Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints

Abstract

Abstract Highly suitable but limited ecological space continues to become one of the most critical constraints in the processes of urbanization due to extremely scarce land resources in urban areas. Urban cellular automata have become an effective tool for simulating the development of cities. However, the constraints in the current literature are established mainly by Boolean constraints or weighted linear combinations. Here we propose an urban model that integrates binary logistic regression, ordered weighted averaging, and cellular automata. The resultant model was used to predict the profile of future urban spatial evolution under different ecological constraints. Important regional ecological spaces were identified based on the importance and sensitivity assessments of key ecosystem services. The obtained various important ecological space layers were sorted and weighted by using ordered weighted averaging (OWA) technology, and, thus, the OWA-based ecological-constraint mechanism was established and integrated into urban cellular automata. We tested the model in the simulations of Xiamen City’s urban form evolutions during periods 1990–2000 and 2005–2015 were simulated. The resulting overall accuracies of the simulations reached 96.0% and 91.6%, and corresponding kappa coefficients were 0.836 and 0.796, respectively. The results of the 2015–2040 simulations suggest that urban model generated a smooth spectrum of future urban forms by adjusting the control factor of the ecological constraints. The new built-up land presented a trend of concentrated development from the northern and northwestern part of the study area to the southern part of Xiang’an District while highlighting the high values of the ecological constraints. The OWA-based urban cellular automata model thus helps identify areas where the contradictions and pressures between urban development and ecological constraints are most concentrated and provides reasonable solutions. The method can also help urban management find a balance between development and environmental protection.