## 4 Results  
After describing the experiments we conducted to determine which approach provides better results in determining home, work, and forest areas, we present the results in this section. All figures for map visualization can be consulted through the reference links. The bold values represent the best results for each algorithm, while the value marked with (*) represent the best result among all the experiments. To identify significant differences among the results, a \(t\)-test [38] with an alpha of 0.05 was conducted. The results with no significant differences with the best one are italicized. Table 2 presents the accuracy obtained from the SVM algorithm with different kernel and parameter configurations. Here, we obtained the best absolute result with smsout and SVM with the polynomial kernel, but no significant differences were found after the \(t\)-test was performed with the rest of the results, except for the SVM with the sigmoid kernel (SVM-S) for all datasets other than callout. Table 3 presents the accuracy obtained from the kNN algorithm with different numbers of neighbors (k) and distance measures. The best result was obtained for smsin and kNN with 10 neighbors (k = 10) and the CI distance. For the \(t\)-test performed, we can see that the smsout dataset had no significant differences for kNN with 10 neighbors and any of the distance measures tested. It also performed similarly with five neighbors and the Euclidean distance. For the Internet dataset, similar results were also obtained for the Manhattan distance with 5 and 10 neighbors and for the CI distance with 10 neighbors. Table 4 presents the accuracy obtained from the RF algorithm with different numbers of estimators, ranging from 50 to 1500. It also presents the DL results for the neural network trained. The best absolute result was obtained from callin for RF with 500 estimators, and for the performed \(t\)-test, we can see that there were no significant differences in any of the estimator settings for the callin dataset. Callout also performed similarly for all the settings except 100 and 500 estimators, and smsin performed similarly with 250 and 500 estimators for the RF classifier. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **smsin** & **smsout** & **Callin** & **Callout** & **Internet** \\ \hline SVM-RO & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.486_ \(\pm\) _0.019_ & _0.486_ \(\pm\) _0.019_ & _0.486_ \(\pm\) _0.019_ \\ SVM-L & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ \\ SVM-RC & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ & _0.490_ \(\pm\) _0.017_ \\ SVM-S & 0.452 \(\pm\) 0.081 & 0.477 \(\pm\) 0.094 & 0.444 \(\pm\) 0.070 & _0.485_ \(\pm\) _0.072_ & 0.436 \(\pm\) 0.064 \\ SVM-P & _0.490_ \(\pm\) _0.025_ & **0.498**\(\pm\) **0.038** & _0.494_ \(\pm\) _0.037_ & _0.490_ \(\pm\) _0.036_ & _0.498_ \(\pm\) _0.040_ \\ \hline \hline \end{tabular}  
* The best result is marked in bold and the results in italic indicates no significant differences with the best according to the \(t\)-test performed  
\end{table} 
Table 2: Performance of different SVM algorithms. Once we have all the results for the different methods, we can visualize the best-performing ones for their predictions in the subgrid. All of the smaller \(4\times 4\) forest subgrids were correctly classified, so the figures show the ground truth values from manual labeling on the left and the classification results on the right. Home is represented in blue, work is in yellow, and forest is in red. Figure 3 shows the SVM classification predictions for the tested subgrids. It shows the smsout results with the polynomial kernel, as it provided the highest accuracy in the tests. We can see that this method fails to properly identify work areas and overestimates home areas, giving an overall bad result for the prediction.