Spatial Statistics and Modeling
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environmental and earth sciences, epidemiology, image analysis and more. This book covers the best-known spatial models for three types of spatial data: geostatistica
- PDF / 8,484,855 Bytes
- 308 Pages / 462.034 x 750.014 pts Page_size
- 111 Downloads / 321 Views
Carlo Gaetan Xavier Guyon
Spatial Statistics and Modeling
Springer Series in Statistics Advisors: P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, S. Zeger
For other titles published in this series, go to www.springer.com/series/692
Carlo Gaetan · Xavier Guyon
Spatial Statistics and Modeling
Translated by Kevin Bleakley
123
Carlo Gaetan Dipartimento di Statistica Universit`a Ca’ Foscari Venezia San Giobbe - Cannaregio, 873 30121 Venezia VE ltaly [email protected]
Xavier Guyon SAMOS Universit´e Paris I 90 rue de Tolbiac 75634 Paris CX 13 France [email protected]
ISBN 978-0-387-92256-0 e-ISBN 978-0-387-92257-7 DOI 10.1007/978-0-387-92257-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009938269 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Spatial analysis methods have seen a rapid rise in popularity due to demand from a wide range of fields. These include, among others, biology, spatial economics, image processing, environmental and earth science, ecology, geography, epidemiology, agronomy, forestry and mineral prospection. In spatial problems, observations come from a spatial process X = {Xs , s ∈ S} indexed by a spatial set S, with Xs taking values in a state space E. The positions of observation sites s ∈ S are either fixed in advance or random. Classically, S is a 2-dimensional subset, S ⊆ R2 . However, it could also be 1-dimensional (chromatography, crop trials along rows) or a subset of R3 (mineral prospection, earth science, 3D imaging). Other fields such as Bayesian statistics and simulation may even require spaces S of dimension d ≥ 3. The study of spatial dynamics adds a temporal dimension, for example (s,t) ∈ R2 ×R+ in the 2-dimensional case. This multitude of situations and applications makes for a very rich subject. To illustrate, let us give a few examples of the three types of spatial data that will be studied in the book.
Geostatistical data Here, S is a continuous subspace of Rd and the random field {Xs , s ∈ S} observed at n fixed sites {s1 , . . . , sn } ⊂ S takes values in a real-valued state space E. The rainfall data in Figure 0.1-a and soil porosity data in Fig. 0.1-b fall into this category. Observation sites may
Data Loading...
