Varieties of mobility measures: Comparing survey and mobile phone data during the COVID-19 pandemic

Measures to reduce individual mobility are prime governmental non-pharmaceutical interventions to curb infection rates during a pandemic. To evaluate the effectiveness of these efforts scientific research relies on a variety of mobility measures that commonly stem from two main data sources: survey-self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using GSM and GPS data. Spanning March 2020 until April 2021 this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (i) GSM data and (ii) Google's Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing different subgroups. Differences emerge if subgroup differences are compared between mobility estimates. Overall, our findings suggest that these mobility measures manage to capture similar latent variables but researchers should be aware of the specific form of mobility different data sources measure.

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Socio-economic indicators correlate with daily mobility during the second-wave of the Covid-19 pandemic

10.31235/osf.io/tca34 ◽

2021 ◽

Author(s):

Jed Long ◽

Chang Ren

Keyword(s):

Positive Association ◽

Economic Indicators ◽

Mobility Patterns ◽

Mobility Data ◽

Individual Mobility ◽

Mobility Behaviour ◽

The Impact ◽

Second Wave ◽

Pharmaceutical Interventions ◽

Over Time

Non-pharmaceutical interventions are being used globally to limit the spread of Covid-19, which are in turn affecting individual mobility patterns. Mobility measures were found to be strongly associated with regional socio-economic indicators during the first wave of the pandemic. Here, we use network mobility data from an ~3.5 million person sample of individuals in Ontario, Canada to study the association between three different individual-mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We demonstrate that understanding how mobility behaviours have changed in response to Covid-19 varies considerably depending on how mobility is measured. We find a strong positive association between different mobility levels and the economic deprivation index, which demonstrates that inequities in the changes to mobility across economic gradients observed during the initial lockdown have persisted into the later stages of the pandemic. However, the associations between mobility and other socio-economic indicators vary over time. We capture a strong day-of-week pattern of association between socio-economic indicators and mobility levels. Our findings have important implications for understanding if and how mobility data should be used to study the impact of non-pharmaceutical interventions on the socio-economic conditions across geographical space, and over time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility behaviour, and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19 on local populations.

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Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

JMIR Research Protocols ◽

10.2196/24432 ◽

2020 ◽

Vol 9 (12) ◽

pp. e24432

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432

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Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics (Preprint)

10.2196/preprints.24432 ◽

2020 ◽

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

BACKGROUND Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). OBJECTIVE Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). METHODS We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. RESULTS This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. CONCLUSIONS Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/24432

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UK Fiscal Squeezes over a Century

10.1093/oso/9780198779612.003.0002 ◽

2017 ◽

Author(s):

Christopher Hood ◽

Rozana Himaz

Keyword(s):

Data Sources ◽

Fiscal Consolidation ◽

Financial Outcomes ◽

Financial Conditions ◽

Deficit Reduction ◽

Before And After ◽

The Uk ◽

Changes Over Time ◽

Spending Cuts ◽

Over Time

This chapter draws on historical statistics reporting financial outcomes for spending, taxation, debt, and deficit for the UK over a century to (a) identify quantitatively and compare the main fiscal squeeze episodes (i.e. major revenue increases, spending cuts, or both) in terms of type (soft squeezes and hard squeezes, spending squeezes, and revenue squeezes), depth, and length; (b) compare these periods of austerity against measures of fiscal consolidation in terms of deficit reduction; and (c) identify economic and financial conditions before and after the various squeezes. It explores the extent to which the identification of squeeze episodes and their classification is sensitive to which thresholds are set and what data sources are used. The chapter identifies major changes over time that emerge from this analysis over the changing depth and types of squeeze.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020073 ◽

2021 ◽

Vol 10 (2) ◽

pp. 73

Author(s):

Raquel Pérez-Arnal ◽

David Conesa ◽

Sergio Alvarez-Napagao ◽

Toyotaro Suzumura ◽

Martí Català ◽

...

