The project integrates population dynamics and building stock development through predictive modeling to promote sustainable urbanization. As urbanization accelerates, predicting and steering where growing populations will settle becomes increasingly important and ensures that built environments remain sustainable without compromising the well-being of current or future residents or the environment. The project aims to develop more precise predictive models to better understand how population distribution will evolve across different urban areas and how both the built and natural environments will change. These insights will help identify and address key sustainability challenges such as residential segregation and environmental inequality, in an interdisciplinary manner. The project combines municipal scale logistic population forecasting and dynamic material flow based building stock models (MFA) with machine learning modeling for population prediction at a more granular spatial level. Additionally, using these modelling results, the project will explore scenarios related to e.g. material supply limitations, green space preservation and different population dynamics. Throughout the modeling process, uncertainty factors will be accounted for, and sensitivity analysis will be conducted to better understand the effects of different variables. Key data sources for this research include register-based population datasets from Statistics Finland, building stock datasets by Finnish Environment institute and open land use datasets.

2025