Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

Abstract

In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.

Do Social Event Attendees cluster based on Socioeconomic Status?

“…In our earlier works, the cellphone prices were used to estimate the socioeconomic status [6,7], but the utilised cellphone price data was depreciated causing distortion in the results. In this paper, release prices of the mobile phones are gathered using Generative Pre-trained Transformer 3 (GPT-3) [8] to resolve this issue.…”

“…During data preprocessing, they eliminated CDRs from Subscriber Identity Modules (SIMs), which did not operate in mobile phones using Type Allocation Code (TAC) databases [6]. In another work, they analysed subscribers' wake-up times and explained how it correlates with their socioeconomic status [7]. They also showed that the phone prices in the TAC database might have depreciated [7].…”

“…In another work, they analysed subscribers' wake-up times and explained how it correlates with their socioeconomic status [7]. They also showed that the phone prices in the TAC database might have depreciated [7].…”

Evaluating the Socioeconomic Status of a Large Social Event Attendees

“…The analysis demonstrated a strong positive connection between the two indicators. They also showed that the mobile phone prices in the TAC database might have depreciated [11].…”

Commuting Analysis of the Budapest Metropolitan Area using Mobile Network Data

“…However, in this study, the commuting tendencies of the Budapest Metropolitan Area are analyzed in more detail. In [5], the temporal differences in the mobile network activity between the regions of Budapest are demonstrated, along with the typical times when a group of subscribers "wakes up" from a mobile network perspective. Some analyses regarding the length of the working hours are also presented.…”