How the COVID-19 Pandemic Influences Human Mobility? Similarity Analysis Leveraging Social Media Data

This paper aims to understand the impact of the COVID-19 on human mobility. We explore individual traces through spatial-temporal check-ins on social media. In particular, we leverage geo-tagged tweets, to extrapolate people's geo-locations in New York City (NYC) when they check in Twitter. Building on these data, we perform gyration and travel similarity analysis to study the change of travel pattern during the pandemic. We make a comparison of users' gyration and the number of COVID-19 deaths across time. We find that (1) Users' gyration decreased by 35% after the stay-at-home order. (2) Check-in activities on social media is related to the fear of coronavirus: User's gyration has a negative correlation (-0.7) with the number of deaths across time. (3) Travel similarity decreased by 15% from March 2020 to June 2020 because many people did not travel outside after the stay-at-home order. (4) Inter-personal travel similarity among users was lower than 0.2 and individual traces of a majority of people had no overlap during the pandemic. © 2022 IEEE.

Spatial-temporal monitoring risk analysis and decision-making of COVID-19 distribution by region

The purpose of this study is to model, map, and identify why some areas present a completely different dispersion pattern of COVID-19, as well as creating a risk model, composed of variables such as probability, susceptibility, danger, vulnerability, and potential damage, that characterises each of the defined regions. The model is based on a risk conceptual model proposed by Bachmann and Allgower in 2001, based on the wildfire terminology, analysing the spatial distribution. Additionally, a model based on population growth, chaotic maps, and turbulent flows is applied in the calculation of the variable probability, based on the work of Bonasera (2020). The results for the Portuguese case are promising, regarding the fitness of the said models and the outcome results of a conceptual model for the epidemiological risk assessment for the spread of coronavirus for each region. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients. © 2021 Neural information processing systems foundation. All rights reserved.

CVAS: An Interactive Visual Analytics System for Exploring COVID-19 Information on the Web

The outbreak of COVID-19 has been a critical social event in the past two years. The pandemic has seriously affected the world. Meanwhile, various forms of data about COVD-19 emerge on the Web endlessly, such as SNS discussions, Press releases, WHO statistics, etc. It is valuable work for government departments, news media, and health organizations to integrate and analyze these pandemics-related multi-source data on the web. In this work, we propose an interactive visual analytics system as CVAS that aims at mining and analyzing multi-source data concerned with COVD-19. Having been inspired by the Sankey diagram, we developed a view elaborately. Through appropriate interactions, massive patients' mobility data can be visualized, thus showing the spread features of the pandemic in time and space more specifically. In addition, we collected more than 10,000 trending topics and nearly 10 million related comments on the SNS as Sina Weibo. We performed NLP to analyze their sentiment, identifying key events since the outbreak and the impact of the pandemic on public sentiment. Part of our work was awarded at the China visualization and visual analysis conference (ChinaVis2020) and recognized by peers. © 2022 ACM.

Physical Distancing Analytics for Construction Planning Using 4D BIM

The COVID-19 pandemic has impacted how the construction industry operates around the world. To fight the risk of transmission, new health, safety, and environmental (HSE) protocols have been put in place. Among these protocols are social distancing and limiting the number of workers per area, where social distancing acts as a so-called protective bubble for each worker. Contractors are now required to attempt to achieve (and be prepared to keep) social distancing among their workers whenever needed and possible. Otherwise, they could be forced to halt operations due to having an unsafe environment. Accordingly, construction plans, and corresponding workspace assignments, should be revised in a four-dimensional (4D) environment to ensure fulfillment. Even after the end of this pandemic, the new HSE awareness achieved during this experiment is expected to reshape the so-called new normal of construction. Therefore, this paper presents a novel workspace simulation and management solution comprising a theoretical framework and a semiautomated tool to incorporate physical distancing during 4D planning. The semiautomated tool creates a 4D building information model, loaded with workspaces and social distance bubbles as stochastic variables, and utilizes Monte Carlo simulation to model uncertainties occurring onsite. The uncertainties considered are both temporal and spatial, i.e., changes in productivity and workspace sizes, respectively. This tool surpasses existing workspace management solutions in that (1) it has a schedule generation module to recompute schedule projections based on temporal uncertainties, (2) its workspace generation module can automatically create physical distance buffers around selected workspaces, as per site conditions, (3) its 4D simulation can realistically mimic the work progress on the site, and (4) its 4D clash detection module can smartly detect and report both soft and hard operational clashes. Additionally, the proposed analytics target three levels of clash resolution: site, workspace, and activity level. The framework and developed tool were tested against a residential building case study. Over the course of 155 days, 26 activities with 257 workspace assignments were examined. The proposed solution was able to capture the critical schedule duration (21 out of 155 days), the impactful 4D clashes (44 out of 2,900), and the activities involved in the most sever clashes (5 out of 26). Hence, the proposed method and the developed software tool will help planners/construction managers understand the space requirements for construction operations considering social distancing and other required safety buffering, identify critical spatiotemporal zones, and suggest resolution strategies for the resulting clashes based on the analytics. © 2022 American Society of Civil Engineers.

