Identification of indoor states of individuals based on mobile phone data

It includes simulated data and codes that support the findings of the study (Identification of indoor states of individuals based on mobile phone data).<br>Abstract: In urban regions, individuals predominantly spend their time indoors. Accurately identifying these indoor states is essential for many fields, such as public health and urban planning. Existing methods generally rely on sensors placed at specific locations or on volunteers’ mobile phones, limiting their applicability to those locations and a fraction of the population. To overcome these limitations, we propose a novel framework that leverages cellular signaling data—providing extensive spatio-temporal and population-wide coverage—to identify individuals’ indoor states comprehensively. We extract three types of features: interaction between individuals’ mobile phones and cells, individuals’ moving and stationary, and environmental context. Using these features, we apply three machine learning models—CatBoost, Random Forest (RF), and Support Vector Machine (SVM)—along with an interpretable machine learning model, Associative Tree (AT), to identify the indoor states. Evaluation with a ground truth dataset shows that CatBoost outperforms the other models, with an F1 score of 97.21% in quantifying the time individuals spend indoors. To our knowledge, this is the first study to identify the indoor states of individuals using cellular signaling data. We argue that this study can contribute to advancements in areas such as public health and urban planning.