Rethinking progress in urban analysis and modeling: models, metaphors, and meaning - Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment - page 360

from this broad overview, urban analysis and modeling have
made enormous strides drawn from the powerful methodologi-
cal techniques as well as conceptual metaphors in mathematics,
physics, biology, and computational science. For quite some
time, metaphors have been regarded as frivolous, ornamental,
obfuscatory, and even logically perverted. We now understand
metaphors are not mere ornamentations or decorative con-
structs, but are central in formulating the problem and finding
solutions. Although the correspondence between its theoretical
category and understanding a geographical reality has been ques-
tioned, there seems to have no better alternatives - something
we are struck with and stuck to (Barnes, 1996) in our intellectual
enterprise.
It is interesting to observe that humanity's understanding of
cities have evolved from conceiving them as celestial manifes-
tations on earth to thinking of cities as machines to simulating
cities as organisms to regarding cities simply as spatial events.
Chronologically, we have drawn inspiration from astronomical,
physical, biological, and computation metaphors to operational-
ize urban models. With the dramatic changes that are anticipated
in the human conditions in our cities, it is more urgent than ever
that we develop more robust urban models to better understand
the urban forms and process. Much of the progresses reported in
this topic related to data acquisition, analysis, and modeling may
serve as a springboard to lunch our new round of search for new
models and metaphors. It is between Pascal's two extremes that
we have to come to terms with our urban models and our new
urban reality.

3 I am aware that Batty (2007) regarded CA and AB models
as the new social physics. As discussed in section four of this
chapter, this tradition is closely related to physics, but its
driving metaphor is biologically motivated, thus I put all CA
and AB models in the social biology modeling tradition.

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Acknowledgments

The author would like to thank Morton O'Kelly, Ed Malecki,
Larry Brown, Kevin Cox, and Jose Gavinha for their critical
comments on an earlier draft. Comments from four anony-
mous reviewers and the editor of this volume also substantially
improved this chapter. Research assistance by Jay Knox, Wenqin
Chen, and Li Li is gratefully acknowledged. The author is solely
responsible for any remaining errors or omissions.

Notes

1 Throughout this chapter, metaphors and analogies are used
interchangeably. No distinctions are made among metaphors,
analogies, and similes in this chapter, but literary scholars do
assign different meanings to these three terms. In the geo-
graphic literature, Downs (1981) made a distinction between
metaphor (a means of expression) and analogy (a means of
explanation) in the context of understanding mental maps.
2 One reviewer pointed that spatial system simulation, rooted
in system ecology (Odum and Odum, 2000; Costanza and
Voinov, 2004; Huang, Kao and Lee, 2007; Lee, Huang, and
Chan, 2008), is an emerging approach for modeling cities,
but this approach is dominated by deterministic, top-down
models. Although it is suitable for the motto of organism
world hypothesis, this modeling approach focuses more on
the ecological rather than social processes, thus discussions on
spatial system simulation have been excluded in this section.

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