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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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
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
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