(Openshaw and Taylor 1979). The common approach to deal with the MAUP issue
is to apply a multi-scale analysis to examine how the relationships among variables
change with varying levels of aggregation and different ways of zoning (e.g.,
Veldkamp and Fresco 1997; Walsh et al. 2001; Evans and Kelley 2004; Hu and
Lo 2007). Multilevel statistical modeling has also been used for analyzing land
change driving factors at nested hierarchical levels (Hoshino 2001).
Land change modeling is further complicated by the potential interactions and
feedbacks among different levels of processes (Verburg 2006). In simulating the
multilevel interactions, the modeling frameworks of cellular automata and agentbased model allow for the representation and incorporation of processes at multiple
levels. The current land use models focus on two types of cross-scale dynamics:
top-down and bottom-up simulation. The top-down control is represented by the
government policies and global interactions affecting land demand and growth
suitability. From the bottom-up perspective, human makes decisions on land
allocation which produces the aggregate land use patterns. Further exploration on
their capabilities is needed given the challenges in theoretical development and data
availability, as well as the high computing demand of agent-based modeling.
1.3.3 Temporal Dynamics and Complexity
Simulating temporal dynamics is another critical issue for land change modeling,
which brings about the need to handle time lags and feedback responses in the
temporal dimension of land change processes (Agarwal et al. 2002). Under the
assumption of stationarity, statistical modeling and machine learning have very
limited capability to represent temporal dynamics and complexity of the land
change processes. They often assume the factors leading to the observed patterns
and processes in the past will continue to do so in the future. This assumption is
problematic as it is very likely the factors will alter their future behaviors given
changes in the landscape or some exogenous conditions. To the contrary, the
framework of cellular automata and agent-based models allows for the temporal
dynamics to be considered as the behaviors at individual level may alter in response
to landscape changes or incorporated external variables at each simulation
time step.
The ecological and socioeconomic responses within the coupled humanenvironmental systems may not be immediately observable or predictable because
the existence of time lags between the human-nature interactions and the appearance of ecological and socioeconomic consequences. To address this issue, a
temporally lagged variable can usually be included in some models such as the
statistical regression models. More complex models have the flexibility to represent
time lags in land use decisions. For example, Irwin and Bockstael (2002) treat the
interactions among neighboring agents making a residential conversion decisions as
a temporally lagged process to better represent the real world decision-making
processes.
1 Land Change Modeling: Status and Challenges
11
is to apply a multi-scale analysis to examine how the relationships among variables
change with varying levels of aggregation and different ways of zoning (e.g.,
Veldkamp and Fresco 1997; Walsh et al. 2001; Evans and Kelley 2004; Hu and
Lo 2007). Multilevel statistical modeling has also been used for analyzing land
change driving factors at nested hierarchical levels (Hoshino 2001).
Land change modeling is further complicated by the potential interactions and
feedbacks among different levels of processes (Verburg 2006). In simulating the
multilevel interactions, the modeling frameworks of cellular automata and agentbased model allow for the representation and incorporation of processes at multiple
levels. The current land use models focus on two types of cross-scale dynamics:
top-down and bottom-up simulation. The top-down control is represented by the
government policies and global interactions affecting land demand and growth
suitability. From the bottom-up perspective, human makes decisions on land
allocation which produces the aggregate land use patterns. Further exploration on
their capabilities is needed given the challenges in theoretical development and data
availability, as well as the high computing demand of agent-based modeling.
1.3.3 Temporal Dynamics and Complexity
Simulating temporal dynamics is another critical issue for land change modeling,
which brings about the need to handle time lags and feedback responses in the
temporal dimension of land change processes (Agarwal et al. 2002). Under the
assumption of stationarity, statistical modeling and machine learning have very
limited capability to represent temporal dynamics and complexity of the land
change processes. They often assume the factors leading to the observed patterns
and processes in the past will continue to do so in the future. This assumption is
problematic as it is very likely the factors will alter their future behaviors given
changes in the landscape or some exogenous conditions. To the contrary, the
framework of cellular automata and agent-based models allows for the temporal
dynamics to be considered as the behaviors at individual level may alter in response
to landscape changes or incorporated external variables at each simulation
time step.
The ecological and socioeconomic responses within the coupled humanenvironmental systems may not be immediately observable or predictable because
the existence of time lags between the human-nature interactions and the appearance of ecological and socioeconomic consequences. To address this issue, a
temporally lagged variable can usually be included in some models such as the
statistical regression models. More complex models have the flexibility to represent
time lags in land use decisions. For example, Irwin and Bockstael (2002) treat the
interactions among neighboring agents making a residential conversion decisions as
a temporally lagged process to better represent the real world decision-making
processes.
1 Land Change Modeling: Status and Challenges
11