels 243 john boland, peter pudneY, and jerZY filar Chapter 9 ◾ Data Analysis for Real- Time Identification of Grid Disruptions 273 Varun Chandola, olufemi omitaomu, and steVen j. fernandeZ Chapter 10 ◾ Statistical Approaches for Wind Resource Assessment 303 kalYan VeeramaChaneni, Xiang Ye, and una- maY o’reillY seCtion V Computational intelligent data analYsis for soCiopolitiCal sustainabilitY Chapter 11 ◾ Spatio- Temporal Correlations in Criminal Offense Records 331 jameson l. toole, nathan eagle, and joshua b. plotkin