A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area

ABSTRACT

Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas.

KEYWORDS:

• Tree-based methods
• extremely randomised trees
• driving factor
• multiple land use change modelling
• cellular automata

Acknowledgements

This research is supported by the Ministry of Education, Culture, Sports, Science and Technology in Japan (MEXT). Any opinions, findings and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the MEXT.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by the Ministry of Education, Culture, Sports, Science and Technology in Japan (MEXT) under a Specially Promoted Research grant [26000001].