Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series
In recent years there has been a growing interest in and concern for the development of a sound spatial statistical body of theory. This work has been undertaken by geographers, statisticians, regional scientists, econometricians, and others (e. g. , sociologists). It has led to the publication of a number of books, including Cliff and Ord's Spatial Processes (1981), Bartlett's The Statistical Analysis of Spatial Pattern (1975), Ripley's Spatial Statistics (1981), Paelinck and Klaassen's Spatial Economet~ics (1979), Ahuja and Schachter's Pattern Models (1983), and Upton and Fingleton's Spatial Data Analysis by Example (1985). The first of these books presents a useful introduction to the topic of spatial autocorrelation, focusing on autocorrelation indices and their sampling distributions. The second of these books is quite brief, but nevertheless furnishes an eloquent introduction to the rela tionship between spatial autoregressive and two-dimensional spectral models. Ripley's book virtually ignores autoregressive and trend surface modelling, and focuses almost solely on point pattern analysis. Paelinck and Klaassen's book closely follows an econometric textbook format, and as a result overlooks much of the important material necessary for successful spatial data analy sis. It almost exclusively addresses distance and gravity models, with some treatment of autoregressive modelling. Pattern Models supplements Cliff and Ord's book, which in combination provide a good introduction to spatial data analysis. Its basic limitation is a preoccupation with the geometry of planar patterns, and hence is very narrow in scope.
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Derivation of the expected value of MC
Areal unit configuration and locational
Reformulating classical linear statistical
Spatial autocorrelation and spectral
The missing data problem of
Appendix 6A FORTRAN subroutine
Correcting for edge effects in spatial
Multivariate models of spatial dependence
Rules for Kronecker products
Summary and conclusions
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Advanced Spatial Statistics: Special Topics in the Exploration of ...
Daniel A. Griffith
No preview available - 2011
algorithm American Statistical Association appearing in Table areal units asymptotic variances autocorrelated surfaces autoregressive model Biometrika buffer zone calculated canonical correlation canonical variate Chapter Cliff and Ord CMISS2 configuration of areal connectivity matrix constraint CONVERGENCE correlation coefficient covariance CTINV data sets diagonal edge effects eigenvalues eigenvalues of matrix eigenvector equation finite Geographical Analysis Griffith and Bennett Haining Hence incomplete data IOBSN Journal likelihood function linear maximum likelihood mean missing data missing values multivariate normal NMISS number of areal parameter estimates patterns PCSA PCSI principal components principal eigenvalue problem procedure properties pseudo-random numbers random variables region regression model RNEIGH sampling distribution simulation experiments solution spatial auto spatial autocorrelation spatial autoregressive model spatial interaction spatial statistics spectral squares SUBROUTINE SUMLMX2 SUMMNG2 SUMOX surface partitioning techniques Toronto trend surface model two-dimensional variance-covariance matrix vector WMOSUM XBAR zero ZXMIN