found in Clarke and Gaydos (1998), Yang and Lo (2003), Jantz et al. (2004),
Mahiny and Clarke (2012), and Akin et al. (2014).
As a dynamic modeling tool, CA model has gained great popularity among all
modeling approaches. Although offering a framework for studying complex systems, CA modeling does not explicitly incorporate drivers of change except for the
neighborhood interactions and transition rules. In addition, CA does not explicitly
account for human decision makings in their modeling structures as the cells cannot
move and their transition in states mainly represent the physical processes of land
conversion.
1.2.5 Economic Models
Economic models generate land use patterns as aggregate outcomes from the
underlying microeconomic behavior that determines demand and supply relationships. These models explicitly involve human choices and economic behaviors and
thus address the human dimension of land changes, mainly focused on land uses.
The basic idea of economic models of land changes is based on market equilibrium
(e.g., market clear with zero excess demand and zero excess supply). Economic
models can operate at aggregate scale (e.g., sector-based models) and disaggregate
scale (e.g., spatially disaggregate models). Sector-based models represent the
global economy and the interactions between different sectors (i.e., general equilibrium models) or only some specific sectors as a closed system (i.e., partial
equilibrium models). Therefore, sector-based models describe the amount of land
allocated to different uses by demand-supply structures (Sohngen et al. 1999).
Spatially disaggregate models simulate the optimal land use decision based on
profits or utility maximization or cost minimization (Bockstael 1996; Wu
et al. 2004). These models explicitly represent individual decision-making at the
micro level that will lead to land change outcomes at the aggregate level.
Economic models explicitly represent human land use decisions based on
market and price mechanism compared with most statistical, machine learning
and cellular models. The spatially disaggregate models are promising in accounting
for the market feedbacks and dynamics within the land change systems. These
models are often used in the agent-based framework to simulate the decisionmaking processes of human agents. Economic models are useful for
non-marginal land change simulation and prediction. However, given the complexity of human choices and data scarcity, it is quite challenging for economic models
to build the underlying assumptions.
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T. Liu and X. Yang
Mahiny and Clarke (2012), and Akin et al. (2014).
As a dynamic modeling tool, CA model has gained great popularity among all
modeling approaches. Although offering a framework for studying complex systems, CA modeling does not explicitly incorporate drivers of change except for the
neighborhood interactions and transition rules. In addition, CA does not explicitly
account for human decision makings in their modeling structures as the cells cannot
move and their transition in states mainly represent the physical processes of land
conversion.
1.2.5 Economic Models
Economic models generate land use patterns as aggregate outcomes from the
underlying microeconomic behavior that determines demand and supply relationships. These models explicitly involve human choices and economic behaviors and
thus address the human dimension of land changes, mainly focused on land uses.
The basic idea of economic models of land changes is based on market equilibrium
(e.g., market clear with zero excess demand and zero excess supply). Economic
models can operate at aggregate scale (e.g., sector-based models) and disaggregate
scale (e.g., spatially disaggregate models). Sector-based models represent the
global economy and the interactions between different sectors (i.e., general equilibrium models) or only some specific sectors as a closed system (i.e., partial
equilibrium models). Therefore, sector-based models describe the amount of land
allocated to different uses by demand-supply structures (Sohngen et al. 1999).
Spatially disaggregate models simulate the optimal land use decision based on
profits or utility maximization or cost minimization (Bockstael 1996; Wu
et al. 2004). These models explicitly represent individual decision-making at the
micro level that will lead to land change outcomes at the aggregate level.
Economic models explicitly represent human land use decisions based on
market and price mechanism compared with most statistical, machine learning
and cellular models. The spatially disaggregate models are promising in accounting
for the market feedbacks and dynamics within the land change systems. These
models are often used in the agent-based framework to simulate the decisionmaking processes of human agents. Economic models are useful for
non-marginal land change simulation and prediction. However, given the complexity of human choices and data scarcity, it is quite challenging for economic models
to build the underlying assumptions.
8
T. Liu and X. Yang