1. The revitalization of historic districts has become an essential aspect of sustainable urban renewal, and data mining and analytics play a significant role in extracting knowledge and decision-making in the revitalization process.
2. Machine learning is a fundamental technique for extracting information, data pattern detection, and prediction in the renewal approach of rejuvenating historic districts.
3. The study investigates the historical block update approach from the perspective of machine learning, utilizing feature space and culture simultaneously, and takes Nantong Temple Street as an example to revitalize historical areas with fresh life in the modern period.
The article "Designing a Renewal Approach of Rejuvenating Historic Districts: Taking Nantong Temple Street as an Example" discusses the importance of preserving historical districts in cities and proposes a renewal approach using machine learning and cultural integration. The article provides a detailed analysis of the research and current status of urban historical blocks, both abroad and domestically.
The article presents a one-sided view that emphasizes the importance of preserving historical districts without exploring potential counterarguments or risks associated with preservation efforts. The article also lacks evidence to support some of its claims, such as the effectiveness of data mining and analytics in revitalizing historic districts.
Furthermore, the article appears to have promotional content for Nantong Temple Street as an example of successful renewal efforts without acknowledging any potential drawbacks or limitations. This bias may be due to the authors' affiliation with Nantong University.
Overall, while the article provides valuable insights into the importance of preserving historical districts and proposes a novel approach to renewal efforts, it would benefit from presenting a more balanced perspective and providing more evidence to support its claims.