Computational Frameworks for Optimizing the Trade-off Between Historic Preservation and Commercial Vitality in Urban Renewal

Abstract

The tension between preserving historic commercial architecture and generating necessary economic vitality represents a complex multi-objective optimization problem in modern urban renewal. While traditional urban planning often relies on qualitative assessments and heuristic decision-making, this paper proposes a rigorous computational approach to resolve the dichotomy between heritage conservation and economic regeneration. We introduce a novel data-driven framework, the Adaptive Heritage-Economy Optimization (AHEO) model, which utilizes high-resolution spatial data, machine learning-based economic forecasting, and evolutionary algorithms to identify optimal intervention strategies. By parameterizing architectural significance alongside projected commercial revenue streams, we demonstrate how computational methods can navigate the non-linear trade-offs inherent in adaptive reuse projects. This research bridges the gap between computer science and urban heritage management, providing a scalable methodology for policymakers and developers. The results indicate that algorithmic mediation can secure higher rates of material preservation while simultaneously maximizing the functional utility and revenue potential of historic districts.