A Casebook for Spatial Statistical Data Analysis (Hardcover)

Daniel A. Griffith

ISBN 10: 0195109589 / ISBN 13: 9780195109580

Editore: Oxford University Press Inc, New York, 1999

Riguardo questo articolo

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.

Codice articolo 9780195109580

Dati bibliografici

• Titolo: A Casebook for Spatial Statistical Data Analysis (Hardcover)
• Editore: Oxford University Press Inc, New York
• Data di pubblicazione: 1999
• Lingua: Inglese
• ISBN 10: 0195109589
• ISBN 13: 9780195109580
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

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.

Contenuti

• Introduction
• List of Abbreviations
• Part I: Theoretical Background
• 1: Introduction
• 2: Important Modeling Assumptions
• 3: Popular Spatial Autoregressive and Geostatistical Models
• Part II: Georeferenced Data Set Case Studies
• 4: Analysis of Georeferenced Socioeconomic Attribute Variables
• 5: Analysis of Georeferenced Natural Resources Attribute Variables
• 6: Analysis of Georeferenced Agricultural Yield Variables
• 7: Analysis of Georeferenced Pollution Variables
• 8: Analysis of Georeferenced Epidemiological Variables
• Part III: Visualizing What is Not Observed
• 9: Exploding Georeferenced Data When Maps Have Holes or Gaps: Estimating Missing Data Values and Kriging
• 10: Concluding Comments
• Epilogue
• Bibliography
• Index