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.

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Key frame extraction method for lecture videos based on spatio-temporal subtitles.

Affected by the Corona Virus Disease 2019 (COVID-19), online lecture videos have witnessed an explosive growth. In the face of massive videos, this paper proposes a method for extracting key frames of lecture videos based on spatio-temporal subtitles, which can efficiently and quickly obtain effective information. Firstly, the spatio-temporal slices of subtitle area of the video sequence are extracted and spliced along the time axis to construct the video spatio-temporal subtitle. Then, the video spatio-temporal subtitle is processed in binarization, and the projection method is used to construct the SSPA curve of the video spatio-temporal subtitle. Finally, a selection method for steady-state key frame is designed, that is, the key frame extraction is realized by combining curve edge detection and subtitle existence threshold, which ensures the robustness of the proposed method. The test results of 8 videos show that the average value of the comprehensive index F1-score of the key frame extracted by the algorithm can reach 0.97, the average precision is 0.97, and the average recall rate is 0.98. It can effectively extract the key frames in lecture videos, and compared with other algorithms, the average running time is reduced to 0.072 of the original, which is helpful to extract video information quickly and accurately.

Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda.

INTRODUCTION: Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions. RESULTS: The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda. CONCLUSION: The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges.

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and CODMn and NH3-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average CODMn concentration exhibited a "falling, rising, and falling" trend, while the average NH3-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH3-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.

We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, rm2s2+2, remarkably lower than msrm+1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

Spatio-temporal patterns of Chinese urban recovery and system resilience under the pandemic new normal

Enhancing urban resilience is an important measure to improve preparedness to public health challenges;therefore, understanding the patterns and determinants of urban recovery is of great significance for sustainable urban development under the pandemic new normal. We first propose an analytical framework of urban recovery capacity, and then apply the geographical detector model and geographic weighted regression model to investigate the dynamic characteristics of urban resilience and urban recovery capacity under the impact of COVID-19 in China. The results show that the overall pattern of vitality recovery follows the U-curve;however, the impact of COVID-19 on each region is significantly different, with the highest degree of recovery in the Northwest and East, and the lowest in the Central and West. The geographical detector model reveals that urban resilience indicators can predominantly explain the variations of urban recovery across cities. The geographically weighted regression model shows that environmental resilience, infrastructure resilience, and social resilience are positively correlated with urban recovery capacity, while economic resilience cannot improve urban recovery capacity in the short term. We suggest promoting urban system diversity and redundancy across different dimensions to enhance urban resilience, but caution that linearly promoting systemic redundancy might harm the long-term sustainability of resource allocations.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Analysis of Spatial and Spatiotemporal Anomalies Using Persistent Homology: Case Studies with COVID-19 Data

We develop a method for analyzing spatial and spatiotemporal anomalies in geospatial data using topological data analysis (TDA). To do this, we use persistent homology (PH), which allows one to algorithmically detect geometric voids in a data set and quantify the persistence of such voids. We construct an efficient filtered simplicial complex (FSC) such that the voids in our FSC are in one-to-one correspondence with the anomalies. Our approach goes beyond simply identifying anomalies;it also encodes information about the relationships between anomalies. We use vineyards, which one can interpret as time-varying persistence diagrams (which are an approach for visualizing PH), to track how the locations of the anomalies change with time. We conduct two case studies using spatially heterogeneous COVID-19 data. First, we examine vaccination rates in New York City by zip code at a single point in time. Second, we study a year-long data set of COVID-19 case rates in neighborhoods of the city of Los Angeles.

