scholarly journals Research on Collective Human Mobility in Shanghai Based on Cell Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 44-62
Author(s):  
Xiyuan Ren ◽  
De Wang
Keyword(s):  
Temporal Variation ◽  
High Frequency ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Indicator System ◽  
Data Sources ◽  
Census Tracts ◽  
Aggregate Distribution ◽  
Human Movements

The high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new opportunities for researchers to understand daily human movements and the structure as a whole. The article aims to explore human spatial movements and their aggregate distribution in Shanghai using large-scale cell phone data. The trajectory of each individual is extracted from cell phone data after data cleansing. Then, an indicator system which includes mobility intensity, mobility stability, influential range, and temporal variation is developed to describe collective human mobility features in census tracts scale. Finally, spatial elements are extracted using the indicator system and the structure of human mobility in Shanghai is discussed.

scholarly journals Tracking the global reduction of marine traffic during the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David March ◽  
Kristian Metcalfe ◽  
Joaquin Tintoré ◽  
Brendan J. Godley
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Marine Traffic ◽  
Human Movements ◽  
Exclusive Economic Zones ◽  
Global Impacts ◽  
Economic Zones

AbstractThe COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have been impacted due to severe restrictions on human movements and changes in consumption. Here, we quantify and map global change in marine traffic during the first half of 2020. There were decreases in 70.2% of Exclusive Economic Zones but changes varied spatially and temporally in alignment with confinement measures. Global declines peaked in April, with a reduction in traffic occupancy of 1.4% and decreases found across 54.8% of the sampling units. Passenger vessels presented more marked and longer lasting decreases. A regional assessment in the Western Mediterranean Sea gave further insights regarding the pace of recovery and long-term changes. Our approach provides guidance for large-scale monitoring of the progress and potential effects of COVID-19 on vessel traffic that may subsequently influence the blue economy and ocean health.

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 Emergence of Urban Growth Patterns from Human Movements

2020 ◽  
Author(s):  
Fengli Xu ◽  
Yong Li ◽  
Chaoming Song
Keyword(s):  
Urban Growth ◽  
Large Scale ◽  
Scaling Laws ◽  
Human Mobility ◽  
Human Movement ◽  
Urban Morphology ◽  
Growth Patterns ◽  
Spatial Correlations ◽  
Movement Model ◽  
Human Movements

Abstract Cities grow in a bottom-up manner, leading to fractal-like urban morphology characterized by scaling laws. Correlated percolation has succeeded in modeling urban geometries by imposing strong spatial correlations. However, the origin of such correlations remains largely unknown. Very recently, our understanding of human movements has been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper proposes a novel human movement model that offers a micro-foundation for the dynamics of urban growth. We compare the proposed model with three empirical datasets, which evidences that strong social couplings and long-memory effects are two fundamental principles responsible for the mystical spatial correlations. The model accounts for the empirically observed scaling laws, but also allows us to understand the city evolution dynamically.

scholarly journals Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haotian Wang ◽  
Abhirup Ghosh ◽  
Jiaxin Ding ◽  
Rik Sarkar ◽  
Jie Gao
Keyword(s):  
Small Group ◽  
Infection Rate ◽  
Large Scale ◽  
Network Effects ◽  
Human Mobility ◽  
Social Activity ◽  
Activity Levels ◽  
Mobility Data ◽  
Human Movements ◽  
The World

AbstractMajor interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

scholarly journals Emergence of Urban Growth Patterns from Human Movements

2021 ◽  
Author(s):  
Fengli Xu ◽  
Yong Li ◽  
Depeng Jin ◽  
Jianhua Lu ◽  
Chaoming Song
Keyword(s):  
Urban Growth ◽  
Large Scale ◽  
Scaling Laws ◽  
Human Mobility ◽  
Urban Morphology ◽  
Growth Patterns ◽  
Spatial Correlations ◽  
Mobility Data ◽  
Human Movements ◽  
Correlated Percolation

Abstract Cities grow in a bottom-up manner, leading to fractal-like urban morphology characterized by scaling laws. Correlated percolation has succeeded in modeling urban geometries by imposing strong spatial correlations. However, the origin of such correlations remains largely unknown. Very recently, our understanding of human movements has been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper designs a novel computational urban growth model that offers a micro-foundation in human mobility behavior. We compare the proposed model with three empirical datasets, which evidences that strong social couplings and long-memory effect are two fundamental principles responsible for the mystical spatial correlations. The model not only accounts for the empirically observed scaling laws, but also allows us to understand the city evolution dynamically.

