scholarly journals High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hengfang Deng ◽  
Daniel P. Aldrich ◽  
Michael M. Danziger ◽  
Jianxi Gao ◽  
Nolan E. Phillips ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Extreme Weather Events ◽  
Policy Recommendations ◽  
Power Law Distribution ◽  
Mobility Data ◽  
Weather Events ◽  
Destination Choices ◽  
Quantitative Understanding ◽  
Houston Area

AbstractMajor disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviours in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighbourhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighbourhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision-makers, and disaster managers alike.

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

The Impact of Disasters on Japan’s Inbound Tourism Demand

2014 ◽  
Vol 9 (sp) ◽  
pp. 699-708 ◽  
Author(s):  
Lihui Wu ◽  
◽  
Haruo Hayashi ◽  
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Geographical Location ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Tourism Demand ◽  
Intervention Models ◽  
Weather Events ◽  
Inbound Tourism ◽  
The Impact

The purpose of this study is to explore the impact of disasters on international tourism demand for Japan by applying Autoregressive Integrated Moving Average (ARIMA) intervention models that focus on evaluating change patterns and the duration of effects by observing variations in parameters. Japan suffered a variety of disasters, especially natural disasters due to its geographical location, so we have divided these disasters into three types: geological disasters, extreme weather events and “others” such as terrorist attacks, infectious diseases, and economic crises. Based on the principle of preparing for the worst, we selected 4 cases for each disaster type, for 12 in all. Results suggest that (1) large-scale disasters such as great earthquakes impacted negatively on inbound tourism demand for Japan; (2) not all disasters resulted in an abrupt drop in inbound tourist arrivals, extreme weather events, for example, did not decrease inbound tourism demand significantly; (3) impact caused by disasters was temporary.

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

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.

scholarly journals Exploring the Citywide Human Mobility Patterns of Taxi Trips through a Topic-Modeling Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Xiong ◽  
Kaiqiang Xie ◽  
Lu Ma ◽  
Feng Yuan ◽  
Rui Shen
Keyword(s):  
Power Law ◽  
Traffic Management ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Modeling Analysis ◽  
Wide Range

Understanding human mobility patterns is of great importance for a wide range of applications from social networks to transportation planning. Toward this end, the spatial-temporal information of a large-scale dataset of taxi trips was collected via GPS, from March 10 to 23, 2014, in Beijing. The data contain trips generated by a great portion of taxi vehicles citywide. We revealed that the geographic displacement of those trips follows the power law distribution and the corresponding travel time follows a mixture of the exponential and power law distribution. To identify human mobility patterns, a topic model with the latent Dirichlet allocation (LDA) algorithm was proposed to infer the sixty-five key topics. By measuring the variation of trip displacement over time, we find that the travel distance in the morning rush hour is much shorter than that in the other time. As for daily patterns, it shows that taxi mobility presents weekly regularity both on weekdays and on weekends. Among different days in the same week, mobility patterns on Tuesday and Wednesday are quite similar. By quantifying the trip distance along time, we find that Topic 44 exhibits dominant patterns, which means distance less than 10 km is predominant no matter what time in a day. The findings could be references for travelers to arrange trips and policymakers to formulate sound traffic management policies.

scholarly journals Community resilience and urban core shelter implementation: A Wellington case study

2021 ◽  
Author(s):  
◽  
Ralph Peter Titmuss
Keyword(s):  
Climate Change ◽  
Disaster Response ◽  
Large Scale ◽  
Community Resilience ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Indian Ocean Tsunami ◽  
Building Stock ◽  
Weather Events ◽  
The World

<p>As a result of climate change, extreme weather events are becoming more common around the world. Coupled with the ever-present threat of sea level rise that coastal cities face there is a potential for far more severe weather events to occur. This thesis will seek to understand how an existing city can adapt to a more hostile environment, and how in the event of an extreme weather occurrence it maintains its function. There is an urgent need to understand how a city can respond when faced with these situations. Previous extreme weather events, Katrina, the Indian Ocean tsunami, and extreme flooding around the world, highlight the danger of a lack of preparedness and resilience found in most cities.  The purpose of this thesis is to understand how the concept of a core shelter, as a way to address the threats of extreme weather events, can be applied to a well-established urban context, Wellington NZ. A core shelter is a structure that in the event of a large-scale disaster, protects its users, and post-disaster still reaches permanent housing standards without being deemed to be a permanent dwelling. It will also look at whether it is possible to create areas in an existing city that can be considered “safe havens” in the event of an extreme natural incident.  This thesis outlines the need for these shelters by identifying the potential threats of climate change in a Wellington context, and by understanding the vulnerability of Wellington’s current building stock. It reaches a conclusion that through the implementation of core shelters in Wellington NZ, resilience will be improved, disaster response efforts will be aided, and destruction arising from extreme weather events will be reduced. In addition, it identifies the areas of Wellington that are deemed to be of higher risk in a disaster or extreme weather event, analyses an existing building’s potential to become a community resilience/core shelter, and proposes a custom building that could be built on Leeds St and Ghuznee St.</p>

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