COVID-19 Deaths in México: A Spatiotemporal Analysis

Officially named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), COVID-19 was first reported in Wuhan, China, at the end of 2019. By February 2020, Mexico had registered its first confirmed case, and by March, its first death. This chapter examines the spatial and temporal patterns of COVID-19 cumulative deaths in Mexico by municipio and analyzes the spatio-temporal distribution and shifting trends of deaths. The analysis was based on a space-time cube with publicly available data from February 3, 2020, through February 8, 2021. The analysis shows the impact of population density and isolation on COVID-19 cumulative deaths in Mexico. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

In December 2019 an outbreak of a new disease happened, in Wuhan city, China, in which the symptoms were very similar to pneumonia. The disease was attributed to SARS-CoV-2 as the infectious agent and it was called the new coronavirus or Covid-19. In March 2020, the World Health Organization declared a worldwide pandemic of the new coronavirus. We have already counted more than 110 million cases and almost 2.5 million deaths worldwide. In order to assist in decision-making to contain the disease, several scientists around the world have engaged in various efforts, and they have proposed a lot of systems and solutions for tracking, monitoring, and predicting confirmed cases and deaths from Covid-19. Mathematical models help to analyze and understand the evolution of the disease, but understanding the disease was not enough, it was necessary to understand the problem in a quantitative way to lead the decision-making during the pandemic. Several initiatives have made use of Artificial Intelligence, and models were designed using machine learning algorithms with features for temporal and spatio-temporal investigation and prediction of cases of Covid-19. Among the algorithms used are Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Graph Neural Networks (GNNs), Ecological Niche Models (ENMs), Long-Short Term Memory Networks (LSTM), linear regression, and others. And these had good results, and to analyze them, the Root Mean Squared Error (RMSE), Log Root Mean Squared Error (RMSLE), correlation coefficient, and others were used as metrics. Covid-19 presents a huge problem to public health worldwide, so it is of utmost importance to investigate it, and with these two approaches it is possible to track not only how the disease evolves but also to know which areas are at risk. And these solutions can help in supporting decision-making by health managers to make the best decisions for the disease that is in the outbreak. This chapter aims to present a literature review and a brief contribution to the use of machine learning methods for temporal and spatio-temporal prediction of Covid-19, using Brazil and its federative units as a case study. From canonical methods to deep networks and hybrid committee-based, approaches will be investigated. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The COVID-19 Pandemic and the International Geographical Union

Having broken out in late 2019, COVID-19 has resulted in a once-in-a-century health emergency that has rapidly evolved into a global socio-economic crisis. As of March 2022, more than 450 million people were infected by the SARS-CoV-2 virus, the cause of COVID-19, resulting in more than six million deaths (WHO, Coronavirus disease (COVID-19) situation dashboard, 2022). The medical systems of many countries have been stretched to the verge of collapse and more than half of the global labor force has stood down. Not only has the pandemic doubled the number of people at risk of starvation to 270 million (Nature, 589:329-330, 2021), but it also pushed 100 million people into poverty in 2020, triggering the worst global recession since World War II (Blake and Wadhwa, 2020 year in review: the impact of COVID-19 in 12 charts, 2020), and increasing the risk of exposure to other pandemics related to ecosystem degradation (IPBES, Workshop report on biodiversity and pandemics of the intergovernmental platform on biodiversity and ecosystem services. Retrieved from Bonn, Germany, 2020;Yin et al., Geogr Sustain 2(1):68-73, 2021). The normal functioning of many organizations has also been hampered by the pandemic and disruptions to the global travel and tourism industry have been unprecedented. By way of an example, travel restrictions led to the postponement of the 2020 34th International Geographical Congress to the following year and, ultimately, the decision was made to transition to an entirely online format for the event. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model.

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

Changing spatiotemporal patterns for hepatitis of unspecified aetiology in China, 2004-2021: a population-based surveillance study.

Objectives: As global efforts continue toward the target of eliminating viral hepatitis by 2030, the emergence of acute hepatitis of unspecified aetiology (HUA) remains a concern. This study assesses the overall trends and changes in spatiotemporal patterns in HUA in China from 2004 to 2021. Methods: We extracted the incidence and mortality rates of HUA from the Public Health Data Center, the official website of the National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System from 2004 to 2021. We used R software, ArcGIS, Moran's statistical analysis, and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence and mortality of the HUA across China. Results: From 2004 to 2021, a total of 707,559 cases of HUA have been diagnosed, including 636 deaths. The proportion of HUA in viral hepatitis gradually decreased from 7.55% in 2004 to 0.72% in 2021. The annual incidence of HUA decreased sharply from 6.6957 per 100,000 population in 2004 to 0.6302 per 100,000 population in 2021, with an average annual percentage change (APC) reduction of -13.1% (p < 0.001). The same result was seen in the mortality (APC, -22.14%, from 0.0089/100,000 in 2004 to 0.0002/100,000 in 2021, p < 0.001). All Chinese provinces saw a decline in incidence and mortality. Longitudinal analysis identified the age distribution in the incidence and mortality of HUA did not change and was highest in persons aged 15-59 years, accounting for 70% of all reported cases. During the COVID-19 pandemic, no significant increase was seen in pediatric HUA cases in China. Conclusion: China is experiencing an unprecedented decline in HUA, with the lowest incidence and mortality for 18 years. However, it is still important to sensitively monitor the overall trends of HUA and further improve HUA public health policy and practice in China.

