Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective

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.

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Using mobile phone data to explore spatial-temporal evolution of home-based daily mobility patterns in Shanghai

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) ◽

10.1109/besc.2016.7804481 ◽

2016 ◽

Cited By ~ 2

Author(s):

Zhicheng Liu ◽

Jinbin Yu ◽

Weiting Xiong ◽

Jian Lu ◽

Junyan Yang ◽

...

Keyword(s):

Mobile Phone ◽

Temporal Evolution ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Daily Mobility ◽

Home Based

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Mining individual mobility patterns from mobile phone data

Proceedings of the 2011 international workshop on Trajectory data mining and analysis - TDMA '11 ◽

10.1145/2030080.2030088 ◽

2011 ◽

Cited By ~ 4

Author(s):

Rong Xie ◽

Yanbing Ji ◽

Yang Yue ◽

Xiaoqing Zuo

Keyword(s):

Mobile Phone ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Individual Mobility

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

10.1101/2021.08.09.21261630 ◽

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.

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

Applied Network Science ◽

10.1007/s41109-019-0221-5 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 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.

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