Environmental Engineering Reference
In-Depth Information
100 meters
FIGURE 11.2
Structural-functional links: IKONOS and area-determined postal addresses (residential on the left, and
commercial on the right) for the Belfast study site.
11.5
Temporal
-
structural
-
functional links
11.6
Empirical
measurement of
temporal lags
A more recent perspective on research into urban structural -
functional models is the pursuit of time-dependence; under-
standing how temporal lags affect the causal links between
societal and political functional demands and physical ramifica-
tions. Thus far integrative remote sensor models have assumed
temporal equality. This is where the same time period is assumed
for both when the image is taken and when functional attributes
are collected. Instead, Fig. 11.3 illustrates how a temporal inte-
grative model at two time periods (
T
1
and
T
2
)canbeformulated
by combining urban structural patterns (derived from classified
remote sensor data) post
T
1
as
T
1
+
1
and post
T
2
as
T
2
+
1
and
urban functional demands and decisions (derived predominantly
from population censuses and urban plans) pre
T
1
as
T
1
−
1
and
pre
T
2
as
T
2
−
1
respectively.
The relationship states that decisions and trends in urban
functions at
T
1
−
1
determine the type and density of urban struc-
ture at
T
2
−
1
. Precisely how urban functions
determine
urban
structure (and maybe even how structure determines functions)
is reflective of theories of urban process; for instance, demand
for new housing type and housing density, suburbanization,
decentralization of businesses, segregation levels, deprivation and
congestion and pollution. Changes in urban population, includ-
ing changes in demographic profiles (family, ethnic minorities
and affluent levels), demand for housing (both size and value),
and local government plans are the main drivers behind urban
processes that link function and structure.
There are a variety of conceivable methods to determine and
even measure temporal lags between structure and function.
These can be data-driven, process-driven, or theory-driven. The
review by Lo (2007) investigated the development of geospatial
technology and its use and potential use in urban morphology.
The fields of remote sensing, photogrammetry and GIS when
combined are particularly important and suitable for use in
urban morphological research. Among the many data-driven
applications based on these geospatial technologies, Taubenbock
et al
. (2009), in particular, explored the benefits of multitemporal
remote sensing for analyzing long-term changes in temporal and
spatial urban sprawl, redensification and urban development for
large cities in India. Most other studies also develop analytical
models to simulate and evaluate temporal changes. Benguigui,
Czamanski and Marinov (2004) investigated the temporal lag
of towns in the Tel Aviv, Israel area by comparing an analytic
model with a computer simulation to predict population growth.
In the dynamic analytic model they used time in two phase; in
the first, the derivative was an increasing function - a town was
very attractive and there was a short delay between decision to
build and complete realization of the process - and there was
no shortage of land. However in the other time phase the delay
began to increase and there was a lack of available land, leading to
a decreased the rate of the population variation until saturation.
Cheng and Masser (2004) acknowledged the inherent spa-
tial and temporal complexity of urban growth by developing
a process-oriented cellular automata methodology at both the
local spatial and the global dynamic scales based planning and
decision-making processes. They linked spatial and temporal
patterns using an innovative nonlinear function of land devel-
opment and dynamic weighting. The model approach was more
recently developed by Chen (2009) who investigated spatial inter-
actions between cities based on a time-lag parameter and time
Functional
T
2
−
1
Functional
T
1
−
1
Structural
Structural
T
1
+
1
T
2
+
1
T
1
T
2
FIGURE 11.3
Temporal-structural-functional relationship.
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