This repository contains python codes and jupyter notebooks of research analyses of published papers:
- Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation https://doi.org/10.1016/j.compenvurbsys.2021.101689
- A data-driven framework to manage uncertainty due to limited transferability in urban growth models https://doi.org/10.1016/j.compenvurbsys.2022.101892
The research involves using
- a constrained cellular automata model CCA to model urban expansion,
- a Markov chain Monte Carlo Approximate Bayesian Computation to calibration model parameters,
- clustering of urban growth modes (parameter clusters) from extrapolated parameters,
- using the urban growth modes (parameter clusters) to classify/characterize urban spatial developments.