Where You Are Is What You Do: On Inferring Offline Activities From Location Data
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Studies have shown that a person's location can reveal to a high degree of accuracy the type of activity they are engaged in. In this paper we investigate the ability of modern machine learning algorithms in inferring basic offline activities, e.g., shopping and dining, from location data. Using anonymized data of thousands of users of a prominent location-based social network, we empirically demonstrate that not only state-of-the-art machine learning excels at the task at hand (Macro-F1>0.9) but also tabular models are among the best performers. The findings we report here not only fill an existing gap in the literature, but also highlight the potential risks of such capabilities given the ubiquity of location data and the high accessibility of tabular machine learning models.
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