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results. Two CA models were compared to test how the socioeconomic data could
improve the urban model simulation in Houston during the last 30 years. Specially,
the following research questions were addressed: How the socioeconomic data
could be incorporated with remote sensing in the urban growth model? Does the
socioeconomic data improve the model? In which classes does this model improve?
12.2
Urban Model Review and Socioeconomic Data
in the Model
With the availability of spatial data on a large scale, various sophisticated models,
especially after the late 1990s, were developed such as UrbanSim model (Waddell
2002
), Markov chain model (Stewart
1994
), LUCAS model (Berry et al.
1996
),
CLUE model (De Kong et al.
1999
), area-based model (Lichtenberg
1985
; Tayyebi
et al.
2011
,
2013
); CA model (Batty and Xie
1994
), Land Transformation Model
(Pijanowski et al.
1997
,
2014
), and agent-based model (Liebrand et al.
1998
). The
detailed review of these spatial explicit models is listed in Table
12.1
.
In terms of the methods to represent the model object, there are vector-based
models and grid-based models (Herold
2004
), and both of them have been used to
incorporate socioeconomic data. Vector-based models use the thematic map as the
input data for the model, and the spatial objects are usually defined as homoge-
nous land units.
UrbanSim
is one of land use simulation models for the growth
government, regional land use, and transportation planning in the states of Hawaii,
Oregon, and Utah (Waddell
2002
). Within the context of urban infrastructure and
governmental policy,
UrbanSim
represents zonal structure in the urban area to
monitor the socioeconomic-related behaviors of households, business, and land
developers. Theoretically,
UrbanSim
is an object-oriented model.
What if
model
(Klosterman
1999
) begins with uniform analysis zones or homogeneous land units
generated from the GIS software. Through applying the governmental policies and
land use demands, this model derives the aggregating value of the regional condition
on the land units.
What if
model projects future land use patterns by balancing the
supply, demand, and land sustainable at different locations.
Area-base
d model is a
vector-based model used in resource assessments to predict the availability of farm
and forest land. Transformed from the regional model (Palmquist
1989
),
area-based
model allocates the proportions of a given land use to predefine land use categories
using Lichtenberg's (
1985
) acreage allocation method (Tayyebi et al.
2011
,
2013
).
Another vector-based model is
Markov
model which predicts future landscape
patterns based on the spatial transition probability. Although Markov model is a
typical spatial transition model, early Markovian analysis is a descriptive tool to
predict land use change on a local or regional scale (Bell
1974
; Bourne
1976
;
Arsanjani et al.
2013
). Actually, the Markov model is not a strict vector-based
model; it is based on the statistical results from the thematic map. Lopez et al.
(
2001
) used Markov chain to simulate the relationships among a set of urban and
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