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British Wildlife is the leading natural history magazine in the UK, providing essential reading for both enthusiast and professional naturalists and wildlife conservationists. Published six times a year, British Wildlife bridges the gap between popular writing and scientific literature through a combination of long-form articles, regular columns and reports, book reviews and letters.

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Conservation Land Management (CLM) is a quarterly magazine that is widely regarded as essential reading for all who are involved in land management for nature conservation, across the British Isles. CLM includes long-form articles, events listings, publication reviews, new product information and updates, reports of conferences and letters.

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Academic & Professional Books  Reference  Data Analysis & Modelling  Cartography, Remote Sensing, Image Analysis & GIS

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

Handbook / Manual
By: Mikhail Kanevski(Editor)
313 pages, b/w illustrations, b/w maps
Advanced Mapping of Environmental Data
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  • Advanced Mapping of Environmental Data ISBN: 9781848210608 Hardback Aug 2008 Usually dispatched within 4 days
    £146.00
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About this book Contents Customer reviews Biography Related titles

About this book

Advanced Mapping of Environmental Data combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, Advanced Mapping of Environmental Data 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.

Contents

1. Model dependent (geostatistics) and data driven (machine learning algorithms)
2. Environmental spatial data.Monitoring networks quantification. Spatial patterns
3. Geostatistics. Spatial predictions and simulations. Linear models. Family of kriging models with illustrations. Nonlinear models. Risk mapping. Indicator kriging. Conditional stochastic simulations. Descriptions of spatial uncertainty and variability
4. Machine learning algorithms. Principles of learning. Learning from environmental spatial data. Posing of classification and regression problems. Artificial neural networks (ANN) for spatial data. Basic ANN models (theory and illustrative examples). Statistical learning theory for spatial data. Concepts and examples
5. Case studies: Geostatistics and machine learning. Classification problems. Regression problems
6. Bayesian maximum entropy (BME)

Customer Reviews

Biography

Mikhail Kanevski, Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland

Handbook / Manual
By: Mikhail Kanevski(Editor)
313 pages, b/w illustrations, b/w maps
Media reviews

"It gives a good overview, is clearly written, is concise, and includes many references to papers published in the different areas."
- Zentralblatt MATH, 2011

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