Keyword(s):

Mobile Phones ◽

Human Mobility ◽

The Other ◽

Data Sources ◽

Experimental Setting ◽

Mobile Data ◽

Restriction Patterns ◽

Mobility Data ◽

The World ◽

Proper Interpretation

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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Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

Sensors ◽

10.3390/s18092972 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2972 ◽

Cited By ~ 3

Author(s):

Jorge Rodríguez ◽

Ivana Semanjski ◽

Sidharta Gautama ◽

Nico Van de Weghe ◽

Daniel Ochoa

Keyword(s):

Hierarchical Clustering ◽

Market Segmentation ◽

Wearable Sensors ◽

Essential Element ◽

Cost Effective ◽

Mobile Phone Data ◽

Mobility Data ◽

Tourism Management ◽

Clustering Approach ◽

Spatio Temporal

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.

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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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Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework

Environment and Planning B Urban Analytics and City Science ◽

10.1177/2399808320924433 ◽

2020 ◽

Vol 47 (8) ◽

pp. 1440-1455 ◽

Cited By ~ 3

Author(s):

Tianren Yang

Keyword(s):

Travel Demand ◽

Spatial Scales ◽

Predictive Ability ◽

Mobile Phone Data ◽

City Centre ◽

Planning Support ◽

Economic Framework ◽

City Regions ◽

Commuting Patterns ◽

Changes Over Time

In order to contain commuting distance growth and relieve traffic burden in mega-city regions, it is essential to understand journey-to-work patterns and changes in those patterns. This research develops a planning support model that integrates increasingly available mobile phone data and conventional statistics into a theoretical urban economic framework to reveal and explain commuting changes. Base-year calibration and cross-year validation were conducted first to test the model’s predictive ability. Counterfactual simulations were then applied to help local planners and policymakers understand which factors lead to differences in commuting patterns and how different policies influence various categorical zones (i.e. centre, near suburbs, sub-centres and far suburbs). The case study of Shanghai shows that jobs–housing co-location results in shorter commutes and that policymakers should be more cautious when determining workplace locations as they play a more significant role in mitigating excessive commutes and redistributing travel demand. Furthermore, land use and transport developments should be coordinated across spatial scales to achieve mutually beneficial outcomes for both the city centre and the suburbs. Coupled with empirical evidence explaining commuting changes over time, the proposed model can deliver timely and situation-cogent messages regarding the success or failure of planned policy initiatives.

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Agency notification and retrospective self-reports of childhood maltreatment in a 30-Year cohort: Estimating population prevalence from different data sources

Child Abuse & Neglect ◽

10.1016/j.chiabu.2020.104744 ◽

2020 ◽

Vol 109 ◽

pp. 104744

Author(s):

Jackob M. Najman ◽

Steve Kisely ◽

James G. Scott ◽

Lane Strathearn ◽

Alexandra Clavarino ◽

...

Keyword(s):

Childhood Maltreatment ◽

Data Sources ◽

Population Prevalence ◽

Self Reports

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Validity and Reliability of Alcoholics' Self-Reports of Use of Alcohol Submitted before and after Treatment

Psychological Reports ◽

10.2466/pr0.1980.46.3.999 ◽

1980 ◽

Vol 46 (3) ◽

pp. 999-1005 ◽

Cited By ~ 21

Author(s):

Edmund J. Freedberg ◽

William E. Johnston

Keyword(s):

Drinking Behavior ◽

Data Sources ◽

Validity And Reliability ◽

High Agreement ◽

Return Rates ◽

Self Reports ◽

Before And After ◽

The Subject ◽

Adequate Data

The subjects were 239 alcoholics who participated in a treatment program for employed alcoholics. Reports on their drinking behavior were obtained at four points: immediately prior to treatment, and at 3, 6, and 12 mo. of the year following residential treatment. Four data sources were used: the subject, his spouse if any (133 were married), his therapist, and his work supervisor. The results indicated high agreement among all four sources on the subjects' drinking behavior, suggesting that any one of the four sources could provide adequate data for program evaluation. It was noted that return rates from all sources decreased during the follow-up year and that a higher proportion of subjects could be assessed by using several data sources.

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