Abstract
We describe extracting people significant places for mobility analysis from a real-world large scale dataset collected by mobile operator. The total data consisted of 9.2 billion GPS points including approximately 1.5 million individual user trajectories accumulated for a year. We conducted the experiments on the dataset by using stay point extraction and density based cluster to extracting significant places from a sparse dataset. We also proposed an approach to derive types of locations especially home and work place by using classification features and inference model. The relevant features including ranking in clusters, number of days that data appeared, night time, and day time were identified and evaluated. Several inference models are evaluated in the experiment. With limited number of ground truth data, Random Forest model could achieve 99.2% accuracy for inferring home and work location. Additionally, Spatial Population Census were employed to indirectly compare the classification results with ground truth. Furthermore, to enable real-world application, we presented a technique to utilize Hadoop/Hive, a cloud computing platform, allowing full-scale data processing. As a result, the proposed method is able to discover home and work locations of users with positive results after checking the census. In addition, by using Hadoop platform, an extraction process is able to perform on the whole dataset with only about 1.53 days compared with a single application which took 32.73 days.
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Acknowledgements
The work described in this paper was conducted at Shibasaki Laboratory with an agreement from Zenrin Data Com to use mobile phone dataset of personal navigation service users for the research. This work was supported by GRENE (Environmental Information) project of MEXT (Ministry of Education, Culture, Sports, Science and Technology).
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Witayangkurn, A., Horanont, T., Nagai, M., Shibasaki, R. (2018). Large Scale Mobility Analysis: Extracting Significant Places Using Hadoop/Hive and Spatial Processing. In: Theeramunkong, T., Skulimowski, A., Yuizono, T., Kunifuji, S. (eds) Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems. KICSS 2015. Advances in Intelligent Systems and Computing, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-319-70019-9_17
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