## 3 Results  
### Ablation Experimental Design and Results  
#### 3.1.1 Experimental Scheme Design  
In this study, an approach is proposed to enhance the accuracy of neighborhood feature learning in early urban dynamic simulation integration. By combining the strengths of traditional machine learning and deep learning models, the deep learning SNSCNN method is introduced to improve the learning of neighborhood features in conjunction with the traditional RF model. The CA model considers that urban development is primarily influenced by both its own raster development suitability and neighborhood effects. To demonstrate the effectiveness of the RF-SNSCN-CA model in extracting neighborhood features, two scenarios with eight experiments are designed for comparison. The goal is to verify the impact of different approaches to extracting development suitability and neighborhood effects on the simulation results. This study aims to showcase how the RF-SNSCN-CA model enhances the accuracy and effectiveness of neighborhood feature extraction. It has been suggested that developmental suitability can be learned directly together with neighborhood features, so the experimental schemes for extracting developmental suitability are RF [27], SNSCNN, and CNN [28], and for extracting neighborhood effects are SNSCNN, CNN, TN (traditional neighborhood), and NN (no neighborhood), and the module combinations used are shown in Table 1.  
Model I, which is the recommended model in this paper, utilizes the RF-SNSCNN-CA framework. The development suitability probability is obtained using the RF method. The neighborhood constraint component incorporates the SNSCNN model to extract neighborhood features at multiple scales. The central image element's land type, with a specific window size, serves as the label data. The driver factor data, serving as independent variables, are used as input for training the SNSCNN model, which produces the probability for the neighborhood constraint factor. The combined probabilities are obtained by combining restrictive constraints and random perturbations. Taking into account the land use requirements during the observation period, the RF-SNSCNN-CA model is established as an urban land use dynamic simulation model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SNSCNN** & **CNN** & **TN** & **NN** \\ \hline RF & I & II & III & VI \\ SNSCNN & \(\times\) & \(\times\) & IV & VII \\ CNN & \(\times\) & \(\times\) & V & VIII \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Module combination table of the experimental scheme. This model stands out by incorporating the channel attention mechanism to capture spatial variations in the influence of driving factors. Additionally, it utilizes spatial attention to capture neighborhood driving factor characteristics. The integrated probability calculation model is represented as follows:  
\[P_{1}=d_{ijRF}\times\Omega_{ij(SNSCNN)}\times r_{ij}\times t_{ij} \tag{15}\]  
Model II uses the RF-CNN-CA model. Suitability is extracted by RF, and neighborhood effects are extracted by CNN. This model is used to compare the effect of the use or not of the attention mechanism on the neighborhood effect. Its integrated probability calculation model is:  
\[P_{2}=d_{ijRF}\times\Omega_{ij(CNN)}\times r_{ij}\times t_{ij} \tag{16}\]  
Models III, IV, and V use RF, _SNSCNN_, and _CNN_ to extract land development suitability, respectively, combined with CA models for urban dynamic simulation. This class of models is used as an ablation experiment to compare the effects of traditional neighborhood extraction methods on simulation results.