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The most commonly used spatial statistical tools are described in detail along with their applications in a range of disciplines, from crime analysis to habitat conservation. GIS users will learn how features are distributed, how to analyze the pattern created by the features, and how to determine the relationships between them. As the tools available through commercial GIS software have grown in sophistication, a need has emerged to instruct users on the best practices of true GIS analysis. In this sequel to the bestselling The ESRI Guide to GIS Analysis , author Andy Mitchell delves into the more advanced realm of spatial measurements and statistics. The premise of The ESRI Guide to GIS Analysis, Volume 2 , targets GIS technology as having been well used as a display and visualization medium but not so widely used as an implement for real analysis. Covering topics that range from identifying patterns and clusters, to analyzing geographic relationships, this book is a valuable resource for GIS users performing complex analysis. • Author: Andy Mitchell • ISBN:9781589481169 • Format:Paperback • Publication Date:2005-07-01
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This is the proceedings of the 11th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2006, held in Hong Kong, August 2006 alongside the Conference on Pattern Recognition, ICPR 2006. 38 revised full papers and 61 revised poster papers are included, together with 4 invited papers covering image analysis, character recognition, bayesian networks, graph-based methods and more. • ISBN:9783540372363 • Format:Paperback • Publication Date:2006-09-01
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In this book a number of novel algorithms for dimension reduction and statistical pattern recognition for both supervised and unsupervised learning tasks have been presented. Several existing pattern classifiers and dimension reduction algorithms are studied. Their limitations and/or weaknesses are considered and accordingly improved techniques are given which overcome several of their shortcomings. Highlights are: i) Survey of basic dimensional reduction tools viz. principal component analysis and linear discriminant analysis are conducted. ii) Development of Fast PCA technique which finds the desired number of leading eigenvectors with much less computational cost. iii) Development of gradient LDA technique for SSS problem. iv) The rotational LDA technique is developed to reduce the overlapping of samples between
the classes. v) A combined classifier using MDC, class-dependent PCA and LDA is presented. vi) The splitting technique initialization is introduced in the local PCA technique. vii) A new perspective of subspace ICA (generalized ICA, where all the components need not be independent) is introduced by developing vector kurtosis (an extension of kurtosis) function. Statistical Models for Pattern Analysis (Paperback)
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Retaining all the material from the second edition and adding substantial new material, this third edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this edition now covers spatio-temporal point patterns. It also incorporates the use of R through several packages dedicated to the analysis of spatial point process data, with code and data sets available online. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences. Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences. This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website. Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences. This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.
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| Author: Adam Grimes | Publisher: Wiley | Publication Date: Jul 03, 2012 | Number of Pages: 480 pages | Language: English | Binding: Hardcover | ISBN-10: 1118115120 | ISBN-13: 9781118115121
