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.
