Introduction to Environmental Modeling and R

AvDavid Courard-Hauri

Inbunden, Engelska, 2026

1 465 kr

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Häftad

670 kr

Beskrivning

R is fast becoming ubiquitous in the environmental sciences to analyse data. This book introduces environmental modeling and R. It assumes no background in either coding or calculus. It offers real-world examples, fully described programs, and detailed exercises. Readers learn how to analyse large datasets, create beautiful images, thoughtfully utilize the benefits of AI, and use techniques like optimization and sensitivity analysis in their modelling of complex environmental systems. Using examples from a range of environmental topics - including ecology, conservation, and climate science - the book will interest readers from a broad range of environmental and conservation sciences. Most graduate programs in environmental science and sustainability use R because it is both open source and powerful. R is common in government and consulting work, so students that go on to more advanced environmental modelling courses and potentially careers in the environmental field will find a grounding in R very useful.

Produktinformation

Utgivningsdatum:2026-05-21
Mått:203 x 254 x 18 mm
Vikt:890 g
Format:Inbunden
Språk:Engelska
Antal sidor:302
Förlag:Cambridge University Press
ISBN:9781009674973

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Mer om författaren

David Courard-Hauri has taught environmental modeling and other quantitative and policy-related courses at Drake University for twenty-five years. He has Ph.D. in Physical Chemistry from Stanford and an MPA with an Economics focus from Princeton, His research spreads across a variety of topics, including chemical dynamics, bacterial and butterfly movement, carbon sequestration, and consumer behavior. He co-authored a series of environmental science textbooks with Andrew Friedland and Rick Relyea.

Innehållsförteckning

1. Introduction; 2. The hardest part of coding; 3. Getting started with R; 4. Numbers, units, and equations; 5. Data structures; 6. Introduction to dynamic models; 7. Exponential growth and loops; 8. Working with steady states; 9. Functions; 10. Phase space and optimization; 11. Files; 12. Data manipulation and visualization; 13. Continuous time; 14. Spatial variation; 15. Adding complexity and speed; 16. Radiative balance and using AI; 17. Delays and conveyors; 18. Sensitivity analysis and Monte Carlo; 19. Further reading; References; Index.