Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change

Lessons Learned from Applying an Integrated Land Use Transport Planning Model to Address Issues of Social and Economic Exclusion of Marginalised Groups: The Case of Cape Town, South Africa

The Group Areas Act of 1950 has resulted in post-apartheid South African cities being characterised by spatial patterns with limited access to social and economic opportunities for the black and coloured population. Typically, high-density low-income housing is located peripherally, while low density high-income housing is located in accessible central areas. With increased rural-to-urban migration, the demand for formal housing has historically surpassed supply, which has increased the growth of informal settlements. Current discourse within South African land use policy suggests that in-situ upgrading of informal housing is a viable response to integrate informal settlements into the formal city. In parallel, it is proposed that new low-income residential areas and employment-generating land uses should be located along transport corridors to improve access to transport, its infrastructure and the opportunities it provides for previously marginalised groups. This study uses Cape Town as a case city to explore two land-use driven development strategies directed at informal settlements and low-income housing. A dynamic land use transport model based on a cellular automata land use model and a four-stage transport model was used to simulate land use and transport changes. Specifically, in-situ upgrading of informal settlements and strategically locating new low-income residential and employment generating land uses along transport corridors were considered. The results from the analysis suggest that in-situ upgrading is a viable option only if new informal settlements are in areas with easy access to economic centres. With regards to low-income housing, targeted interventions aimed at ‘unlocking’ low-income housing activities along transport corridors were found to be useful. However, it was also observed that middle-income residential development and employment generating activities were also attracted to the same corridors, thus, resulting in mixed land uses, which is beneficial but can potentially result in rental bids between low and middle-income earners thus displacing low-income earners away from these areas

Empirically derived method and software for semi-automatic calibration of Cellular Automata land-use models

Land-use change models generally include neighbourhood rules to capture the spatial dynamics between different land-uses that drive land-use changes, introducing many parameters that require calibration. We present a process-specific semi-automatic method for calibrating neighbourhood rules that utilises discursive knowledge and empirical analysis to reduce the complexity of the calibration problem, and efficiently calibrates the remaining interactions with consideration of locational agreement and landscape pattern structure objectives. The approach and software for implementing it are tested on four case studies of major European cities with different physical characteristics and rates of urban growth, exploring preferences for different objectives. The approach outperformed benchmark models for both calibration and validation when a balanced objective preference was used. This research demonstrates the utility of process-specific calibration methods, and highlights how process knowledge can be integrated with automatic calibration to make it more efficient.Charles P. Newland, Aaron C. Zecchin, Holger R. Maier, Jeffrey P. Newman, Hedwig van Delde