## 3 Methods  
### Backpropagation Neural Network  
Data visualization within this study was meticulously carried out using ArcGIS 10.4, which provided an intuitive platform for mapping and analyzing the geographical distributions of data points. Concurrently, data analysis and model optimization tasks were conducted using Python, which is a versatile programming language known for its robust libraries and frameworks that support complex data analysis and machine learning algorithms (Figure 3). In the realm of neural networks, the BP neural networks employed here were typically structured with three layers: the input layer, the hidden layer, and the output layer. Each node in the hidden layer was equipped with an activation function, which was crucial for the network's ability to capture non-linear relationships within the data. The use of BP neural networks in this study was pivotal due to their proficiency in handling multilayered learning processes, which was essential for the complex nature of urban data. The choice to utilize a three-layer BP neural network structure was driven by the need for a balance between simplicity and computational efficiency. A three-layer architecture provided sufficient complexity to model non-linear relationships and interactions between variables,  
Figure 2: POI data diagram. facilitating a clear understanding of how inputs influence outputs, which is essential for practical applications in urban planning. The simplicity aided in maintaining model transparency and interchangeability, which are crucial for stakeholders who depend on the clarity of model findings. Additionally, more complex multi-layer networks increase the risk of overfitting, where a model learns the noise in the training data rather than the actual patterns, leading to poor performance on new, unseen data. A simpler three-layer network mitigates the risk and requires less computational power and time to train, which is vital given the large datasets typically involved in urban studies. Furthermore, adhering to the principle of parsimony, or Occam's razor, suggests selecting the simplest model that sufficiently captures the underlying trends. This approach not only ensures a more robust and generalized model but also aligns with empirical evidence that often finds three-layer networks are adequate for effectively modeling complex phenomena. The operational flow of the BP neural network algorithm implemented in research includes several critical steps designed to optimize the accuracy and reliability of the model predictions. These steps are detailed as follows:  
Identification and normalization of feature values: Initially, relevant feature values were identified from the dataset, which involved selecting significant variables that influenced the study outcomes. Subsequently, all data underwent normalization to ensure that the neural network processed values on a comparable scale, which was fundamental for achieving model stability and convergence during training. Parameter setting for network training: After normalizing the data, the parameters of the BP neural network, such as the number of neurons in each layer, the learning rate, and the activation functions, were meticulously established. These parameters were crucial, as they directly influenced the learning efficiency and the predictive performance of the network. Simulation and output evaluation: With the parameters set, the neural network was trained on the data, and model predictions were generated. The output from the neural network was then critically assessed against predefined accuracy metrics. If the output met the set accuracy standards, a reverse normalization process was applied to transform the data back to its original scale, facilitating the practical interpretation and application of the results.