From Industrial Heritage to Experiential Consumption Spaces: Socio- Economic Impact Assessment of "Adaptive Reuse" Projects in Urban Regeneration

Abstract

The transformation of obsolete industrial infrastructure into vibrant experiential consumption spaces represents a critical paradigm shift in contemporary urban regeneration. This paper presents a novel, data-driven computational framework to assess the socio-economic impacts of adaptive reuse projects. While traditional urban planning assessments rely heavily on static census data and qualitative surveys, this study leverages urban computing techniques, including big data analytics, spatial sentiment analysis, and multi-criteria evaluation models, to provide a dynamic and granular impact assessment. We focus on the transition from production-oriented landscapes to consumption-oriented heritage sites, analyzing how this shift influences local economic vitality, gentrification velocity, and social inclusion. By integrating geo-tagged social media data, real-time transaction records, and municipal zoning datasets, we propose a multi-modal assessment algorithm that quantifies the "experiential value" and its correlation with economic uplift. The results indicate that while adaptive reuse significantly boosts commercial density and tourism revenue, it necessitates careful algorithmic monitoring to mitigate displacement risks. This research bridges the gap between computer science and urban sociology, offering policymakers a robust digital toolkit for sustainable heritage management.