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Quantitative Analysis and Modeling of Earth and Environmental Data
Space-Time and Spacetime Data Considerations
- 1st Edition - December 3, 2021
- Authors: Jiaping Wu, Junyu He, George Christakos
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 6 3 4 1 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 6 3 4 2 - 9
Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offe… Read more
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Request a sales quoteQuantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other.
The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications).
- Explores the analysis and processing of chronotopologic (i.e., space-time and spacetime) data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines
- Studies the synthesis of scientific theory and empirical evidence (in its various forms) that offers a mathematically rigorous and physically meaningful assessment of real-world phenomena
- Covers a wide range of data describing a variety of attributes characterizing physical phenomena and systems including earth, ocean and atmospheric variables, environmental and ecological parameters, population health states, disease indicators, and social and economic characteristics
- Includes case studies and practice exercises at the end of each chapter for both real-world applications and deeper understanding of the concepts presented
Graduate students in Earth and Environmental Science, Geography, GIS, and data analysis; GIS scientists, Earth Scientists, geographers
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- Chapter 1: Chronotopologic data analysis
- Abstract
- 1: From topos to chronotopos
- 2: Chronotopologic variability, dependency and uncertainty
- 3: Theory and evidence
- 4: Chronotopologic estimation and mapping
- 5: A review of CTDA techniques
- 6: Chronotopologic visualization technology
- 7: The range of CTDA applications
- 8: Public domain software libraries
- 9: Practice exercises
- Chapter 2: Chronotopology theory
- Abstract
- 1: Introduction
- 2: Basic chronotopologic notions
- 3: Chronotopologic metric modeling
- 4: Metric effects on chronotopologic attribute interpolation
- 5: Practice exercises
- Chapter 3: CTDA methodology
- Abstract
- 1: Methodologic chain
- 2: About knowledge
- 3: Big data: Why learn, if you can look it up?
- 4: Attribute data scales
- 5: Emergence of chronotopology-dependent statistics
- 6: More on chronotopologic visualization
- 7: Practice exercises
- Chapter 4: Chrono-geographic statistics
- Abstract
- 1: Introduction
- 2: CGS of data point information
- 3: CGS of chrono-geographic attribute values
- 4: Chrono-geographic clustering and hotspot (coldspot) analysis
- 5: Practice exercises
- Chapter 5: Classical geostatistics
- Abstract
- 1: Historical introduction
- 2: Random field theory
- 3: Covariography and variography
- 4: Chronotopologic block data analysis
- 5: Practice exercises
- Chapter 6: Modern geostatistics
- Abstract
- 1: Toward a theory-driven CTDA
- 2: Knowledge bases revisited
- 3: Integrating lawful and dataful statistics
- 4: Rethinking chronotopologic dependence
- 5: Applications
- 6: Practice exercises
- Chapter 7: Chronotopologic interpolation
- Abstract
- 1: Introduction
- 2: Deterministic chronotopologic interpolation techniques
- 3: Statistical chronotopologic interpolation techniques
- 4: Practice exercises
- Chapter 8: Chronotopologic krigology
- Abstract
- 1: The emergence of geostatistical Kriging
- 2: 1st Kriging classification
- 3: Second Kriging classification: point, chronoblock and functional
- 4: Mapping accuracy indicators and cross-validation tests
- 5: Applied krigology: benefits and concerns
- 6: Practice exercises
- Chapter 9: Chronotopologic BME estimation
- Abstract
- 1: Epistemic underpinnings
- 2: Mathematical developments
- 3: An overview of real world BME case studies
- 4: Practice exercises
- Chapter 10: Studying physical laws
- Abstract
- 1: The important role of physical PDE in CTDA
- 2: BME solution of a physical law
- 3: BME solution of an epidemic law
- 4: Comparing core and specificatory probabilities
- 5: Practice exercises
- Chapter 11: CTDA by dimensionality reduction
- Abstract
- 1: The motivation
- 2: The space-time projection (STP) method
- 3: Noteworthy STP features
- 4: Practice exercises
- Chapter 12: DIA models
- Abstract
- 1: Introduction
- 2: Machine learning
- 3: Linear regression techniques
- 4: Artificial neural network
- 5: Practice exercises
- Chapter 13: Syntheses of CTDA techniques with DIA models
- Abstract
- 1: A broad synthesis perspective
- 2: A synthesis of the STP and BME techniques
- 3: A synthesis of the STP-BME technique with the LUR and ANN models
- 4: A synthesis of the BME technique with the MLR and GWR models
- 5: Epilogue
- 6: Practice exercises
- References
- Index
- No. of pages: 502
- Language: English
- Edition: 1
- Published: December 3, 2021
- Imprint: Elsevier
- Paperback ISBN: 9780128163412
- eBook ISBN: 9780128163429
JW
Jiaping Wu
Dr. Jiaping Wu is Director of the Institute of Islands and Coastal Ecosystems at Ocean College, Zhejiang University. His research interests include remote sensing of the environment and space-time data analysis. He has written over 70 journal articles on topics related to data analysis in the environment.
Affiliations and expertise
Professor, Zhejiang University, ChinaJH
Junyu He
Junyu He is Professor at the Institute of Island and Coastal Ecosystems at Ocean College,Zhejiang University. His research interests include geostatistics, environmental modeling, and risk analysis. His PhD dissertation was specifically on quantitative analysis and modeling of data with spatial variation and temporal dynamics.
Affiliations and expertise
Research Associate, Zhejiang University, ChinaGC
George Christakos
George Christakos is a Professor in the Department of Geography at San Diego State University (USA) and in the Institute of Island & Coastal Ecosystems, Ocean College at Zhejiang University (China). He is an expert in spatiotemporal random field modeling of natural systems, and his teaching and research focus on the integrative analysis of natural phenomena; spatiotemporal random field theory; uncertainty assessment; pollution monitoring and control; human exposure risk and environmental health; space-time statistics and geostatistics.
Affiliations and expertise
Professor, San Diego State University, USA and Zhejiang University, ChinaRead Quantitative Analysis and Modeling of Earth and Environmental Data on ScienceDirect