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.
Readership: Graduate students, spatial analysts.
Daniel A. Griffith, Professor in the Department of Geography and Interdisciplinary Statistics Program, Syracuse University, and Larry J. Layne, Assistant Professor, Instituto de Geografia y Desarrollo Regional, Universidad Central de Venezuela
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
