scholarly journals Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

2017 ◽  
Vol 6 (1) ◽  
pp. 7 ◽  
Author(s):  
Shiwei Lu ◽  
Zhixiang Fang ◽  
Xirui Zhang ◽  
Shih-Lung Shaw ◽  
Ling Yin ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Location Data

Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data

2021 ◽  
Author(s):  
Xiping Yang ◽  
Zhixiang Fang ◽  
Yang Xu ◽  
Ling Yin ◽  
Junyi Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Location Data

Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach

2015 ◽  
Vol 42 (4) ◽  
pp. 625-646 ◽  
Author(s):  
Yang Xu ◽  
Shih-Lung Shaw ◽  
Ziliang Zhao ◽  
Ling Yin ◽  
Zhixiang Fang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Location Data ◽  
Home Based

scholarly journals Detecting and Reducing Biases in Cellular-Based Mobility Data Sets

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 736
Author(s):  
Alicia Rodriguez-Carrion ◽  
Carlos Garcia-Rubio ◽  
Celeste Campo
Keyword(s):  
Mobile Phone ◽  
Mobile Networks ◽  
Human Mobility ◽  
Mobility Model ◽  
Data Sets ◽  
Location Data ◽  
Mobility Data ◽  
Key Factor ◽  
Data Collection Approach ◽  
Call Detail Records

Correctly estimating the features characterizing human mobility from mobile phone traces is a key factor to improve the performance of mobile networks, as well as for mobility model design and urban planning. Most related works found their conclusions on location data based on the cells where each user sends or receives calls or messages, data known as Call Detail Records (CDRs). In this work, we test if such data sets provide enough detail on users’ movements so as to accurately estimate some of the most studied mobility features. We perform the analysis using two different data sets, comparing CDRs with respect to an alternative data collection approach. Furthermore, we propose three filtering techniques to reduce the biases detected in the fraction of visits per cell, entropy and entropy rate distributions, and predictability. The analysis highlights the need for contextualizing mobility results with respect to the data used, since the conclusions are biased by the mobile phone traces collection approach.

The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data

2019 ◽  
Vol 33 (7) ◽  
pp. 1471-1495 ◽  
Author(s):  
Zhiyuan Zhao ◽  
Shih-Lung Shaw ◽  
Ling Yin ◽  
Zhixiang Fang ◽  
Xiping Yang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Location Data ◽  
Temporal Sampling ◽  
Sampling Intervals

scholarly journals Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Adrian M. Tompkins ◽  
Nicky McCreesh
Keyword(s):  
Mobile Phone ◽  
Malaria Transmission ◽  
Gravity Model ◽  
Mobile Phone Data ◽  
Agent Based ◽  
Migration Model ◽  
Location Data ◽  
Home Location ◽  
One Year ◽  
Overnight Stays

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agentbased model can approximately reproduce the patterns of migration involving overnight stays.

scholarly journals Challenges in Contact Tracing by Mining Mobile Phone Location Data for COVID-19: Implications for Public Governance in South Africa

10.28945/4736 ◽  
2021 ◽  
Vol 16 ◽  
pp. 101-124
Author(s):  
Paul Kariuki ◽  
Lizzy O Ofusori ◽  
Prabhakar Rontala Subramanniam ◽  
Moses Okpeku ◽  
Maria L Goyayi
Keyword(s):  
Public Health ◽  
South Africa ◽  
Mobile Phone ◽  
Contact Tracing ◽  
Future Research ◽  
Public Governance ◽  
The Public ◽  
Location Data ◽  
Pertinent Data ◽  
Privacy And Confidentiality

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

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