Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

Pappalardo, Luca; Ferres, Leo; Sacasa, Manuel; Cattuto, Ciro; Bravo, Loreto

doi:10.1140/epjds/s13688-021-00284-9

Cited by 31 publications

(26 citation statements)

References 39 publications

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“…In order to find the relationship between these most important locations, first their positions of these locations have to be determined. There are a few approaches used to find home locations via mobile phone data analysis [2,60,54,34,41].…”

Section: Home and Work Locationsmentioning

confidence: 99%

“…Identifying the home and work locations of a subscriber is a common [19][20][21][22][23][24][25][26][27] and crucial part of the CDR processing, as these locations fundamentally determine the people's mobility customs. Furthermore, a good portion of the people live their lives in an area that is determined by only their home and workplace [19,21] or their communities [28].…”

Section: Introductionmentioning

confidence: 99%

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

Pintér

Felde

2022

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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.

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Section: Home and Work Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pintér

Felde

2022

Information

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“…After the right subscribers have been selected, it is common practice to determine users' home and work locations [8][9][10]; then, between these two crucial locations, the commuting trends can be identified. Commuting is studied between cities [9,[11][12][13] or within a city [7,[14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study

Pintér

Felde

2021

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In this study, Call Detail Records (CDRs) covering Budapest for the month of June in 2016 were analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which significantly affected the habit of the residents despite the fact that not a single match was played in the city. We evaluated the fans’ behavior in Budapest during and after the Hungarian matches and found that the mobile phone network activity reflected the football fans’ behavior, demonstrating the potential of the use of mobile phone network data in a social sensing system. The Call Detail Records were enriched with mobile phone properties and used to analyze the subscribers’ devices. Applying the device information (Type Allocation Code) obtained from the activity records, the Subscriber Identity Modules (SIM), which do not operate in cell phones, were omitted from mobility analyses, allowing us to focus on the behavior of people. Mobile phone price was proposed and evaluated as a socioeconomic indicator and the correlation between the phone price and the mobility customs was found. We also found that, besides the cell phone price, the subscriber age and subscription type also had effects on users’ mobility. On the other hand, these factors did not seem to affect their interest in football.

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“…Although the applied approach is practically equivalent to what can be found in the literature, the validity of the results is hard to confirm. Pappalardo managed to validate the home locations in the case of sixty-five subscribers [17], but this is not possible in this case. So, the settlement and -in the case of Budapest -district-based population data [2] is applied from the HCSO.…”

Section: Methodsmentioning

confidence: 99%

“…Pappalardo et al [17] compared the estimated home locations of sixty-five subscribers with the known geographical coordinates of their residence location, using different types of mobile network data: CDR, eXtended Detail Record (XDR) and Control Plane Record (CPR). It has been found that XDRs should be preferred when performing home location detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Commuting Analysis of the Budapest Metropolitan Area using Mobile Network Data

Pintér¹,

Felde²

2022

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The analysis of the human movement patterns based on the mobile network data makes it possible to examine a very large population cost-effectively, and led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data and propose an automatized alternative to the current, questionnaire-based method. Commuting is predominantly analyzed by the census, but that is performed only once in a decade in Hungary. To analyze commuting, the home and the work locations of the subscribers are determined based on their appearances during and outside the working hours. The home locations were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It has been found that commuting analysis based on mobile network data strongly correlates with the census-based findings, even though home and work locations have been estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and demographic distribution of the commuters, show that mobile network data can be an automatized, fast, cost-effective, and relatively accurate way of commuting analysis, that could provide a powerful tool to the sociologists interested in commuting.

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Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

Cited by 31 publications

References 39 publications

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

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

Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study

Commuting Analysis of the Budapest Metropolitan Area using Mobile Network Data

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