Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis

With the growing insurrections in Syria in 2011, an exodus in large numbers have emerged. The turmoil and violence have caused mass migration to destinations both within the region and beyond. The current "refugee crisis" has escalated sharply and its impact is widening from neighbouring countries toward Europe. Today, the Syrian crisis is the major cause for an increase in displacement and the resultant dire humanitarian situation in the region. Since the conflict shows no signs of abating in the near future, there is a constant increase in the number of Syrians fleeing their homes. However, questions on the future impact of the Syrian crisis on the scope and scale of this human mobility are still to be answered. As the impact of the Syrian crisis on host countries increases, so does the demand for the analyses of the needs for development and protection in these countries. In this special issue, we aim to bring together a number of studies examining and discussing human mobility in relation to the Syrian crisis.

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How tourism industry agglomeration improves tourism economic efficiency?

Tourism Economics ◽

10.1177/13548166211009116 ◽

2021 ◽

pp. 135481662110091

Author(s):

Zhoufei Li ◽

Huiyue Liu

Keyword(s):

Positive Impact ◽

Regional Analysis ◽

Tourism Industry ◽

Scale Efficiency ◽

Eastern Region ◽

Location Quotient ◽

Industry Agglomeration ◽

Central Regions ◽

National Perspective ◽

The Impact

The agglomeration of the tourism industry has important effects on its efficiency. This article used panel data on the Chinese provincial tourism industry for the 2011–2016 period, applied the location quotient index and three-stage data envelopment analysis method to, respectively, measure the degree of agglomeration and efficiency, and explained the impact of agglomeration on tourism efficiency. The empirical results of this study indicate the following. (1) China’s tourism industry shows a trend towards agglomeration, revealing gradient differences where the highest degree of agglomeration is in the eastern region, followed by the western and central regions. (2) After eliminating random and environmental factors, the adjusted efficiencies are lower than the unadjusted efficiencies. The average overall tourism efficiency is higher in the eastern region than in the central and western regions. (3) From the national perspective, industrial agglomeration can significantly improve the overall efficiency (TE), pure technical efficiency (PTE), and scale efficiency of the tourism industry. (4) Based on regional analysis, the agglomeration of the eastern tourism industry can significantly enhance its TE and PTE. Agglomeration for the western area has a significant positive impact on PTE. There is no significant relationship between agglomeration and efficiency in the central region.

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Impact of temperature and relative humidity on the transmission of COVID-19: a modelling study in China and the United States

BMJ Open ◽

10.1136/bmjopen-2020-043863 ◽

2021 ◽

Vol 11 (2) ◽

pp. e043863

Author(s):

Jingyuan Wang ◽

Ke Tang ◽

Kai Feng ◽

Xin Lin ◽

Weifeng Lv ◽

...

Keyword(s):

Regression Analysis ◽

Relative Humidity ◽

Human Mobility ◽

The United States ◽

Reproductive Number ◽

Cross Sectional ◽

The Usa ◽

Higher Temperature ◽

Potential Impact ◽

The Impact

ObjectivesWe aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status.DesignA retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted.SettingWe use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties.ParticipantsA total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers.Primary outcome measuresRegression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).ResultsStatistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the USA.ConclusionsHigher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (−0.0395 to −0.0125)) in China and by 0.020 (95% CI (−0.0311 to −0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (−0.0108 to −0.0045)) in China and by 0.0080 (95% CI (−0.0150 to −0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.

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Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

BMC Public Health ◽

10.1186/s12889-021-10260-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Meng-Chun Chang ◽

Rebecca Kahn ◽

Yu-An Li ◽

Cheng-Sheng Lee ◽

Caroline O. Buckee ◽

...

Keyword(s):

Disease Surveillance ◽

Initial Conditions ◽

Human Mobility ◽

Human Movement ◽

Disease Spread ◽

Spatial Spread ◽

Movement Data ◽

Intercity Travel ◽

Travel Restrictions ◽

The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

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Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Scientific Reports ◽

10.1038/s41598-021-94060-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaobin Wang ◽

Yun Tong ◽

Yupeng Fan ◽

Haimeng Liu ◽

Jun Wu ◽

...

