Non-standard Spatial Statistics and Spatial Econometrics

by Daniel A. Griffith, Jean H. Paul Paelinck

Overview

Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree "spatial analysis with measurement"—some may remember the debate after WWII about "theory without measurement" versus "measurement without theory"—several general themes have emerged in the pertaining literature. But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

Product Details

ISBN-13:	9783642160424
Publisher:	Springer Berlin Heidelberg
Publication date:	01/11/2011
Series:	Advances in Geographic Information Science , #1
Edition description:	2011
Pages:	264
Product dimensions:	6.30(w) x 9.20(h) x 0.90(d)

Table of Contents

Part 1. Non-standard spatial statistics.- 1. Introduction: spatial statistics, - 2. Individual versus ecological analyses.- 3. Statistical models for spatial data: some linkages and communalities.- 4. Frequency distributions for simulated spatially autorcorrelated random variable.- 5. Understanding correlations among spatial random variables.- 6. Spatially structured random effects: a comparison of three popular specifications.- 7. Spatial filter versus conventional spatial model specifications: some comparisons.- 8. The role of spatial of auorrelation in prioritizing sites within a geographic landscape.- 9. General spatial statistics conclusions.- 10. References: spatial statistics (Part 1) Part 2. Non-standard spatial econometrics.- 11. Introduction: spatial econometrics.- 12. Mixed linear-logarithmetic specification for Lotka-Volterra models with endogenously generated SDLS-variables.- 13. Selecting spatial regimes by threshold analysis.- 14. Finite automata.- 15 Learning from residuals.- 16. Verhulst and Poisson distributions.- 17. QUARLIREG: qualitative regression and its application to spatial data.- 18. Filtering complexity for observational errors and spatial bias.- 19. General spatial econometrics conclusions.- 20. References: spatial econometrics (Part 2).