Evaluation of location-data based features using Gaussian mixture models for age group estimation

Yohei Kakimoto, Yuto Omae

Research output: Contribution to journalConference articlepeer-review


Several studies have estimated the demographics and behavioral patterns of users of mobile devices, such as smartphones, using a variety of information obtained from such devices. However, most studies have estimated unknown demographics by correlating the geographical information of users with their mobile device usage histories and social networks. In such cases, significant costs are incurred in preprocessing the data before building an estimation model. Therefore, in this study, we verified whether user demographics can be estimated using only location data obtained from mobile devices. We constructed a machine-learning model that classifies user age groups into two classes, young and elderly, based on the input features generated from location information using a Gaussian-mixture model. By measuring the classification performance of the constructed model, we confirmed that location information contained the information necessary for user attribute estimation. Experimental results confirmed that the classification model constructed based on location information exhibited high classification accuracy for the two classes of equally sampled age groups. These findings indicate that location data contain the necessary information for estimating user demographics.

Original languageEnglish
Article number012070
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2024
Event12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023 - Belgrade, Serbia
Duration: 28 Aug 202331 Aug 2023


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