How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

Analysis of the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic

“…As a result, current studies that utilized mobile-device data only focused on the aggregated mobility patterns (e.g., mobility intensity, mobility characteristics) in a specific area ( Kang et al, 2020 ), but few studies have investigated changes in specific activity patterns. However, understanding the extent of changes in specific activity patterns is essential to evaluate the pandemic's influence on different sectors including commerce, transportation, employment, and environment, and so on ( Liu et al, 2021 ).…”

“…To overcome these limitations, some studies have used big data to explore the change of activity pattern during COVID-19. For example, based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, Liu et al (2021) investigated changes in dwelling and working activities during COVID-19 pandemic. The results show that working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas during COVID-19 outbreak.…”

Spatiotemporal pattern of Covid-19 outbreak in Turkey

“…Therefore, The World Health Organizaton has consistently used spatial data analysis to control infectious diseases (Esri, 2020;Nasiri et al, 2021). Within the scope of the research of measures that can be taken to prevent the pandemic, many researchers in different countries have revealed the effects of spatial factors on the fast spread of coronavirus (Adegboye et al, 2021;Casado-Aranda et al, 2021;Castro et al, 2021;Gupta et al, 2021;Li et al, 2021;Liu et al, 2021;Maiti et al, 2021;Mansour et al, 2021;Nasiri et al, 2021;Rubino et al, 2020;Sarkar et al, 2021;Shariati et al, 2020;Tang et al, 2020;Tao et al, 2020;Vaz, 2021;Xiong et al, 2020). Ramírez-Aldana et al (2020) determined that the number of Covid-19 cases in Iranian provinces is spatially related.…”

Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

“…For the same purpose, Rongbo and Qianao et al have introduced a real-time warning model that studies the factors of public opinion on the internet and the dynamic characteristics of epidemic incidents. Therefore, they constructed a vector machine and a logistic regression model in order to enhance the prediction based on COVID-19 data [11]. Following the same motive, Srikanta et al have analyzed COVID-19 mortality and infectious diseases in Europe using spatial regression models.…”

“…At MATLAB's Curve Fitting Tool, we implemented a ninth-degree polynomial for curve fitting corresponding to Italy's cumulative infectious cases. As a result, we generated the curve shown in Figure 4 interpreted by (11) where x is normalized by mean 181 and std 104.4, and the coefficients' (with 95% confidence bounds) goodness of fit results are presented in Table 3.…”