Spatiotemporal Distribution and Influencing Factors of Theft during the Pre-COVID-19 and COVID-19 Periods: A Case Study of Haining City, Zhejiang, China

Theft is an inevitable problem in the context of urbanization and poses a challenge to people's lives and social stability. The study of theft and criminal behavior using spatiotemporal, big, demographic, and neighborhood data is important for guiding security prevention and control. In this study, we analyzed the theft frequency and location characteristics of the study area through mathematical statistics and hot spot analysis methods to discover the spatiotemporal divergence characteristics of theft in the study area during the pre-COVID-19 and COVID-19 periods. We detected the spatial variation pattern of the regression coefficients of the local areas of thefts in Haining City by modeling the influencing factors using the geographically weighted regression (GWR) analysis method. The results explained the relationship between theft and the influencing factors and showed that the regression coefficients had both positive and negative values in the pre-COVID-19 and COVID-19 periods, indicating that the spatial distribution of theft in urban areas of Haining City was not smooth. Factors related to life and work indicated densely populated areas had increased theft, and theft was negatively correlated with factors related to COVID-19. The other influencing factors were different in terms of their spatial distributions. Therefore, in terms of police prevention and control, video surveillance and police patrols need to be deployed in a focused manner to increase their inhibiting effect on theft according to the different effects of influencing factors during the pre-COVID-19 and COVID-19 periods.

Multi-dimensional characteristics and governance strategies of rumors about the COVID-19 pandemic

This study was based on the rumor data of the National Joint Anti- Rumor Online Platform, data mining and textual analysis were used to analyze the contents of rumors from the spatial, temporal, and semantic dimensions. The results indicated that the number of rumors showed a relatively consistent trend with the development of the pandemic, spatial distributions of rumors were relatively uneven, exhibiting a concentration in large cities. In addition, rumor keywords in different spatiotemporal contexts were all related to the themes of the prevailing outbreak situation and the prevention and control measures, however, the obvious differences were seen as well. The number of rumors was positively correlated with the number of confirmed cases and was easily affected by external factors. © 2022 IEEE.

Distribution Patterns of Ambient Air Quality Pre and During Pandemic in the Urban Area of Yogyakarta, Indonesia

This study identified the spatial distribution pattern of the ambient air quality in the Yogyakarta Urban Area. It was performed to determine the distribution pattern of SO2, CO, and NO2 concentrations for 2016–2019 (pre-pandemic) and 2020 (during pandemic). Furthermore, the spatial analysis was performed using the Inverse Distance Weighting interpolation method. This study proved that spatial modeling using this method has good accuracy, and it is easier to map the distribution pattern of ambient air quality. In 2020, most of the locations met the quality standard (62.64%). As a result, the SO2 and CO parameters immediately showed that most conditions are satisfactory. In 2016, the SO2 parameters met the quality standards at 74.24% of locations. In 2020, the number increased to 85.71%. In addition, the CO parameter reached the quality standard at 81.82% of locations in 2016 and a perfect level of 100% in 2020. This occurred due to the effects of the COVID-19 pandemic because most human and business activities decreased drastically. Therefore, all studies can be used as the basis for air quality modeling and post-COVID-19 predictions. This study is also important as a policy material in the monitoring and management system of ambient air quality in urban areas © 2022, Journal of Ecological Engineering.All Rights Reserved.

Spatiotemporal Distribution and Ecological Risk Assessment of Plastic Additives in Taihu Lake

To reveal the spatiotemporal distribution and risks of plastic additives in Taihu Lake during the COVID-19 pandemic, the occurrences of typical bisphenols, phthalate esters, and benzotriazoles in the surface water of Taihu Lake were investigated. The plastic additives in 19 sites in Taihu Lake were monitored in four seasons, and their potential ecological risks were evaluated. Diethylphthalate (DEP), dimethoxyethyl phthalate (DMEP), benzyl butyl phthalate (BBP), bisphenol A (BPA), and 2-(2H-benzotriazol-2-yl)-4, 6-di-tert-pentylphenol (UV-328) were detected, with detection rates of 100%, 97%, 58%, 98%, and 7%, respectively. During the COVID-19 pandemic, the sharply increasing usage of plastic products did not result in a significant increase in the plastic additives pollution in Taihu Lake. Conversely, the pollution of plastic additives showed a decreasing trend due to reduced human activities. There were significant seasonal differences in the concentrations of plastic additives in Taihu Lake. The average concentrations of plastic additives in spring and summer were 104.7 and 100.3 ng•L-1, respectively, which were higher than those in autumn (30.7 ng•L-1) and winter (29.9 ng•L-1). The plastic additive pollution also showed some differences in spatial distribution. The concentrations of plastic additives near the southwest coast of Taihu Lake were higher than those in other monitoring sites. The presence of plastic additives in Taihu Lake showed low risks to algae with the proportion of 30%. The risks in autumn and winter were higher than those in spring and summer. BPA and UV-328 may have been the main risk factors, which should be of concern. © 2022, Science Press. All right reserved.

Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods.

Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.

Spatiotemporal Characteristics and Patterns of the COVID-19 Pandemic in China: An Empirical Study Based on 413 Cities or Regions.

The global economy was stagnant and even regressed since the outbreak of COVID-19. Exploring the spatiotemporal characteristics and patterns of COVID-19 pandemic spread may contribute to more scientific and effective pandemic prevention and control. This paper attempts to investigate the spatiotemporal characteristics in cumulative confirmed COVID-19 cases, mortality, and cure rate in 413 Chinese cities or regions using the data officially disclosed by the government. The results showed that: (1) The pandemic development can be divided into five stages: early stage (sustained growth), early mid-stage (accelerated growth), mid-stage (rapid growth), late mid-stage (slow growth), and late-stage (stable disappearance); (2) the cumulative number of confirmed COVID-19 cases remained constant in Wuhan, whilst the mortality tended to rise faster from the early stage to the late-stage and the cure rate moved from the southeast to the northwest; (3) the three indicators mentioned above showed significant and positive spatial correlation. Moran's I curve demonstrated an inverted "V" trend in cumulative confirmed COVID-19 cases; the mortality curve was generally flat; the cure rate curve tended to rise. There are apparent differences in the local spatial autocorrelation pattern of the three primary indicators.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data.

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic.

Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag 'Lockdown Diary' recorded from 23 January to April 8, 2020, and 'Nanjing Outbreak' recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.

Spatiotemporal Distribution of Zika Virus and Its Spatially Heterogeneous Relationship with the Environment.

Infectious diseases have caused some of the most feared plagues and greatly harmed human health. However, despite the qualitative understanding that the occurrence and diffusion of infectious disease is related to the environment, the quantitative relations are unknown for many diseases. Zika virus (ZIKV) is a mosquito-borne virus that poses a fatal threat and has spread explosively throughout the world, impacting human health. From a geographical perspective, this study aims to understand the global hotspots of ZIKV as well as the spatially heterogeneous relationship between ZIKV and environmental factors using exploratory special data analysis (ESDA) model. A geographically weighted regression (GWR) model was used to analyze the influence of the dominant environmental factors on the spread of ZIKV at the continental scale. The results indicated that ZIKV transmission had obvious regional and seasonal heterogeneity. Population density, GDP per capita, and landscape fragmentation were the dominant environmental factors affecting the spread of ZIKV, which indicates that social factors had a greater influence than natural factors on the spread of it. As SARS-CoV-2 is spreading globally, this study can provide methodological reference for fighting against the pandemic.

A discernable increase in the severe acute respiratory syndrome coronavirus 2 R.1 lineage carrying an E484K spike protein mutation in Japan.

Three COVID-19 waves in Japan have been characterized by the presence of distinct PANGO lineages (B.1.1. 162, B.1.1.284, and B.1.1.214). Recently, in addition to the B.1.1.7 lineage, which shows 25% abundance, an R.1 lineage carrying the E484K mutation in the spike protein was found to show up to 40% predominance. E484K could be a pivotal amino acid substitution with the potential to mediate immune escape; thus, more attention should be paid to such potential variants of concern to avoid the emergence of mutants of concern. Such comprehensive real-time genome surveillance has become essential for the containment of COVID-19 clusters.

Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia.

The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020.

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

Analysis on epidemic situation and spatiotemporal changes of COVID-19 in Anhui / 中华预防医学杂志

We used the epidemic data of COVID-19 published on the official website of the municipal health commission in Anhui province. We mapped the spatiotemporal changes of confirmed cases, fitted the epidemic situation by the population growth curve at different stages and took statistical description and analysis of the epidemic situation in Anhui province. It was found that the cumulative incidence of COVID-19 was 156/100 000 by February 18, 2020 and the trend of COVID-19 epidemic declined after February 7, changing from J curve to S curve. The actual number of new cases began to decrease from February 2 to February 4 due to the time of case report and actual onset delayed by 3 to 5 days.