scholarly journals The impact of biases in mobile phone ownership on estimates of human mobility

2013 ◽  
Vol 10 (81) ◽  
pp. 20120986 ◽  
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle ◽  
Abdisalan M. Noor ◽  
Robert W. Snow ◽  
Caroline O. Buckee
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Population Level ◽  
Time Frame ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Human Populations ◽  
Mobility Patterns ◽  
Wide Range ◽  
The Impact

Mobile phone data are increasingly being used to quantify the movements of human populations for a wide range of social, scientific and public health research. However, making population-level inferences using these data is complicated by differential ownership of phones among different demographic groups that may exhibit variable mobility. Here, we quantify the effects of ownership bias on mobility estimates by coupling two data sources from the same country during the same time frame. We analyse mobility patterns from one of the largest mobile phone datasets studied, representing the daily movements of nearly 15 million individuals in Kenya over the course of a year. We couple this analysis with the results from a survey of socioeconomic status, mobile phone ownership and usage patterns across the country, providing regional estimates of population distributions of income, reported airtime expenditure and actual airtime expenditure across the country. We match the two data sources and show that mobility estimates are surprisingly robust to the substantial biases in phone ownership across different geographical and socioeconomic groups.

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 Comparing Sources of Mobility for Modelling the Epidemic Spread of Zika Virus in Colombia

2021 ◽  
Author(s):  
Daniela Perrotta ◽  
Enrique Frias-Martinez ◽  
Ana Pastore y Piontti ◽  
Qian Zhang ◽  
Miguel Luengo-Oroz ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Zika Virus ◽  
Social Impact ◽  
Initial Conditions ◽  
Human Mobility ◽  
National Level ◽  
Surveillance Data ◽  
Added Value ◽  
Mobile Phone Data ◽  
Mobility Patterns

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne disease. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson's r=0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility network in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed network, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data capture a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

scholarly journals Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Satish V. Ukkusuri ◽  
P. Suresh C. Rao
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Recovery Process ◽  
Mobile Phone Data ◽  
Mobility Patterns ◽  
Social Forms ◽  
Recovery Processes ◽  
Mobility Data ◽  
New Perspective

Abstract Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

scholarly journals Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

2019 ◽  
Vol 18 ◽  
Author(s):  
Merkebe Getachew Demissie ◽  
Santi Phithakkitnukoon ◽  
Lina Kattan ◽  
Ali Farhan
Keyword(s):  
Mobile Phone ◽  
Developing Country ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Mobility Patterns

A refined dichotomy on returners and explorers in urban mobility pattern

2019 ◽  
Vol 33 (22) ◽  
pp. 1950251
Author(s):  
Qing-Chao Shan ◽  
Hong-Hui Dong ◽  
Hai-Jian Li ◽  
Li-Min Jia
Keyword(s):  
Mobile Phone ◽  
Urban Areas ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Support Vector ◽  
Radius Of Gyration ◽  
Urban Mobility ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Spatial Indices

With the change in people’s lifestyle and travel mode, understanding the individual and population mobility patterns in urban areas remains to an outstanding problem. Pervasive mobile communication technologies generate voluminous data related to human mobility, such as mobile phone data. To further study the characteristics of returning and exploration patterns of human movement in urban space, a multi-index model is proposed based on the original radius of the gyration index. In this paper, the classification mechanism of a single ratio of the radius of gyration for k-explorers and k-returners is illustrated. Some disadvantages of this mechanism are noted. A few indices of the model are proposed for deep mining of data on human mobility exploration and returning characteristics. Taking a mobile phone data during an entire month as a sample, and after data processing on the Spark platform, the characteristics of various indicators and their correlations are analyzed. The classification effects of different spatial indices for human exploration and returning are compared by using a support vector machine and the binary classification algorithm and are further compared with existing research results. The differences in the classification effects of these indicators are analyzed, which is helpful for in-depth studies of urban mobility patterns.

Using Mobile Phone Data to Determine Human Mobility Patterns in Paris

2018 ◽  
Author(s):  
Eric Valega Prawirodidjojo ◽  
Rui Jie Quek ◽  
Bu-Sung Lee ◽  
Vincent Gauthier ◽  
Markus Schlapfer
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Mobility Patterns

scholarly journals The duration of travel impacts the spatial dynamics of infectious diseases

2020 ◽  
Vol 117 (36) ◽  
pp. 22572-22579 ◽  
Author(s):  
John R. Giles ◽  
Elisabeth zu Erbach-Schoenberg ◽  
Andrew J. Tatem ◽  
Lauren Gardner ◽  
Ottar N. Bjørnstad ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Human Mobility ◽  
Trip Duration ◽  
Disease Dynamics ◽  
Mobility Patterns ◽  
Spatial And Temporal Scales ◽  
The Impact ◽  
Spatial Transmission

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

scholarly journals Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities

2020 ◽  
Vol 12 (9) ◽  
pp. 3631
Author(s):  
Johan Meppelink ◽  
Jens Van Langen ◽  
Arno Siebes ◽  
Marco Spruit
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Scaling Method ◽  
Crucial Step ◽  
Mobility Behaviour ◽  
Data Source ◽  
Proven Method ◽  
Call Detail Records

Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies—for one, during weekends—calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information.

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