Jin Li

Spatial Predictive Modeling with R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The book is designed to be very accessible, with a focus on methods, examples, and computing, and theoretical details kept to an absolute minimum. It could be used as a reference for researchers and practitioners in industry. It could also be used as a course text for graduate students of spatial statistics.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 404
• Erscheinungstermin: 22. Februar 2022
• Englisch
• ISBN-13: 9781000542639
• Artikelnr.: 63274092

Autorenporträt

Dr Jin Li works at Data2action, Australia as a Founder. He has research experience in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology. As a scientist, he worked in the Chinese Academy of Sciences, University of New England, CSIRO, and Geoscience Australia. He was an Associate Editor (Jul 2008-Dec 2015) and an editorial board member (Jan 2016-April 2020) of Acta Oecologica, and a Guest Academic Editor (Mar 2018) and an Academic Editor (May 2018-Apr 2020) of PLOS ONE. He has produced over 100 various publications, developed a number of hybrid methods for spatial predictive modeling, and published four R packages for variable selections and spatial predictive modelling.
For further information see https://www.researchgate.net/profile/Jin-Li-74, https://scholar.google.com/citations?user=Jeot53EAAAAJ&hl=en and https://www.linkedin.com/in/jin-li-01421a68/.

Inhaltsangabe

1. Data acquisition, data quality control and spatial reference systems
2. Predictive variables and exploratory analysis
3. Model evaluation and validation
4. Mathematical spatial interpolation methods
5. Univariate geostatistical methods
6. Multivariate geostatistical methods
7. Modern statistical methods
8. Tree-based machine learning methods
9. Support vector machine
10. Hybrids of modern statistical methods with mathematical and univariate
geostatistical methods
11. Hybrids of machine learning methods with mathematical and univariate
geostatistical methods
12. Applications and comparisons of spatial predictive methods
Appendix A. Data sets used in this book