Computational intelligent data analysis for sustainable development

443 Pages·2013·15.592 MB·English

by  Ting Yu

Computer Science
Chapman & Hall/CRC Chapman & Hall/CRC
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Data Mining and Knowledge Discovery Series Data Mining and Knowledge Discovery Series
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Going beyond performing simple analyses, researchers involved in p
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the highly dynamic field of computational intelligent data analysis
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design algorithms that solve increasingly complex data problems a
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in changing environments, including economic, environmental, u i Data Analysis for
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and social data. Computational Intelligent Data Analysis for t n
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Sustainable Development presents novel methodologies for i l
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automatically processing these types of data to support rational I
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decision making for sustainable development. Through numerous t
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case studies and applications, it illustrates important data analysis e l
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methods, including mathematical optimization, machine learning, g
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signal processing, and temporal and spatial analysis, for quantifying
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and describing sustainable development problems. e t
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The book shows how modern data analysis can improve the p a
research and practical implementation of sustainable development m t
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solutions. It first examines how integrated sustainability analysis e
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uniformly measures and reports environmental impacts such as the n
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carbon footprint of global trade. It then addresses climate change,
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biodiversity and wildlife conservation, renewable energy and the
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need for smart grids, and economic and sociopolitical sustainability. i
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Sustainable development problems, such as global warming,
resource shortages, global species loss, and pollution, push
researchers to create powerful data analysis approaches that
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analysts can then use to gain insight into these issues to support , C
rational decision making. This volume shows both the data analysis h
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and sustainable development communities how to use intelligent
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data analysis tools to address practical problems and encourages , a
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K14261 Ting Yu, Nitesh V. Chawla, and Simeon Simoff
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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
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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