Quantitative 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).
1. 1 Introduction - p. 106 2. 2 CGS of data point information - p. 102 3. 3 CGS of chrono-geographic attribute values - p. 121 4. 4 Chrono-geographic clustering and hotspot (coldspot) analysis - p. 136 5. 5 Practice exercises - p. 144 6. References - p. 148 7. Chapter 5. Classical geostatistics - p. 150 8. Chapter 6. Modern geostatistics - p. 213 9. Abstract - p. 213 10. 1 Toward a theory-driven CTDA - p. 213 11. 2 Knowledge bases revisited - p. 216 12. 3 Integrating lawful and dataful statistics - p. 229 13. 4 Rethinking chronotopologic dependence - p. 243 14. 5 Applications - p. 253 15. 6 Practice exercises - p. 261 16. References - p. 266 17. Chapter 7. Chronotopologic interpolation - p. 285 18. Chapter 8. Chronotopologic krigology - p. 327 19. Abstract - p. 327 20. 1 The emergence of geostatistical Kriging - p. 327 21. 2 1st Kriging classification - p. 299 22. 3 Second Kriging classification: point, chronoblock and functional - p. 320 23. 4 Mapping accuracy indicators and cross-validation tests - p. 323 24. 5 Applied krigology: benefits and concerns - p. 339 25. 6 Practice exercises - p. 341 26. References - p. 344 27. Chapter 9. Chronotopologic BME estimation - p. 379 28. Chapter 10. Studying physical laws - p. 385 29. CTDA by dimensionality reduction - p. 407 30. Chapter 12. DIA models - p. 431 31. Syntheses of CTDA techniques with DIA models - p. 449 32. Index - p. 485
Quantitative 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).
1. 1 Introduction - p. 106 2. 2 CGS of data point information - p. 102 3. 3 CGS of chrono-geographic attribute values - p. 121 4. 4 Chrono-geographic clustering and hotspot (coldspot) analysis - p. 136 5. 5 Practice exercises - p. 144 6. References - p. 148 7. Chapter 5. Classical geostatistics - p. 150 8. Chapter 6. Modern geostatistics - p. 213 9. Abstract - p. 213 10. 1 Toward a theory-driven CTDA - p. 213 11. 2 Knowledge bases revisited - p. 216 12. 3 Integrating lawful and dataful statistics - p. 229 13. 4 Rethinking chronotopologic dependence - p. 243 14. 5 Applications - p. 253 15. 6 Practice exercises - p. 261 16. References - p. 266 17. Chapter 7. Chronotopologic interpolation - p. 285 18. Chapter 8. Chronotopologic krigology - p. 327 19. Abstract - p. 327 20. 1 The emergence of geostatistical Kriging - p. 327 21. 2 1st Kriging classification - p. 299 22. 3 Second Kriging classification: point, chronoblock and functional - p. 320 23. 4 Mapping accuracy indicators and cross-validation tests - p. 323 24. 5 Applied krigology: benefits and concerns - p. 339 25. 6 Practice exercises - p. 341 26. References - p. 344 27. Chapter 9. Chronotopologic BME estimation - p. 379 28. Chapter 10. Studying physical laws - p. 385 29. CTDA by dimensionality reduction - p. 407 30. Chapter 12. DIA models - p. 431 31. Syntheses of CTDA techniques with DIA models - p. 449 32. Index - p. 485
