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E-raamat: Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations

(Professor, Zhejiang University, China), (Research Associate, Zhejiang University, China), (Professor, San Diego State University, USA and Zhejiang University, China)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 04-Dec-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780128163429
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Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data ConsiderationsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 04-Dec-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780128163429
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Quantitative Analysis and Modeling of Earth and Environmental Data: Applications for Spatial and Temporal Variation offers a systematic, quantitative analysis of multi-sourced data, including the spatial distribution and temporal dynamics of natural attributes. It covers data handling techniques that may vary by space and/or time, and aims to improve understanding of physical laws of change underlying available numerical datasets, while also considering in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). Featuring real-world practical applications and practice exercises, this book is a comprehensive step-by-step tutorial of data-driven techniques that will help students and researchers master data analysis in earth and environmental sciences.

The notions and methods presented in the book 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).

  • Addresses the analysis and processing data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines
  • 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
Preface ix
1 Chronotopologic data analysis
1 From topos to chronotopos
1(8)
2 Chronotopologic variability, dependency and uncertainty
9(9)
3 Theory and evidence
18(4)
4 Chronotopologic estimation and mapping
22(2)
5 A review of CTDA techniques
24(2)
6 Chronotopologic visualization technology
26(1)
7 The range of CTDA applications
27(1)
8 Public domain software libraries
28(2)
9 Practice exercises
30(3)
2 Chronotopology theory
1 Introduction
33(2)
2 Basic chronotopologic notions
35(12)
3 Chronotopologic metric modeling
47(5)
4 Metric effects on chronotopologic attribute interpolation
52(3)
5 Practice exercises
55(2)
3 CTDA methodology
1 Methodologic chain
57(9)
2 About knowledge
66(15)
3 Big data: Why learn, if you can look it up?
81(7)
4 Attribute data scales
88(4)
5 Emergence of chronotopology-dependent statistics
92(4)
6 More on chronotopologic visualization
96(2)
7 Practice exercises
98(3)
4 Chrono-geographic statistics
1 Introduction
101(1)
2 CGS of data point information
102(19)
3 CGS of chrono-geographic attribute values
121(15)
4 Chrono-geographic clustering and hotspot (coldspot) analysis
136(9)
5 Practice exercises
145(4)
5 Classical geostatistics
1 Historical introduction
149(6)
2 Random field theory
155(10)
3 Covariography and variography
165(35)
4 Chronotopologic block data analysis
200(6)
5 Practice exercises
206(7)
6 Modern geostatistics
1 Toward a theory-driven CTDA
213(3)
2 Knowledge bases revisited
216(13)
3 Integrating lawful and dataful statistics
229(14)
4 Rethinking chronotopologic dependence
243(10)
5 Applications
253(8)
6 Practice exercises
261(6)
7 Chronotopologic interpolation
1 Introduction
267(6)
2 Deterministic chronotopologic interpolation techniques
273(9)
3 Statistical chronotopologic interpolation techniques
282(9)
4 Practice exercises
291(2)
8 Chronotopologic krigology
1 The emergence of geostatistical Kriging
293(6)
2 1st Kriging classification
299(21)
3 Second Kriging classification: point, chronoblock and functional
320(3)
4 Mapping accuracy indicators and cross-validation tests
323(16)
5 Applied krigology: benefits and concerns
339(2)
6 Practice exercises
341(4)
9 Chronotopologic BME estimation
1 Epistemic underpinnings
345(1)
2 Mathematical developments
346(10)
3 An overview of real world BME case studies
356(21)
4 Practice exercises
377(8)
10 Studying physical laws
1 The important role of physical PDE in CTDA
385(4)
2 BME solution of a physical law
389(8)
3 BME solution of an epidemic law
397(5)
4 Comparing core and specificatory probabilities
402(3)
5 Practice exercises
405(2)
11 CTDA by dimensionality reduction
1 The motivation
407(1)
2 The space-time projection (STP) method
408(19)
3 Noteworthy STP features
427(1)
4 Practice exercises
428(3)
12 DIA models
1 Introduction
431(2)
2 Machine learning
433(1)
3 Linear regression techniques
434(4)
4 Artificial neural network
438(6)
5 Practice exercises
444(5)
13 Syntheses of CTDA techniques with DIA models
1 A broad synthesis perspective
449(3)
2 A synthesis of the STP and BME techniques
452(7)
3 A synthesis of the STP-BME technique with the LUR and ANN models
459(5)
4 A synthesis of the BME technique with the MLR and GWR models
464(7)
5 Epilogue
471(1)
6 Practice exercises
472(5)
References 477(8)
Index 485
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. 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. 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.
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