Keyword(s):

Impact Analysis ◽

Human Mobility ◽

Economic Activities ◽

Underdeveloped Countries ◽

Mobility Data ◽

Long Run ◽

Level Of Economic Development ◽

Human World ◽

Dynamic Relationships ◽

The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

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Static grid carbon factors – Can we do better?

Building Services Engineering Research and Technology ◽

10.1177/0143624421991964 ◽

2021 ◽

pp. 014362442199196

Author(s):

Jamie Risner ◽

Anna Sutherland

Keyword(s):

Carbon Emissions ◽

Regional Analysis ◽

Electricity Consumption ◽

Carbon Intensity ◽

External Temperature ◽

Building Regulations ◽

Calculation Methodology ◽

Alternative Methodology ◽

The Uk ◽

The Impact

The average carbon intensity (gCO2e/kWh) of electricity provided by the UK National Grid is decreasing and becoming more time variable. This paper reviews the impact on energy calculations of using various levels of data resolution (half hourly, daily, monthly and annual) and of moving to region specific data. This analysis is in two parts, one focused on the potential impact on Part L assessments and the other on reported carbon emissions for existing buildings. Analysis demonstrated that an increase in calculated emissions of up to 12% is possible when using an emissions calculation methodology employing higher resolution grid carbon intensity data. Regional analysis indicated an even larger calculation discrepancy, with some regions annual emissions increasing by a factor of ten as compared to other regions. This paper proposes a path forward for the industry to improve the accuracy of analysis by using better data sources. The proposed change in calculation methodology is analogous to moving from using an annual average external temperature to using a CIBSE weather profile for a specific city or using a future weather file. Practical application: This paper aims to quantify the inaccuracy of a calculation methodology in common use in the industry and key to building regulations (specifically Building Regulations Part L – Conservation of Fuel and Power) – translating electricity consumption into carbon emissions. It proposes an alternative methodology which improves the accuracy of the calculation based on improved data inputs.

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Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19

Scientific Reports ◽

10.1038/s41598-021-91392-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lei Lin ◽

Feng Shi ◽

Weizi Li

Keyword(s):

New York ◽

Los Angeles ◽

Traffic Safety ◽

Traffic Accidents ◽

Road Traffic ◽

Human Mobility ◽

Critical Conditions ◽

Road Traffic Safety ◽

Accident Data ◽

The Impact

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

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The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030996 ◽

2021 ◽

Vol 18 (3) ◽

pp. 996

Author(s):

Yun Li ◽

Moming Li ◽

Megan Rice ◽

Haoyuan Zhang ◽

Dexuan Sha ◽

...

Keyword(s):

Growth Rate ◽

Human Mobility ◽

Public Information ◽

Key Factors ◽

Information Campaigns ◽

Rapid Spread ◽

The Us ◽

Policy Measures ◽

The Impact ◽

At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

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The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

Remote Sensing ◽

10.3390/rs13030426 ◽

2021 ◽

Vol 13 (3) ◽

pp. 426

Author(s):

Zheng Qi Wang ◽

Roger Randriamampianina

Keyword(s):

Weather Prediction ◽

Deterministic System ◽

Observation Error ◽

Key Factors ◽

Background Error ◽

Current Configuration ◽

Error Covariance ◽

Regional Reanalysis ◽

The Impact ◽

European Regional

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

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The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

ACM Transactions on Spatial Algorithms and Systems ◽

10.1145/3459625 ◽

2021 ◽

Vol 7 (4) ◽

pp. 1-24

Author(s):

Douglas Do Couto Teixeira ◽

Aline Carneiro Viana ◽

Jussara M. Almeida ◽

Mrio S. Alvim

Keyword(s):

State Of The Art ◽

Human Mobility ◽

Contextual Information ◽

Prediction Method ◽

Mobility Prediction ◽

Mobility Patterns ◽

Distinct Cell ◽

Inherent Nature ◽

The One ◽

The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

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