Artificial Intelligence and Spatial Optimization: Evaluation of the Economic and Social Value of UGS in Vračar (Belgrade)

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform.