## 2 Supervised Approach for Land Use
It is best suited for grid-based spatial data, given its sensitivity to axis-aligned movements commonly found in structured urban environments or grid-like spatial layouts [30]. * Euclidean distance: Considered as the default metric in many machine learning applications, the Euclidean distance measures the straight-line distance between points in a feature space. The geometric nature of the Euclidean distance makes it a baseline metric for evaluating spatial relationships due to its intuitive measure of proximity [30]. * Complexity-invariant distance (CID): The CID incorporates the inherent complexity of data into its computation by adjusting for variability in temporal or spatial patterns. This makes it particularly advantageous for analyzing data with diverse dynamical behaviors, such as call detail records (CDR), where mobility patterns may vary significantly depending on location types and usage scenarios. By normalizing the distances based on pattern complexity, the CID offers a nuanced approach to similarity measurement [31]. The selection of these metrics allows for a comprehensive assessment of kNN performance, taking into account both traditional measures and domain-specific features of the dataset. Ensemble methods, specifically random forest (RF) [32], constitute a more robust way of classification by combining multiple individual classifiers. RFs construct multiple decision trees during training. Each tree is trained on a randomly selected subset of the data and features. The final classification is determined by majority voting among all the trees. This ensemble approach provides several advantages: it reduces overfitting through aggregation, handles non-linear relationships effectively, and provides inherent feature importance measures that can help identify the most discriminative characteristics for place identification. A neural network is a computational model consisting of interconnected layers of nodes (neurons). Each node processes input data by applying weights, biases, and an activation function, passing the result to the next layer. We use a deep learning network (DL) with three layers, each of them with 20, 8, and 10 neurons, which includes an input layer, a hidden layer, and an output layer. The network processes input data through weighted connections and nonlinear activation functions in the hidden layer to extract features and model complex relationships. The cross-entropy loss function used measures the discrepancy between the predicted probability distribution and the true class labels, penalizing incorrect predictions more severely. Algorithm 1 presents our approach in a structured and formal way and describes the process to address the problem of meaningful place identification with the use of supervised classifiers. ```
1 Select a suitable subgrid that covers all the features;
2 Label the chosen set of cells \(T=\{x_{i},y_{i}\}\) according to the places to identify;
3 formethod in [SVM, kNN, RF, DL]do
4 Apply method to \(T\) to learn the function \(h(x_{i})\);
5 Evaluate the accuracy of the outputs \(y^{\prime}_{i}\) in respect to the expected values \(y_{i}\);
6
7 Select the classifier with the best performance \(h^{best}\);
8 Apply \(h^{best}(x^{\prime}_{i})\) to the complete grid to categorize unlabeled patterns \(x^{\prime}_{i}\);
9 Evaluate the results \(Y^{\prime}_{i}\) by comparing them with real evidence;
```  
**Algorithm 1**Supervised approach for solving the place identification problem.