## 5 Conclusions  
This study leverages concepts such as grid density and the GScan algorithm to introduce a Bayesian optimization algorithm that develops the novel RScan algorithm for identifying active regions. It empirically analyzes the second-hand housing transaction activity in Shenyang City, examining its spatio-temporal evolution. The findings indicate variability in the activity levels of Shenyang's second-hand housing market, characterized by \"fluctuating\" changes. The degree of activity varies across different regions, with activity levels distributed in an irregular, ladder-like manner and clustered. This research not only deepens our understanding of the development trends and spatial distribution patterns of Shenyang's second-hand housing market but also aids governmental bodies and related organizations in making informed decisions and regulations. Additionally, it serves as a decision-making reference for investors, developers, and homebuyers. There remains a gap in the research concerning the activity of second-hand housing transactions, and this study enriches the existing literature, offering new theoretical perspectives and foundations for future scholarly research. By adopting an interdisciplinary approach, it extends the application of machine learning to the real estate sector and fosters innovation in data analysis methods and concepts within this field. The effectiveness of the proposed RScan algorithm is validated through evaluation indices and visualization and analysis methods, demonstrating its applicability and potential for broader adoption. As investment demand wanes and owner-occupied demand resurfaces, the cost-effectiveness of the second-hand housing market becomes increasingly significant. Amid consumer downgrading, residents are prioritizing residential qualities over the allure of high-priced new homes. Moreover, the inherent uncertainties and risks associated with new home deliveries contrast sharply with the transparent nature of second-hand homes, which align with homebuyers' expectations of \"what you see is what you get.\" Post-pandemic, as homebuyers gradually regain confidence in their future income prospects, the overall activity in Shenyang's second-hand housing market has declined, yet the scope and volume of transactions have expanded. Some local areas are beginning to show signs of recovery, though full restoration will take time. The visualization results emphasize that the spatial and temporal distribution of second-hand housing transaction activity in Shenyang City is closely tied to locational differences, with highly active areas often featuring rich educational resources, strong transportation links, thriving businesses, or high greenery rates. The next phase of research will focus on developing a semantic mining model to deeply explore the impact of various factors on second-hand housing transaction activity, providing a more detailed foundation and reference for future analyses. The study presented here has certain limitations which are important to acknowledge:  
1. Limitations of time span: The dataset used in this research spans from 2018 to 2023. Although this timeframe allows for an analysis of recent changes in the activity of the second-hand housing market in Shenyang, it may not be sufficiently extensive to fully capture the long-term trends of the market. Given the cyclical nature of real estate markets and the delayed effects of policy changes, this shorter duration could restrict a comprehensive understanding of the broader market dynamics. 2. Lack of research on spatio-temporal dynamic mechanisms: This study primarily documents the shifts in the distribution characteristics of second-hand real estate transaction activity in Shenyang City across different periods. However, it does not delve into the intrinsic dynamic mechanisms driving these changes. Going forward, by addressing the limitations related to the time span, we aim to continue collecting data on second-hand house transactions to enhance the richness of the dataset.