Geography in America at the Dawn of the 21st Century

244 · Human/Society Dynamics
computing power and large databases have enabled new
models from the physical sciences to be tested and their
relevance to urban space assessed.
Daniel Sui (1994: 260) points out that the integration
of GIS with spatial analysis and modeling encourages
a variety of sophisticated urban research ranging from
studies of urban spatial structure to urban crime analyses
and transportation planning (see also Shaw 1993).
Foody (1995), for example, merges artificial neural network
techniques with ancillary data from GIS to classify
urban land cover. Pond and Yeates (1994) use GIS visualization
technologies to identify land in transition to
urban uses. Perhaps the most influential new types of
urban modeling are derived from fractal geometries and
cellular automata. Sui (1997) notes that cities modeled
through fractals move well beyond the Euclidean logic
of the 1970s because they describe spaces and patterns
that are asserted by some to be “natural.” Sui (ibid. 76)
and others argue that fractal geometry “has proved to
be instrumental in visualizing dynamic systems” because
urban areas are fractal by nature. Fractals describe
patterns that remain constant with changes in scale.
These patterns are persistent in “nature” (e.g. coastlines
and leaves), suggesting to some urban researchers that
they may be used (heuristically at least) to describe
city morphology. Some extend this argument to speculate
on ways that urban fractal patterns suggest social
processes. For the most part, these processes are reduced
to what earlier urban models describe as population
density functions, rank-size distributions, and spatial
dependence (e.g. Anselin et al. 1993; Batty and Xie
1994b).
Another model-based approach that theoretically
links physical systems to attempts at understanding
urban growth is that prescribed by cellular automata
(tessellation automation or self–replicating entities in
proximate space) (K. Clark et al. 1997: 249). The motivation
for modeling cities as cellular automata is to understand
dynamics and linkages from a perspective that
is independent of scale. The underlying argument is
that local processes may be linked to global patterns.
Helen Couclelis (1997) notes that GIS has facilitated
the ability to use cellular automaton-based urban
models as forecasting and policy evaluation tools. These
models have been used to simulate growth in Savannah
(Batty and Xie 1994a), Cincinnati (White et al. 1997),
Washington/Baltimore and the San Francisco Bay Area
(Kirtland et al. 1994; K. Clark et al. 1997; K. Clark and
Gaydos 1998). The latter studies incorporate remotely
sensed spatial data integrated with other geographic
data in a GIS to model and predict human-induced
land-cover change. These models are useful from the
standpoint of policy regarding how urban growth might
be channeled and managed.
As Sui (1997: 75) notes these attempts to use digital
representation and modeling are common to urban
management efforts. While interest in the applicability
of GIS and other technology to urban problems continues
to grow, there has simultaneously been a general
decline of interest amongst geographers in the study
of so-called city systems or urban fields (cf. Wang 2000;
Markusen et al. 2000). As a result, urban modeling and
representation in the 1990s has been focused at the local
scale or on specific populations. For example, although
there is sustained speculation on the importance of commuting
fields to larger urban structures and networks of
cities (W. Clark and Kuijpers-Linde 1994), a large part of
this interest focuses on equity issues as they relate to
gender and racial diversity in the labor force (McLafferty
and Preston 1991; Pratt and Hanson 1991; England 1993;
Hanson and Pratt 1995). One novel area in the study of
commuting fields is the application of GIS and spacetime
geography to model urban accessibility (H. Miller
1991) and issues of gender equity (Kwan 1999).
Although urban modelers are cautious about making
claims that GIS is a panacea for planning problems, most
agree that the management of information and the management
of urban systems “have in the last decade
become twins” (Nijkamp and Scholten 1993: 85) and
that this is an appropriate coupling. Consistent with this
perspective, many GIS researchers favor rational instrumentalist
perspectives. Rational instrumentalism is
based on a modernist discourse that adheres to the premiss
that through the application of rational-scientific
methods and technology it is possible to build better
cities (Cosgrove 1990; Lake 1992). The main assumption
is that human spatial patterns and behaviors (at least in
the aggregate) are predictable. Instrumentalism is allied
to rational-choice theory which portrays society as a
set of goal-oriented individuals who seek to maximize
wealth and efficiency through fixed choices from
amongst alternative courses of action (Barnes and
Shepherd 1992). Nigel Thrift (1996) cautions that much
of this work is driven by “technological determinism”
rather than any real concerns for urban theory. Robert
Lake (1993: 404) adds to this critique by suggesting that
the “rational model” used in planning and applied urban
geography “has been actively resurrected and rehabilitated
by the ascendancy of GIS to a position near or at the
core of both planning and geography,” even though “the
post positivist assumptions embraced by GIS have long
since been jettisoned by academic theorists.” The question
of how GIS is appropriated and used to “solve”
urban problems is critically assessed in literature that