Advanced mapping of environmental data. Geostatistics, machine learning and bayesian maximum entropy / Расширенное картографирование экологических данных. Геостатистика, машинное обучение и байесовская система максимальной энтропии

In this introductory chapter we describe general problems of spatial environmental data analysis, modeling, validation and visualization. Many of these problems are considered in detail in the following chapters using geostatistical models, machine learning algorithms (MLA) of neural networks and Support Vector Machines, and the Bayesian Maximum Entropy (BME) approach. The term “mapping” in the book is considered not only as an interpolation in two- or threedimensional geographical space, but in a more general sense of estimating the desired dependencies from empirical data. The references presented at the end of this chapter cover the range of books and papers important both for beginners and advanced researchers. The list contains both classical textbooks and studies on contemporary cutting-edge research topics in data analysis. In general, mapping can be considered as: a) a spatiotemporal classification problem such as digital soil mapping and geological unit classification, b) a regression problem such as mapping of pollution and topo-climatic modeling, and c) a problem of probability density modeling, which is not a mapping of values but “mapping” of probability density functions, i.e., the local or joint spatial distributions conditioned on data and available expert knowledge. <...>