scholarly journals Large-scale assessment of human mobility during COVID-19 outbreak

2020 ◽  
Author(s):  
Lucas Queiroz ◽  
Lucas Queiroz ◽  
José Luciano Melo ◽  
Gabriel Barboza ◽  
Alysson H. Urbanski ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Population Mobility ◽  
Social Distancing ◽  
Public Health Systems ◽  
Large Scale Assessment ◽  
Strong Control

Social distancing is an important measure to prevent the collapse of public health systems during the COVID-19 pandemic. While some countries have managed to impose a strong control for social distancing, countries like Brazil largely depend on the population’s cooperation. Therefore, it is very necessary to monitor human mobility to detect whether social distancing policies are being implemented and to adjust them in places where the population is not adhering to these. By using cell phone data of millions of people, we were able to assess the population mobility in Brazil’s biggest city, São Paulo. Our analysis revealed the reduction in the circulation of people in most neighborhoods after social distancing policies began. We also showed the dispersion of people by tracking the visits of people to the GRU airport and the visit locations of the same people after they left the airport. Over the course of a few days, it was possible to detect over 70,000 visits across Brazil, with distances greater than 2,000 km from the GRU airport. We hope that data when collected in real time can be useful to stem the progress of the COVID-19 epidemic, or at least to help “flatten the curve”.

scholarly journals Characteristics of human mobility patterns revealed by high-frequency cell-phone position data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Chen Zhao ◽  
An Zeng ◽  
Chi Ho Yeung
Keyword(s):  
High Frequency ◽  
Cell Phone ◽  
Human Mobility ◽  
Short Time Scale ◽  
Mobility Patterns ◽  
Transition Mechanism ◽  
Position Data ◽  
Temporal And Spatial ◽  
The Individual ◽  
Short Time

AbstractHuman mobility is an important characteristic of human behavior, but since tracking personalized position to high temporal and spatial resolution is difficult, most studies on human mobility patterns rely on sparsely sampled position data. In this work, we re-examined human mobility patterns via comprehensive cell-phone position data recorded at a high frequency up to every second. We constructed human mobility networks and found that individuals exhibit origin-dependent, path-preferential patterns in their short time-scale mobility. These behaviors are prominent when the temporal resolution of the data is high, and are thus overlooked in most previous studies. Incorporating measured quantities from our high frequency data into conventional human mobility models shows inconsistent statistical results. We finally revealed that the individual preferential transition mechanism characterized by the first-order Markov process can quantitatively reproduce the observed travel patterns at both individual and population levels at all relevant time-scales.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

scholarly journals Optimizing the Frequency Capping: A Robust and Reliable Methodology to Define the Number of Ads to Maximize ROAS

2021 ◽  
Vol 11 (15) ◽  
pp. 6688
Author(s):  
Jesús Romero Leguina ◽  
Ángel Cuevas Rumin ◽  
Rubén Cuevas Rumin
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Low Frequency ◽  
Digital Marketing ◽  
Optimal Frequency ◽  
Novel Technique ◽  
Maximum Threshold

The goal of digital marketing is to connect advertisers with users that are interested in their products. This means serving ads to users, and it could lead to a user receiving hundreds of impressions of the same ad. Consequently, advertisers can define a maximum threshold to the number of impressions a user can receive, referred to as Frequency Cap. However, low frequency caps mean many users are not engaging with the advertiser. By contrast, with high frequency caps, users may receive many ads leading to annoyance and wasting budget. We build a robust and reliable methodology to define the number of ads that should be delivered to different users to maximize the ROAS and reduce the possibility that users get annoyed with the ads’ brand. The methodology uses a novel technique to find the optimal frequency capping based on the number of non-clicked impressions rather than the traditional number of received impressions. This methodology is validated using simulations and large-scale datasets obtained from real ad campaigns data. To sum up, our work proves that it is feasible to address the frequency capping optimization as a business problem, and we provide a framework that can be used to configure efficient frequency capping values.

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