scholarly journals Using Mobility Data to Understand and Forecast COVID19 Dynamics

2020 ◽  
Author(s):  
Lijing Wang ◽  
Xue Ben ◽  
Aniruddha Adiga ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Spatial Interaction ◽  
Human Mobility ◽  
State Level ◽  
Interaction Patterns ◽  
Disease Dynamics ◽  
Mobility Data ◽  
Spatio Temporal ◽  
The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

scholarly journals The impact of human mobility data scales and processing on movement predictability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamil Smolak ◽  
Katarzyna Siła-Nowicka ◽  
Jean-Charles Delvenne ◽  
Michał Wierzbiński ◽  
Witold Rohm
Keyword(s):  
Prediction Models ◽  
Human Mobility ◽  
Reference Value ◽  
Human Movement ◽  
Temporal Data ◽  
Movement Trajectories ◽  
Mobility Data ◽  
Wide Range ◽  
Spatio Temporal ◽  
The Impact

AbstractPredictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.

scholarly journals Measuring Urban Vibrancy of Residential Communities Using Big Crowdsourced Geotagged Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Pengyang Wang ◽  
Kunpeng Liu ◽  
Dongjie Wang ◽  
Yanjie Fu
Keyword(s):  
Social Interaction ◽  
Urban Geography ◽  
Spatial Configuration ◽  
Human Mobility ◽  
Interaction Patterns ◽  
Scoring Method ◽  
Mining System ◽  
Mobility Data ◽  
Residential Communities ◽  
The Impact

The pervasiveness of mobile and sensing technologies today has facilitated the creation of Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which helps us understand the interactions between humans and cities. In this article, we aim to analyze BCGD, including mobile consumption check-ins, urban geography data, and human mobility data, to learn a model that can unveil the impact of urban geography and human mobility on the vibrancy of residential communities. Vibrant communities are defined as places that show diverse and frequent consumer activities. To effectively identify such vibrant communities, we propose a supervised data mining system to learn and mimic the unique spatial configuration patterns and social interaction patterns of vibrant communities using urban geography and human mobility data. Specifically, to prepare the benchmark vibrancy scores of communities for training, we first propose a fused scoring method by fusing the frequency and the diversity of consumer activities using mobile check-in data. Besides, we define and extract the features of spatial configuration and social interaction for each community by mining urban geography and human mobility data. In addition, we strategically combine a pairwise ranking objective with a sparsity regularization to learn a predictor of community vibrancy. And we develop an effective solution for the optimization problem. Finally, our experiment is instantiated on BCGD including real estate, point of interests, taxi and bus GPS trajectories, and mobile check-ins in Beijing. The experimental results demonstrate the competitive performances of both the extracted features and the proposed model. Our results suggest that a structurally diverse community usually shows higher social interaction and better business performance, and incompatible land uses may decrease the vibrancy of a community. Our studies demonstrate the potential of how to best make use of BCGD to create local economic matrices and sustain urban vibrancy in a fast, cheap, and meaningful way.

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

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.

Mobility Profiling

2014 ◽  
pp. 1-29
Author(s):  
Mirco Nanni ◽  
Roberto Trasarti ◽  
Paolo Cintia ◽  
Barbara Furletti ◽  
Chiara Renso ◽  
...  
Keyword(s):  
Moving Objects ◽  
Human Mobility ◽  
Large Datasets ◽  
Accurate Analysis ◽  
Interesting Phenomenon ◽  
Transportation Management ◽  
Traffic Demand ◽  
Semantic Level ◽  
Mobility Data ◽  
Spatio Temporal

The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network.

scholarly journals Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Yuhao Kang ◽  
Song Gao ◽  
Yunlei Liang ◽  
Mingxiao Li ◽  
Jinmeng Rao ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Interaction ◽  
Human Mobility ◽  
Open Data ◽  
The United States ◽  
Interaction Patterns ◽  
Spatiotemporal Resolution ◽  
Health Crisis ◽  
Spreading Dynamics ◽  
Behaviour Changes

AbstractUnderstanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

scholarly journals Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

2020 ◽  
Vol 12 (22) ◽  
pp. 9662 ◽  
Author(s):  
Disheng Yi ◽  
Yusi Liu ◽  
Jiahui Qin ◽  
Jing Zhang
Keyword(s):  
Traffic Management ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
The Other ◽  
Temporal Data ◽  
Trajectory Data ◽  
Data Field ◽  
Spatio Temporal ◽  
The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

2021 ◽  
Vol 7 (10) ◽  
pp. eabd6989
Author(s):  
Nicole E. Kogan ◽  
Leonardo Clemente ◽  
Parker Liautaud ◽  
Justin Kaashoek ◽  
Nicholas B. Link ◽  
...  
Keyword(s):  
Early Warning ◽  
Data Streams ◽  
Human Mobility ◽  
Warning System ◽  
State Level ◽  
The United States ◽  
Digital Data ◽  
Mobility Data ◽  
Epidemiological Monitoring ◽  
Nonpharmaceutical Interventions

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

scholarly journals Human Mobility and Epidemic Evolution

2021 ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

Abstract The CoViD-19 pandemic ceased to be describable by a susceptible-infected-recovered (SIR) model when lockdowns were enforced. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 (Sudden Acute Respiratory Syndrome Coronavirus 2) in terms of individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

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