Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

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Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Frontiers in Big Data ◽

10.3389/fdata.2021.718351 ◽

2021 ◽

Vol 4 ◽

Author(s):

A. Potgieter ◽

I. N. Fabris-Rotelli ◽

Z. Kimmie ◽

N. Dudeni-Tlhone ◽

J. P. Holloway ◽

...

Keyword(s):

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

Data Source ◽

Reduced Mobility ◽

Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

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Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

10.20944/preprints202106.0211.v1 ◽

2021 ◽

Author(s):

Arminn Potgieter ◽

Inger Fabris-Rotelli ◽

Zaid Kimmie ◽

Nontembeko Dudeni-Tlhone ◽

Jenny Holloway ◽

...

Keyword(s):

South Africa ◽

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

Reduced Mobility ◽

Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

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Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges

Computational Urban Science ◽

10.1007/s43762-021-00022-x ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Xiao Li ◽

Haowen Xu ◽

Xiao Huang ◽

Chenxiao Guo ◽

Yuhao Kang ◽

...

Keyword(s):

Data Privacy ◽

Large Scale ◽

Human Mobility ◽

Data Sources ◽

Data Uncertainty ◽

Connected Vehicle ◽

Research Experience ◽

Mobility Data ◽

Vehicle Data ◽

Spatial Coverage

AbstractEffectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.

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Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales

10.1101/2020.10.29.20222547 ◽

2020 ◽

Author(s):

Cornelia Ilin ◽

Sébastien Annan-Phan ◽

Xiao Hui Tai ◽

Shikhar Mehra ◽

Solomon Hsiang ◽

...

Keyword(s):

Statistical Models ◽

Human Mobility ◽

The United States ◽

Decision Makers ◽

Disease Dynamics ◽

Epidemiological Models ◽

National Data ◽

Mobility Data ◽

The World ◽

Pharmaceutical Interventions

AbstractPolicymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility — collected by Google, Facebook, and other providers — can be used to evaluate the effectiveness of non-pharmaceutical interventions and forecast the spread of COVID-19. This approach relies on simple and transparent statistical models, and involves minimal assumptions about disease dynamics. We demonstrate the effectiveness of this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world.SummaryBackgroundPolicymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. In some contexts, decision-makers have access to sophisticated epidemiological models and detailed case data. However, a large number of decisions, particularly in low-income and vulnerable communities, are being made with limited or no modeling support. We examine how public human mobility data can be combined with simple statistical models to provide near real-time feedback on non-pharmaceutical policy interventions. Our objective is to provide a simple framework that can be easily implemented and adapted by local decision-makers.MethodsWe develop simple statistical models to measure the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19 at local, state, and national levels. The method integrates concepts from econometrics and machine learning, and relies only upon publicly available data on human mobility. The approach does not require explicit epidemiological modeling, and involves minimal assumptions about disease dynamics. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world.FindingsWe find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections. The first set of results show the impact of NPIs on human mobility at all geographic scales. While different policies have different effects on different populations, we observed total reductions in mobility between 40 and 84 percent. The second set of results indicate that — even in the absence of other epidemiological information — mobility data substantially improves 10-day case rates forecasts at the county (20.75% error, US), state (21.82 % error, US), and global (15.24% error) level. Finally, for example, country-level results suggest that a shelter-in-place policy targeting a 10% increase in the amount of time spent at home would decrease the propagation of new cases by 32% by the end of a 10 day period.InterpretationIn rapidly evolving disease outbreaks, decision-makers do not always have immediate access to sophisticated epidemiological models. In such cases, valuable insight can still be derived from simple statistic models and readily-available public data. These models can be quickly fit with a population’s own data and updated over time, thereby capturing social and epidemiological dynamics that are unique to a specific locality or time period. Our results suggest that this approach can effectively support decision-making from local (e.g., city) to national scales.

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HUMAN MOBILITY DURING THE COVID-19 PANDEMIC IN WESTERN BALKAN COUNTRIES

10.47063/ebtsf.2021.0021 ◽

2021 ◽

Author(s):

Srđan Damjanović ◽

◽

Predrag Katanić ◽

Vesna Petrović ◽

◽

...

Keyword(s):

