ARTICLE | doi:10.20944/preprints202301.0083.v2
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: mobile network data; call detail records; geospatial data; data analysis; human mobility; urban mobility; large social event; social sensing; socioeconomic status; machine learning; clustering
Online: 17 January 2023 (12:12:07 CET)
Mobile phones have become an integral part of our lives in the last two decades, leaving a digital trace of our activities and communication. This study aims to develop a data processing framework to evaluate human mobility and socioeconomic status based on call detail records. The methodology proposed first calculates radius of gyration and entropy for each user, then estimates the socioeconomic status by the price and age of the subscribers' phones. Finally, an unsupervised machine learning algorithm was used to group the cells into clusters based on their mobility and socioeconomic metrics. The research showed differences between Buda and Pest during a large scale social event using mobile phone ages and prices. Additionally, the clustering results revealed homogenous groups of cells around Budapest, with similar mobility and socioeconomic metrics. The main conclusion is that mobile network data combined with mobile phone properties offer a useful tool for characterising urban mobility and socioeconomic status.