Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.Content:
Chapter 1 Advanced Mapping of Environmental Data: Introduction (pages 1–17): M. Kanevski
Chapter 2 Environmental Monitoring Network Characterization and Clustering (pages 19–46): D. Tuia and M. Kanevski
Chapter 3 Geostatistics: Spatial Predictions and Simulations (pages 47–94): E. Savelieva, V. Demyanov and M. Maignan
Chapter 4 Spatial Data Analysis and Mapping Using Machine Learning Algorithms (pages 95–148): F. Ratle, A. Pozdnoukhov, V. Demyanov, V. Timonin and E. Savelieva
Chapter 5 Advanced Mapping of Environmental Spatial Data: Case Studies (pages 149–246): L. Foresti, A. Pozdnoukhov, M. Kanevski, V. Timonin, E. Savelieva, C. Kaiser, R. Tapia and R. Purves
Chapter 6 Bayesian Maximum Entropy — BME (pages 247–306): G. Christakos