Human Mobility ◽

Daily Basis ◽

Disease Spread ◽

Handheld Devices ◽

The Balkans ◽

Freedom Of Movement ◽

Mobility Data ◽

The World ◽

History Of ◽

Chinese Province

At the end of 2019, a new coronavirus appeared in the Chinese province of Wuhan, causing the appearance of the disease COVID-19. The disease spread very quickly to other countries in the world, including the Balkans. The governments of many countries have decided to combat the spread of the COVID-19 virus in the community through social distancing measures. Decisions to ban the movement of people were easy to make, but they were very difficult to implement and enforce in practice. Some of the countries monitored their citizens through various applications installed on smartphones. This led to criticism by many NGOs, as they felt that this violated basic human rights of freedom of movement and privacy. Some lawsuits were even filed in the courts because the citizens felt that they were denied rights guaranteed by the respective constitution. Google uses the ability to monitor all those citizens around the world on a daily basis who use smartphones or handheld devices, which provide the option to record the "location history" of the users. This is possible for them, since most people have voluntarily agreed to this option on their devices. In early 2020, Google began publishing global mobility data on a daily basis through a report called “Community Mobility Reports”. The report shows the percentage change in human activity at six grouped locations. Data obtained in the reference days before the outbreak of the COVID-19 pandemic are used as a basis for comparison. In this paper, we studied the dynamics of human mobility during the COVID-19 pandemic in 7 countries of the Balkans: Bosnia and Herzegovina, Serbia, Croatia, North Macedonia, Bulgaria, Greece, and Romania. For Montenegro and Albania Google did not provide data on human mobility. We present the processed data graphically. For all examined countries, we statistically analyzed the obtained data and presented them in a table.

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Mobile Phones, Developing Countries and Learning

Mobile Technologies and Handheld Devices for Ubiquitous Learning ◽

10.4018/978-1-61692-849-0.ch008 ◽

2011 ◽

pp. 120-137

Author(s):

Elba del Carmen Valderrama Bahamóndez ◽

Albrecht Schmidt

Keyword(s):

Developing Countries ◽

Mobile Phones ◽

Learning Process ◽

Internet Access ◽

Developed Countries ◽

The Other ◽

The Internet ◽

Developing Regions ◽

The World ◽

Educational Applications

The Internet and computers are accessible to only half of the population in the world. For the other half, computers and the Internet are almost alien concepts. This half has no medium for gathering information, and they are computer illiterate. In addition, it is well-known, that the use of computers and the Internet, directly and indirectly, enhance the learning process. Therefore, students from under privileged areas of developing regions of the world are, clearly, at a disadvantage compared to their peers in developed countries. However, mobile phones could change this situation. In developing countries, mobile phones are far more accessible than computers or Internet access. This high accessibility together with the multiple functionalities of mobile phones, allow for the potential to build feasible educational applications that enhance the learning experiences of students in developing countries. Such opportunities enable the students’ experiences to be made proportionate to the other half of the world, with a real mechanism for gathering information.

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Human mobility and COVID-19 initial dynamics

Nonlinear Dynamics ◽

10.1007/s11071-020-05854-6 ◽

2020 ◽

Vol 101 (3) ◽

pp. 1901-1919 ◽

Cited By ~ 3

Author(s):

Stefano Maria Iacus ◽

Carlos Santamaria ◽

Francesco Sermi ◽

Spyros Spyratos ◽

Dario Tarchi ◽

...

Keyword(s):

Coming Out ◽

Human Mobility ◽

Mobile Network ◽

Systematic Analysis ◽

Mobile Data ◽

Eu Member States ◽

Mobility Data ◽

Internal Mobility ◽

The Impact ◽

Excess Deaths

Abstract Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14–20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

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Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

Scientific Reports ◽

10.1038/s41598-021-86297-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nishant Kishore ◽

Rebecca Kahn ◽

Pamela P. Martinez ◽

Pablo M. De Salazar ◽

Ayesha S. Mahmud ◽

...

Keyword(s):

Urban Areas ◽

Human Mobility ◽

Unintended Consequences ◽

Disease Spread ◽

Epidemic Spread ◽

Long Distance ◽

Mobility Data ◽

The World ◽

Travel Restrictions ◽

Long Distance Movement

AbstractIn response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

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Public mobility data enables COVID-19 forecasting and management at local and global scales

Scientific Reports ◽

10.1038/s41598-021-92892-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cornelia Ilin ◽

Sébastien Annan-Phan ◽

Xiao Hui Tai ◽

Shikhar Mehra ◽

Solomon Hsiang ◽

...

Keyword(s):

Economic Activity ◽

Statistical Models ◽

Human Mobility ◽

The United States ◽

Disease Dynamics ◽

National Data ◽

Machine Learning Methods ◽

Mobility Data ◽

The World ◽

Pharmaceutical Interventions

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Physicists, stamp collectors, human mobility forecasters

Corvinus Journal of Sociology and Social Policy ◽

10.14267/cjssp.2010.02.07 ◽

2010 ◽

Vol 1 (2) ◽

pp. 129-154

Author(s):

Zoltán Szántó ◽

István Szakadát

Keyword(s):

High School ◽

Action Theory ◽

Human Mobility ◽

The Other ◽

Social Actions ◽

The World

One of the two reviewers studied in high school to be a physicist. In the end, he became something else, but he never lost his awe of physics. The other reviewer never intended to become a physicist, but he sometimes asks himself why he didn’t become one. Today, they are both sociologists who practice their science on an action theory basis and believe that regularities exist in the world of social actions which can be perceived, understood, explained – and even used for making predictions.

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