Издательство Elsevier, 2014, -372 pp.This book is an aggregation of principles, methods, codes, and applications for the data mining and knowledge discovery in geosciences based on the author’s studies over the past 17 years. In the past 20 years, the field of data mining has seen an enormous success in terms of both wide-ranging applications and scientific methodologies. Data mining is the computerized process of extracting previously unknown and important actionable information and knowledge from large databases. Such knowledge can then be used to make crucial decisions by incorporating individuals’ intuition and experience so as to objectively generate for decision makers informed options that might otherwise go undiscovered. So, data mining is also called knowledge discovery in database, and it has been widely applied in many fields of economics, science, and technology. However, data mining applications to geosciences are still at an initial stage, partly due to the multidisciplinary nature and complexity of geosciences and partly due to the fact that many new methods in data mining require time and well-tested case studies in geosciences. Facing the challenges of large amounts of geosciences databases, geoscientists can use database management systems to conduct conventional applications (such as queries, searches, and simple statistical analysis), but they cannot obtain the available knowledge inherent in data by such methods, leading to a paradoxical scenario of rich data but poor knowledge. The true solution is to apply data mining techniques in geosciences databases and modify such techniques to suit practical applications in geosciences. This book, Data Mining and Knowledge Discovery for Geoscientists, is a timely attempt to summarize the latest developments in data mining for geosciences. This book introduces some successful applications of data mining in geosciences in recent years for knowledge discovery in geosciences. It systematically introduces to geoscientists the widely used algorithms and discusses their basic principles, conditions of applications, and diversity of case studies as well as describing what algorithm may be suitable for a specific application. This book focuses on eight categories of algorithm: (1) probability and statistics, (2) artificial neural networks, (3) support vector machines, (4) decision trees, (5) Bayesian classification, (6) cluster analysis, (7) Kriging method, and (8) other soft computing algorithms, including fuzzy mathematics, gray systems, fractal geometry, and linear programming.Introduction Probability and Statistics Artificial Neural Networks Support Vector Machines Decision Trees Bayesian Classification Cluster Analysis Kriging Other Soft Computing Algorithms for Geosciences A Practical Software System of Data Mining and Knowledge Discovery for Geosciences Table of Unit Conversion∗ Answers to Exercises
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2nd Edition. — NY: CRC Press, 2014. 584 p. — (Monographs on Statistics and Applied Probability) — ISBN: 1439819173.
Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the...
Монография, Springer-Verlag, Berlin, 2010. — 828 p. — ISBN: 978-3-642-03646-0.
GI Software Tools
Spatial Statistics in ArcGIS
Spatial Statistics in SAS
Spatial Econometric Functions in R
GeoDa: An Introduction to Spatial Data Analysis
STARS: Space-Time Analysis of Regional Systems
Space-Time Intelligence System Software for the Analysis of...
John Wiley & Sons, Inc., 2013. — 672 p. — 3rd edition. — ISBN: 1118159802.
На англ. языке.
Introducing Geographic Information Systems with ArcGISintegrates a broad introduction to GIS with a software-specific workbook for Esri's ArcGIS. Where most courses make do using two separate texts, one covering GIS and another the software, this book enables students and...
Springer, 2015. — 368 p.
While there are many packages currently available that can be used for geospatial analysis, this work focuses on a specific subset of them. This is due either to their unique functionality that they provide or the fact that they underpin other software packages. For vector and raster data processing, they are:
CRC Press. Taylor & Francis Group. 2014. - 565 p.
This book focuses on remote sensing systems, algorithms, and their applications for evaluation of natural resources, especially in the areas of sampling design, land use and land cover (LULC) characterization and classification, natural landscape and ecosystem assessment, forestry and agriculture mapping, biomass and carbon cycle...
Книга содержит следующие разделы: введение в ГИС анализ; анализ местоположения объектов; Анализ распределения числовых показателей; карты плотности; поиск объектов внутри области; анализ окружения; анализ пространственных изменений