Mobility Data-driven Location Type Inference Based on Crowd Voting

Geographic information serves as fundamental data for economic and social development.One of the common and vital type of data in this field is point-of-interest(POI) data.Previously,POI data are collected by map manufacturers,which are costly,have limited spatial coverage,and are not fine-grained enough,affecting the effectiveness of downstream applications.Fortunately,the popularization of the mobile Internet has generated vast amounts of mobility data that reveal the existence of POIs and have the potential to infer their location types.However,such potentiality is challenged by sparse visited locations by users,complex contextual dependency,and random individual behaviors,which are not adequately addressed by existing work.Therefore,we propose a mobility data-driven location type inference method based on crowd voting,namely Milotic.This method refines the task of predicting location types to each trajectory,models complex relationships between locations with graph models,fully retains and integrates fine-grained trajectory context information through check-in embeddings and Bi-LSTM,and overcomes the randomness of individual behaviors through a voting mechanism.Experimental results demonstrate that Milotic achieves weighted F1 score improvements of 7.5% and 13.3% respectively over the best baseline on two real-world mobility datasets.