Spatial Cluster Modelling / Edition 1

by Andrew B. Lawson, David G.T. Denison

Overview

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research.

In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal cluster modelling.

Many figures, some in full color, complement the text, and a single section of references cited makes it easy to locate source material. Leading specialists in the field of cluster modelling authored each chapter, and an introduction by the editors to each chapter provides a cohesion not typically found in contributed works. Spatial Cluster Modelling thus offers a singular opportunity to explore this exciting new field, understand its techniques, and apply them in your own research.

Product Details

ISBN-13:	9780367454913
Publisher:	Taylor & Francis
Publication date:	01/29/2020
Pages:	308
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Andrew B. Lawson, David G.T. Denison

Table of Contents

Spatial Cluster Modelling: An Overview. POINT PROCESS CLUSTER MODELLING: Significance in Scale-Space for Clustering. Statistical Inference for Cox Processes. Extrapolating and Interpolating Spatial Patterns. Perfect Sampling for Point Process Cluster Modelling. Bayesian Estimation and Segmentation of Spatial Point Processes Using Voronoi Tilings. SPATIAL PROCESS CLUSTER MODELLING: Partition Modelling. Cluster Modelling for Disease Rate Mapping. Analyzing Spatial Data Using Skew-Gaussian Processes. Accounting for Absorption Lines In Images Obtained With The Chandra X-Ray Observatory. Spatial Modelling of Count Data: A Case Study in Modelling Breeding Bird Survey Data on Large Spatial Domains. SPATIO-TEMPORAL CLUSTER MODELLING: Modelling Strategies for Spatial-Temporal Data. Spatio-Temporal Partition Modelling: An Example FROM Neurophysiology. Spatio-Temporal Cluster Modelling of Small Area Health Data. References. Index. Author Index.

Editorial Reviews

"This text provides an effective treatment and review of several ways to view a clustering pattern, depending on the context. Examples include image segmentation, spatial epidemiology, and object recognition using partition models. … Each of the 14 chapters has multiple authors, each aware of the book's content so there is effective cross-referencing. I strongly recommend this book for anybody who is serious about spatial clustering. …"
-Tom Burr Statistics in Medicine, Vol. 23, 2004

"[This book] is a collection of contributions by leading specialist in the field, which are brought together coherently with unified notation. … Overall, the book is an excellent, well and up-to-date referenced presentation of the current state of research in spatial cluster analysis … an insightful reference not only for the statistician, but also for scientists … ."
-Zentralblatt MATH, 1046

"The chapter authors are all recognized for their excellence in research. … the text is well written and informative, and is a worthy addition to the library of anyone wishing to keep up to date on current research in spatial cluster modeling."
-Journal of the American Statistical Association, Vol. 99, No. 467, September 2004

From the Publisher

Reviews the diverse approaches to the analysis and statistical modeling of clusters and clustering in space. The 14 papers explore point and object process modeling, spatial process modeling, and the burgeoning area of spatio-temporal modeling. Topics include statistical inference for Cox processes, Bayesian estimation and segmentation of spatial point processes using Voronoi tilings, accounting for absorption lines in images obtained with the Chandra X-ray Observatory, partition modeling, and the application of spatio- temporal cluster modeling to the analysis of health data. Annotation c. Book News, Inc., Portland, OR (booknews.com)