## 4 Experiment
### Discussion  
We designed a method for inferring foreign tourists' nationalities from large-scale mobility traces using location embeddings and LSTM neural network. We demonstrated the hypothesis that different nationalities may not only visit different areas over the territory, but also visit the same locations in a different order, hence proving that the way people move is a good indication of their origins. \begin{table}
\begin{tabular}{c c c c c} \hline
**Travel Distance =** & \(\leq\)**10 km** & **10–25 km** & **25–50 km** & \(\geq\)**50 km** \\ \hline Most Visits & 0.2443 (0.4849) & 0.2757 (0.5368) & 0.2953 (0.5604) & 0.2753 (0.5386) \\ Most Transitions & 0.2557 (0.4986) & 0.2848 (0.5510) & 0.3002 (0.5700) & 0.2938 (0.5557) \\ Markov model & 0.1813 (0.3779) & 0.2398 (0.4999) & 0.3078 (0.5890) & 0.4922 (0.7287) \\ LSTM model & 0.2868 (0.5385) & 0.3533 (0.6267) & 0.4023 (0.6768) & 0.4939 (0.7439) \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy (and accuracy in top 3 in brackets) comparison for different values of traveled distance. \begin{table}
\begin{tabular}{c c c c} \hline
**Location Changes =** & **1–2** & **3–4** & **5–6** \\ \hline Most Visits & 0.2489 (0.4975) & 0.2721 (0.5318) & 0.3122 (0.5847) \\ Most Transitions & 0.2607 (0.5104) & 0.2780 (0.5386) & 0.3370 (0.6139) \\ Markov model & 0.2335 (0.4592) & 0.3556 (0.6131) & 0.5096 (0.7493) \\ LSTM model & 0.3226 (0.5828) & 0.4212 (0.6838) & 0.5215 (0.7691) \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy (and accuracy in top 3 in brackets) comparison for different numbers of location changes. \begin{table}
\begin{tabular}{c c c c c} \hline
**Amoount of Data:** & **\textgreater{}5\%** & **1–5\%** & **0.5–1\%** & **\textgreater{}0.5\%** \\  & **(Five Nationalities)** & **(Ten Nationalities)** & **(Nine Nationalities)** & **(Ten Nationalities)** \\ \hline Most Visits & 0.2567 & 0.0910 & 0.0247 & 0.0328 \\ Most Transitions & 0.2781 & 0.1236 & 0.0306 & 0.0549 \\ Markov model & 0.3776 & 0.3275 & 0.2556 & 0.2344 \\ LSTM model & 0.4110 & 0.3400 & 0.2602 & 0.2784 \\ \hline \end{tabular}
\end{table} 
Table 5: Macro-average F1-score for nationalities in different ranges of amount of data. The percentage value in the first row refers to the amount of data represented by each nationality in that column with respect to the whole dataset. In particular, results show that baseline approaches relying only on the cumulative number of location visits or transitions, therefore representing the overall presence of nationalities over the territory, perform poorly. The Markov model for sequence classification, taking into account each nationality's probabilities of location changes, achieves better results, but its behavior is highly sensitive to motion characteristics. LSTM, specifically designed to find patterns along sequences, substantially outperforms each of the other models, demonstrating the feasibility of correctly identifying nationalities of individual users based on ordered location sequences representing their mobility traces. This reveals that different nationalities move in different ways over the territory. Moreover, the influence of motion characteristics in mobility traces suggests a higher predictability for more distinctive trajectories--that is, for a high number of location transitions or a long traveled distance. This means that highly overlapping motion behaviors between different nationalities (e.g., short movements and high stationarity) negatively affects predictability. This trend is particularly visible for the Markov and LSTM models, highly improving performance as the number of location changes increases or the value of traveled distance grows. Distinctive paths and characteristic traces are more predictable than local movements and stationary behaviors: while many nationalities may move in a similar way in the context of urban activities, the frequent routes of each nationality become more specific and recognizable when it comes to larger-scale mobility.