regression can be binomial or multinomial. It takes the logit transformation of the
categorical dependent variable to ensure that the dependent variable of the regression is continuous.
Given the less demand of computational resources and easy operability, statistical regression models have become one of the most popular approaches for land
change research communities. Statistical methods provide valuable information on
key factors of land changes but are relatively deterministic compared to more
advanced forms of model. It can also contribute to theory building and testing
(Lesschen et al. 2005). However, it has very limited capability to represent the
complex interactions and the temporal dynamics within the coupled humanenvironmental systems.
1.2.2 Artificial Neural Networks
Artificial neural networks (ANN) are developed based on machine learning algorithms (e.g., Li and Yeh 2002; Liu and Seto 2008). The functioning of ANN is
relating to regression models in that they both seek to associate land change and its
potential drivers. ANN is characterized by its ‘learning’ ability which can be used to
detect non-linear relationships through the incorporation of a hidden layer. The
algorithms of ANN calculate weights for input layers, hidden layers, and output
layers by introducing the input in a feed-forward manner. For example, Liu and
Seto (2008) presented an ART-MMAP neural network model for urban growth
prediction from historical data. A set of proximity, neighborhood, and physical
factors were included. This paper also applied a multi-resolution analysis to test the
model’s performance. In general, spatial aggregation results in higher accuracies.
By comparing with a null model, two random models and a naive model, neural
network outperforms other models at finer resolution.
The strength of neural networks lies in their flexibility and non-linearity
(Lesschen et al. 2005) in predicting future changes. However, it provides little
interpretability because the relationships between variables remain invisible, criticized as a “black box”. ANN is commonly used for predicting future land cover/
use changes based on the ‘knowledge’ learned from the patterns and behaviors
observed from historical data. The assumption here is that past and present trend
will continue into the future (i.e., stationarity), which tends to oversimplify the
temporal complexity of land change processes.
1.2.3 Markov Chain Modeling
The Markov chain modeling approach employs a discrete stochastic process to
determine the transition probability of land conversion. There is a set of discrete
states in the modeling structure. In the context of land change modeling, each state
6
T. Liu and X. Yang
categorical dependent variable to ensure that the dependent variable of the regression is continuous.
Given the less demand of computational resources and easy operability, statistical regression models have become one of the most popular approaches for land
change research communities. Statistical methods provide valuable information on
key factors of land changes but are relatively deterministic compared to more
advanced forms of model. It can also contribute to theory building and testing
(Lesschen et al. 2005). However, it has very limited capability to represent the
complex interactions and the temporal dynamics within the coupled humanenvironmental systems.
1.2.2 Artificial Neural Networks
Artificial neural networks (ANN) are developed based on machine learning algorithms (e.g., Li and Yeh 2002; Liu and Seto 2008). The functioning of ANN is
relating to regression models in that they both seek to associate land change and its
potential drivers. ANN is characterized by its ‘learning’ ability which can be used to
detect non-linear relationships through the incorporation of a hidden layer. The
algorithms of ANN calculate weights for input layers, hidden layers, and output
layers by introducing the input in a feed-forward manner. For example, Liu and
Seto (2008) presented an ART-MMAP neural network model for urban growth
prediction from historical data. A set of proximity, neighborhood, and physical
factors were included. This paper also applied a multi-resolution analysis to test the
model’s performance. In general, spatial aggregation results in higher accuracies.
By comparing with a null model, two random models and a naive model, neural
network outperforms other models at finer resolution.
The strength of neural networks lies in their flexibility and non-linearity
(Lesschen et al. 2005) in predicting future changes. However, it provides little
interpretability because the relationships between variables remain invisible, criticized as a “black box”. ANN is commonly used for predicting future land cover/
use changes based on the ‘knowledge’ learned from the patterns and behaviors
observed from historical data. The assumption here is that past and present trend
will continue into the future (i.e., stationarity), which tends to oversimplify the
temporal complexity of land change processes.
1.2.3 Markov Chain Modeling
The Markov chain modeling approach employs a discrete stochastic process to
determine the transition probability of land conversion. There is a set of discrete
states in the modeling structure. In the context of land change modeling, each state
6
T. Liu and X. Yang