Region Formation for Efficient Offline Location Prediction

As low-power devices continue to be integrated into daily life, we are presented with the challenge of deciding when it's beneficial for these devices to interrupt us. Predicting a user's movements provides valuable insight to solve this problem. Here, the authors present a new approach for offline location prediction for low-power devices, representing a user's mobility patterns as an optimal set of geographical regions. Their approach yielded a 27 percent increase in precision and a 13 percent increase in recall over standard time- and place-based approaches against GPS data for hundreds of users. Their approach requires minimal additional cost and opens up potential for further development.

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