## 3 Results
### Results of the Spatio-Temporal Evolution of Second-Hand Housing Transaction Activity  
This study utilizes the RScan algorithm to visualize the spatial and temporal evolution characteristics of the second-hand housing market activity in Shenyang. The timeframe of the study spans from August 2018 to August 2023, covering five annual intervals. The spatial and temporal patterns of second-hand housing market activity in each region of Shenyang City were derived by analyzing the transaction data at each yearly interval. Figure 4 illustrates the visualization results of the regional spatio-temporal evolution of second-hand housing transaction activity in Shenyang. In the period from 2018 to 2019, as illustrated by the data in Figure 4a and Table 1, the range of active areas and the number of transactions for second homes were the smallest observed over the last five years. The categorization of activity areas during this time was relatively coarse. Generally, the active areas displayed a distribution pattern of \"high in the south and low in the north\", with a \"ladder-like\" stratification that gradually increased from the outer to the inner layers. According to the activity levels, nearly 50% of the area was in a low-activity state. There was only one high-activity area, penetrated by Metro Line 2, situated between the Industrial Exhibition Center and the Century Building metro stations, at the junction of the Hunnan, Heping, and Shenhe Districts. During the 2019-2020 period, the transaction volume of second-hand housing in Shenyang City increased, the range of active regions expanded, and the activity across all levels became more evident in the visualization results. As shown in Figures 1 and 4b, overall activity significantly increased compared to the previous year, yet the \"high in the south and low in the north\" pattern persisted. The distribution of high-activity areas became more scattered, with notable gaps in second-hand real estate transaction activity between these areas and their neighboring regions. Three level 5 activity areas were located in the Yuhong, Tiesi, and Shenhe Districts; level 4 activity areas were in Shenbei New District, Heping District, Shenhe District, and Hunnan District, with nearly 50% in Hunnan District. Level 3 activity areas were mostly concentrated south of the Hun River, while level 2 activity was primarily in Tiesi District, and level 1 activity areas showed a wider distribution. Figure 3: Map of intermediate results of the spatio-temporal evolution of second-hand housing activity by the RScan algorithm and the kernel density estimate. Maps (**a**–**e**) represent the regional mining results of the RScan algorithm for second-hand housing transaction activity in Shenyang city from 2018 to 2023, each figure corresponding to one-year increments and (**f**–**j**) depict the results from the kernel density estimation method for the same periods. Figure 4: _Cont._  
Figure 4: _Cont_. For the period of 2020-2021, the active areas for second-hand housing transactions in Shenyang City mainly expanded in Huanggu District, typically within low-level-activity areas. The overall activity showed a downward trend, and the range of active areas stabilized. The north-south disparity still existed but was less pronounced than in the previous year. The number of high-activity areas significantly decreased, with the only level 5 activity area remaining between the Human and Shenhe Districts, consistent with the previous year. The Hunnan District's second-hand real estate market gradually emerged as a leading area of activity. There were four level 4 activity areas, with one in Yuhong District and two in Hunnan District, consistent with the previous year, and one spanning the Heping and Hunnan Districts, marking a shift northward from last year's active areas. The distribution of level 2 and 3 activity areas across the Hun River was relatively balanced, with clustering that was irregular. In the 2021-2022 period, there was a general decline in activity among the high-activity level areas.