In recent years there has been a growing concern for the
development of both efficient and effective ways to handle
space-time problems. Such developments should be theoretically as
well as empirically oriented. Regardless of which of these two
arenas one enters. the impression is quickly gained that
contemporary wO, rk on dynamic and evolutionary models has not
proved to be as illuminating and rewarding as first anticipated.
Historically speaking. the single. most important lesson this
avenue of research has provided. is that linear models are woefully
inadequate when dominant non-linear trends and relationships
prevail. and that independent activities and actions are all but
non-existent in the real-world. Meanwhile. one prominent imp 1
ication stemming from this 1 iterature is that the easiest
modelling tasks are those of specifying good dynamic space-time
models. Somewhat more problematic are the statistical questions of
model specification. parameter estimation. and model validation.
whereas even more problematic is the operationalization of
evolutionary conceptual models. A timely next step in spatial
analysis would seem to be a return to basics. with a pronounced
focus both on specific problems (and data) and on the mechanisms
that transform phenomena through space and/or time'. It appears
that these transformation mechanisms must embrace both non-linear
and autoregressive formalisms. Given. also. the variety of
geographic forms. they must allow for bifurcation points to emerge.
too.
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