Human mobility during religious festivals and its implications on public health in Senegal: A mobile dataset analysis

Scharff, Christelle; Ndiaye, Khadidiatou; Jordan, Meghan; Diène, Aminata Niang; Dramé, Fatou Maria

doi:10.1109/ghtc.2015.7343962

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The possibilities that mobile CDR data affords for analysis of a broad set of problems are surveyed in [4]. Examples of projects conducted with similar data include analysis of disaster resilience [5], infrastructure planning [6], quantifying mobility effects on the spread of infectious diseases [7,8], developing agent based models for disease migrations [9], disease containment [10], analysis of community structures and socio-demographic indicators [11], detection of unusual events [12], poverty analysis [13], mobility during holidays and religious festivals [14], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Salah

Pentland

Lepri

et al. 2019

Guide to Mobile Data Analytics in Refugee Scenarios

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The Data for Refugees (D4R) Challenge is a non-profit challenge initiated to improve the conditions of the Syrian refugees in Turkey by providing a special database to scientific community for enabling research on urgent problems concerning refugees, including health, education, unemployment, safety, and social integration.The collected database is based on anonymised mobile Call Detail Record (CDR) of phone calls and SMS messages of Türk Telekom customers. It indicates broad activity and mobility patterns of refugees and citizens in Turkey for one year. The data collection period is from 1 January

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Section: Introductionmentioning

confidence: 99%

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Salah

Pentland

Lepri

et al. 2019

Guide to Mobile Data Analytics in Refugee Scenarios

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Understanding Mobile Application Usage During the 2014 FIFA World Cup

Domingues

Silva

et al. 2017

Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

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Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

Silva

Domingues

Silva

2018

ACM Trans. Knowl. Discov. Data

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The discovering of patterns regarding how, when, and where users interact with mobile applications reveals important insights for mobile service providers. In this work, we exploit for the first time a real and large-scale dataset representing the records of mobile application usage of 5,342 users during 2014. The data was collected by a software agent, installed at the users’ smartphones, which monitors detailed usage of applications. First, we look for general patterns of how users access some of the most popular mobile applications in terms of frequency, duration, diversity, and data traffic. Next, we mine the dataset looking for temporal patterns in terms of when and how often accesses occur. Finally, we exploit the location of each access to detect users’ points of interest and location-based communities. Based on the results, we derive a model to generate synthetic datasets of mobile application usage and evaluate solutions to predict the next application to be launched. We also discuss a series of implications of the findings regarding telecommunication services, mobile advertisements, and smart cities. This is the first time this dataset is used, and we also make it publicly available for other researchers.

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Human mobility during religious festivals and its implications on public health in Senegal: A mobile dataset analysis

Cited by 4 publications

References 3 publications

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Understanding Mobile Application Usage During the 2014 FIFA World Cup

Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

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