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.

scholarly journals Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh

2020 ◽  
Vol 17 (167) ◽  
pp. 20190809
Author(s):  
Solveig Engebretsen ◽  
Kenth Engø-Monsen ◽  
Mohammad Abdul Aleem ◽  
Emily Suzanne Gurley ◽  
Arnoldo Frigessi ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Human Mobility ◽  
Mobility Model ◽  
Disease Spread ◽  
Spatial Synchrony ◽  
Mobility Data ◽  
Daily Mobility ◽  
Spatio Temporal ◽  
Good Proxy

Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.

scholarly journals Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh

2020 ◽  
Author(s):  
Solveig Engebretsen ◽  
Kenth Engø-Monsen ◽  
Mohammad Abdul Aleem ◽  
Emily Suzanne Gurley ◽  
Arnoldo Frigessi ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Human Mobility ◽  
Mobility Model ◽  
Disease Spread ◽  
Spatial Synchrony ◽  
Mobility Data ◽  
Daily Mobility ◽  
Spatio Temporal ◽  
Good Proxy

AbstractHuman mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014-2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of Approximate Bayesian Computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.

Unsupervised Learning for Human Mobility Behaviors

2021 ◽  
Author(s):  
Siyuan Liu ◽  
Shaojie Tang ◽  
Jiangchuan Zheng ◽  
Lionel M. Ni
Keyword(s):  
Unsupervised Learning ◽  
Human Mobility ◽  
Data Sets ◽  
Individual Data ◽  
Context Reasoning ◽  
Mobility Data ◽  
Temporal And Spatial ◽  
Mobile Context ◽  
Location Sensing ◽  
Spatial Sparsity

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals Characterizing Pairwise Social Relationships Quantitatively: Interest-Oriented Mobility Modeling for Human Contacts in Delay Tolerant Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jiaxu Chen ◽  
Yazhe Tang ◽  
Chengchen Hu ◽  
Guijuan Wang
Keyword(s):  
Social Relationships ◽  
Mobile Networks ◽  
Human Mobility ◽  
Synthetic Data ◽  
Delay Tolerant Networks ◽  
Mobility Model ◽  
Interaction Matrix ◽  
Mobility Modeling ◽  
The Social ◽  
Delay Tolerant

Human mobility modeling has increasingly drawn the attention of researchers working on wireless mobile networks such as delay tolerant networks (DTNs) in the last few years. So far, a number of human mobility models have been proposed to reproduce people’s social relationships, which strongly affect people’s daily life movement behaviors. However, most of them are based on the granularity of community. This paper presents interest-oriented human contacts (IHC) mobility model, which can reproduce social relationships on a pairwise granularity. As well, IHC provides two methods to generate input parameters (interest vectors) based on the social interaction matrix of target scenarios. By comparing synthetic data generated by IHC with three different real traces, we validate our model as a good approximation for human mobility. Exhaustive experiments are also conducted to show that IHC can predict well the performance of routing protocols.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

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

scholarly journals Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sofonias Tessema ◽  
Amy Wesolowski ◽  
Anna Chen ◽  
Maxwell Murphy ◽  
Jordan Wilheim ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Southern Africa ◽  
Human Mobility ◽  
Genetic Data ◽  
Mobile Phone Data ◽  
Malaria Surveillance ◽  
Travel History ◽  
Cross Border ◽  
Mobility Data ◽  
Regional Coordination

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

scholarly journals Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Hsiao-Han Chang ◽  
Amy Wesolowski ◽  
Ipsita Sinha ◽  
Christopher G Jacob ◽  
Ayesha Mahmud ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Genetic Data ◽  
Epidemiological Surveillance ◽  
Mobile Phone Data ◽  
Spatial Spread ◽  
Control Programs ◽  
Mobility Data ◽  
Imported Infections ◽  
National Control

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

Mobile Phone Tower Tracking

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Mobile Phone ◽  
Travel Behaviour ◽  
Text Messages ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Travel Patterns ◽  
Mobile Phone Use ◽  
Time Space ◽  
Call Detail Records

Tracking tourists using mobile phone data involves collating mobile phone call detail records (CDR), that can determine travel patterns of mobile phone users. The size of the data involved in this style of research is enormous; Xiao, Wang, and Fang (2019) received 600 – 800 million records per day when they used mobile phone data from Shanghai, resulting in over 10 billion mobile phone trajectories. However, mobile phone data does not provide precise travel itineraries. Rather, the data is a series of time-space points, showing where mobile phone users were when they made or received calls or text messages. Inferences are required to determine which mobile phone users are tourists, and when they entered countries or regions. However, the ubiquity of mobile phone use and the size of the data sets available to researchers means that this form of data can be used as a proxy for accommodation and visitation (Xiao, Wang, and Fang, 2019; Ahas et al., 2008; Ahas et al., 2007). Many significant findings regarding travel behaviour have emerged from this technique, including understandings of the impacts of seasonality, the impacts of nationality, and the impacts of events. This chapter will review these findings as well as the challenges that arise from the use of this data.

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