A Real-Time Human Mobility Visualization of Covid-19 Spread from East Asian Countries

Human mobility using different modes of transportation is constantly increasing. The culture of posting activities while travelling has gained attention on social networks immensely. The evolution of social networking platforms has resulted in an engaging user base multiplying across the globe. The combination of data and information created by these online platforms is massive in terms of the volume and variety of topics. The real-time existence of user-produced information has inspired researchers to analyze material to obtain real-time insight into current affairs. The focus is on collecting tweets of real-time travelling activities for the first 100 days of Covid-19 keeping Chinese Airports as the source. This paper illustrates the multidimensional visualization of real-time covid-19 spread from China neighbouring East Asian countries to the rest of the world. The visualization tools used are python folium, matplotlib networks/graphx, Carto, Tableau, Google Data Studio, and MS Excel. © 2021 IEEE.

Agent-based Modeling as a Tool for Predicting the Spatial-temporal Diffusion of the COVID-19 Pandemic

The article presents the original methodology of using agent-based modeling (ABM) for the numerical simulations of the COVID-19 pandemic's development. The proposed solution makes it possible to analyze changes in the number of cases both in space and time. The devised methodology enables considering spatial conditions in terms of population distribution, such as places of work, rest, or residence, and uses multi-agent modeling to consider spatial interactions. Numerical simulations utilize the spatial and demographic data in GIS databases and the GAMA environment that enables the parameterization of the epidemiological model. Testing the developed methodology on a test area also allowed for checking the effects of a potential decrease or increase in social restrictions numerically. The simulations performed show a high correlation between the level of social distancing and the number of COVID-19 cases. © 2021 IEEE.

Unsupervised data mining on spatial-temporal passenger mobility and survey data during Covid-19

Traditionally, survey data and travel data are considered and analyzed independently. By being able to combine survey data with the respective trip data, this paper analyzes patterns between quantitative mobility data and qualitative survey responses. Firstly, we apply spatial-temporal clustering on the mobility data to understand travel patterns. Secondly, we utilize association rule mining to understand the differences between the clusters. Lastly, we apply association rule mining on the combined mobility and survey data set to understand the perception of Covid-19 related measurements in public transportation. With the created association rules, public transportation authorities can comprehend how different measurements affect the awareness of their services. © 2021 IEEE.

Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user’s preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods. IEEE

Smoothing of time-varying graph with the generalized LASSO

This paper proposes a joint model based on the generalized LASSO to smooth a time-varying graph. The model generalizes the gLASSO from a purely spatial setting to a spatial-temporal one. In the proposed model, the first term measures the fitting error, while the second term incorporates the structural information of graphs and total variations of time sequence, and hence the model can extract both temporal and spatial information. To illustrate the performance of the proposed model, we analyzed the simulated datasets for epidemic diseases and the real datasets for COVID-19 and mortality rate in mainland China. The results show that the proposed model can capture the trends/regions simultaneously in both temporal and spatial domains, being an effective model to analyze the problems that can be modelled as time-varying graphs. Author

A prediction method for population density in key areas

Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas. © 2021 SPIE.