A Casebook for Spatial Statistical Data Analysis (Hardcover)

Daniel A. Griffith

ISBN 10: 0195109589 / ISBN 13: 9780195109580

Edité par Oxford University Press Inc, New York, 1999

A propos de cet article

Hardcover. This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers,regional scientists, applied spatial statisticians, and spatial scientists from across disciplines. Spatial statistics provide ways to analyse spatial data, and this book aims to meet a need for a compilation of data sets. It presents a summary discussion of the theoretical basis of techniques, numerical evidence and a variety of data examples. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

N° de réf. du vendeur 9780195109580

Détails bibliographiques

• Titre: A Casebook for Spatial Statistical Data Analysis (Hardcover)
• Éditeur: Oxford University Press Inc, New York
• Date de parution: 1999
• Langue: anglais
• ISBN à 10 chiffres: 0195109589
• ISBN à 13 chiffres: 9780195109580
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

Synopsis

This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers, regional scientists, applied spatial statisticians, and spatial scientists from across disciplines.