Perspectives on Spatial Data Analysis - Luc Anselin

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Résumé :
Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis' contributions: . Spatial analysis . Pattern analysis . Local statistics . Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications.

Sommaire:
Spatial Analysis.- Perspectives on Spatial Data Analysis.- Spatial Interaction and Spatial Autocorrelation: A Cross-Product Approach.- Spatial Statistical Analysis and Geographic Information Systems.- Whose Hand on the Tiller? Revisiting Spatial Statistical Analysis and GIS.- Spatial Interaction and Spatial Autocorrelation.- Pattern Analysis.- Second-Order Analysis of Point Patterns: The Case of Chicago as a Multi-center Urban Region.- Second-Order Neighborhood Analysis of Mapped Point Patterns.- A Class of Local and Global K Functions and Their Exact Statistical Methods.- Spatial Point Pattern Analysis of Plants.- Local Statistics.- The Analysis of Spatial Association by Use of Distance Statistics.- Constructing the Spatial Weights Matrix Using a Local Statistic.- Spatial Autocorrelation: A Statistician's Reflections.- Health Surveillance Around Prespecified Locations Using Case-Control Data.- Applications.- Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures.- Characteristics of the Spatial Pattern of the Dengue Vector, Aedes aegypti, in Iquitos, Peru.- Spatial Filtering and Missing Georeferenced Data Imputation: A Comparison of the Getis and Griffith Methods.- Spatial Patterns of Fertility in Rural Egypt.

From the reviews:

This book belongs is 'an informed readership in universities, research organisations, and policy-making institutions throughout the world'. ... this volume, with its retrospective flavour, presents a friendlier face. ... provides a more accessible context for the demanding papers because many key ideas are introduced and developed from first principles in the original papers by Gertis. ... the book is 'ideal for advanced seminars and courses'. ... provide useful additional reading for an advanced class in spatial analysis. (David O'Sullivan, Environment and Planning B: Planning and Design, Vol. 38, 2011)

The book is a tribute to the methodological contributions of Professor Arthur Getis in spatial and point pattern analysis and the development of local statistics. ... provide researchers and advanced students a historical background of Professor Getis's impact on modern spatial and point pattern data analysis, as well as new directions, including data driven approaches for specifying neighbours, spatial filtering models, and spatial interaction modelling. ... This volume will be most appreciated by applied researchers and advanced students of spatial data analysis. (Dayton M. Lambert, Papers in Regional Science, Vol. 91 (1), March, 2012)