Industrial heritage serves as a testament to the historical and cultural legacy of industrialization, and its preservation and adaptive reuse are crucial for promoting sustainable urban development. This study explores the spatiotemporal dynamics of industrial heritage in Foshan City, China, and simulates potential heritage corridors to inform effective conservation and revitalization strategies. By employing Kernel Density Estimation (KDE) and Standard Deviational Ellipse (SDE) methods, the research investigates the spatial and temporal distribution patterns of industrial heritage across different historical periods and industrial types. An Analytic Hierarchical Process (AHP) is used to construct a hierarchical model of resistance factors, which serves as the basis for simulating potential heritage corridors using the Minimum Cumulative Resistance (MCR) model. The results unveil distinct spatiotemporal distribution patterns, with concentrations of industrial heritage in the central Chancheng District and southeastern Shunde District. Two primary potential heritage corridors are identified, and prioritized strategies for their adaptive reuse are proposed. The findings contribute to a comprehensive understanding of industrial heritage distribution in Foshan City and provide valuable insights for the conservation, planning, and sustainable development of these significant sites. The study highlights the importance of integrating spatiotemporal analysis and heritage corridor modeling in the decision-making process for industrial heritage revitalization, ensuring the preservation of invaluable industrial history and culture while fostering sustainable urban growth.

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.