In this study, we propose a universally applicable, two-level approach aimed at optimizing both tree planting locations and species selection, integrating the model into an intuitive Web Application. By combining multiple data sources, the application first provides a deeper understanding of the dataset by exploring internal correlations and uncovering the underlying patterns that define the urban tree ecosystem. It then uses a best-fit regression model to outline the current state of the data, serving as a solid foundation for predictive modeling. The research results in the development of a comprehensive predictive model combined with a heuristic algorithm, which offers recommendations for optimal tree planting locations within a city. It prioritizes areas with the highest potential for urban greening and balances species selection between those that maximize ecosystem benefits and those already prevalent in the area, thereby ensuring continuity and coherence with the existing green cover. The findings of this study are intended to support urban greening planning and policy decisions in cities, fostering sustainable development and improving public health and well-being. The approach is designed to be transferable to any urban context and is demonstrated here using Bologna (Italy) as a representative case study. • We present an instrument to optimize urban tree planting. • It prioritizes areas with the highest greening potential, balancing species selection. • It features a comprehensive predictive model to support urban planners.
Maccacaro et al. (Fri,) studied this question.