Using mobile phone data analysis for the estimation of daily urban dynamics

2017 ◽  
Author(s):  
Danya Bachir ◽  
Vincent Gauthier ◽  
Mounim El Yacoubi ◽  
Ghazaleh Khodabandelou
Keyword(s):  
Data Analysis ◽  
Mobile Phone ◽  
Mobile Phone Data ◽  
Urban Dynamics

scholarly journals The city turned off: Urban Dynamics during the COVID-19 pandemic based on mobile phone data

2021 ◽  
pp. 102524
Author(s):  
Gustavo Romanillos ◽  
Juan Carlos García-Palomares ◽  
Borja Moya-Gomez ◽  
Javier Gutiérrez ◽  
Javier Torres ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Urban Dynamics ◽  
The City

scholarly journals SOMOBIL – Improving Public Transport Planning Through Mobile Phone Data Analysis

2016 ◽  
Vol 14 ◽  
pp. 4478-4485 ◽  
Author(s):  
Daniel Elias ◽  
Friedrich Nadler ◽  
Johannes Stehno ◽  
Jens Krösche ◽  
Manuel Lindorfer
Keyword(s):  
Data Analysis ◽  
Mobile Phone ◽  
Public Transport ◽  
Mobile Phone Data ◽  
Transport Planning

scholarly journals Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis

2020 ◽  
Vol 2 (11) ◽  
pp. e622-e628 ◽  
Author(s):  
Nishant Kishore ◽  
Mathew V Kiang ◽  
Kenth Engø-Monsen ◽  
Navin Vembar ◽  
Andrew Schroeder ◽  
...  
Keyword(s):  
Data Analysis ◽  
Mobile Phone ◽  
Mobile Phone Data ◽  
Common Framework

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

scholarly journals Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamid Khataee ◽  
Istvan Scheuring ◽  
Andras Czirok ◽  
Zoltan Neufeld
Keyword(s):  
Mobile Phone ◽  
Basic Reproductive Number ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Expansion Phase ◽  
Social Distancing ◽  
Exponential Expansion ◽  
Total Death ◽  
Mobility Reduction ◽  
Epidemiological Parameters

AbstractA better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

scholarly journals Did the NSA and GCHQ Diminish Our Privacy? What the Control Account Should Say

2020 ◽  
Vol 7 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Leonhard Menges
Keyword(s):  
Mobile Phone ◽  
Personal Information ◽  
Real Life ◽  
Mobile Phone Data ◽  
Standard Account ◽  
Do So

AbstractA standard account of privacy says that it is essentially a kind of control over personal information. Many privacy scholars have argued against this claim by relying on so-called threatened loss cases. In these cases, personal information about an agent is easily available to another person, but not accessed. Critics contend that control accounts have the implausible implication that the privacy of the relevant agent is diminished in threatened loss cases. Recently, threatened loss cases have become important because Edward Snowden’s revelation of how the NSA and GCHQ collected Internet and mobile phone data presents us with a gigantic, real-life threatened loss case. In this paper, I will defend the control account of privacy against the argument that is based on threatened loss cases. I will do so by developing a new version of the control account that implies that the agents’ privacy is not diminished in threatened loss cases.

Vehicle Travel Path Recognition in Urban Dense Road Network Environments by Using Mobile Phone Data

2021 ◽  
pp. 1-29
Author(s):  
Yudong Guo ◽  
Fei Yang ◽  
Peter Jing Jin ◽  
Haode Liu ◽  
Sai Ma ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Road Network ◽  
Mobile Phone Data ◽  
Travel Path ◽  
Network Environments

Analysis of urban agglomeration structure through spatial network and mobile phone data

2021 ◽  
Author(s):  
Xintao Liu ◽  
Jianwei Huang ◽  
Jianhui Lai ◽  
Junwei Zhang ◽  
Ahmad M. Senousi ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Agglomeration ◽  
Mobile Phone Data ◽  
Spatial Network
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