Spatial analysis of the spread of Covid-19 and its demographic consequences in the regions of the South of European Russia

The article presents results of the multi-scale analysis of the processes of coronavirus infection spread and its impact on the demographic situation in the world, Russia and regions of the South of the European part of Russia. The methodological basis of the study was the principles of geoinformation monitoring, making it possible to process and visualize large volumes of diverse materials. The information base was statistical data from the Russian and foreign sources reflecting the spread of coronavirus infection at various spatial levels from global to regional-local. The characteristic features of changes in the parameters of the disease during its active expansion are described. The article also deals with dynamics in demographic indicators and identifies trends in their widespread deterioration. The contribution of the South of European Russia macro-region to the all-Russian Covid-19 situation is determined. Development of the coronavirus pandemic at the level of municipal districts is analyzed using individual regions as an example. The study identifies main factors of the Covid-19 pandemic development and demonstrates some of its features and consequences in the largest urban agglomerations.

Characterizing spatiotemporal patterns of elevated PM2.5 exposures in a megacity of China using combined mobile and stationary measurements

Exposure to PM2.5 (particles with an aerodynamic diameter equal to or less than 2.5 μm) is associated with a variety of negative health outcomes. Measurements from sparsely situated air quality monitoring stations (AQMSs) may be inappropriate to represent real PM2.5 exposures, particularly in traffic-related environments. In this study, efforts were made to characterize spatiotemporal variation of PM2.5 pollutions over Shenzhen, China from July 2019 to June 2020 using combined mobile (on-road PM2.5) and stationary (AQMS PM2.5) measurements. Monthly-average concentrations of on-road PM2.5 ranged from 10.4 ± 6.1 to 47.3 ± 23.9 μg/m3, and showed consistent trend with AQMS PM2.5 concentrations which ranged from 8.3 ± 3.1 to 37.2 ± 12.9 μg/m3. On-road PM2.5 and AQMS PM2.5 concentrations dropped by 54.6% and 30.2% in February 2020, probably due to the low anthropogenic emissions during the period of Spring Festival and COVID-19 lockdown. Weekend effect on both on-road and AQMS PM2.5 concentrations was not noticeable. Relative high on-road PM2.5 concentrations were observed during morning and evening rush hours. An "elevated concentration” concept was applied to estimate the influence of emissions on PM2.5 exposures. Elevated concentrations showed strong diurnal and spatial variation, and was about 5.0 μg/m3 on-average. Mappings of on-road PM2.5 and elevated concentrations confirmed the heterogeneity of spatial distribution of PM2.5 exposures in Shenzhen where PM2.5 pollutions were more severe in western and northern areas. Our results highlight the elevated PM2.5 exposures in traffic-related environments, and the inequity in urban exposure levels and health.

Untangling the Integral Impact of Land Use Change, Economic, Ecological and Social Factors on the Development of Burabay District (Kazakhstan) during the Period 1999–2021

Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Spatio-temporal relative risks and priority areas for visceral leishmaniasis control in Brazil, between 2001 and 2020.

Visceral leishmaniasis (VL) is a pressing public health problem in Brazil. The proper implementation of disease control programs in priority areas is a challenge for healthcare managers. The present study aimed to analyze the spatio-temporal distribution and identify high risk areas of VL occurrence in the Brazilian territory. We analyzed data regarding new cases with confirmed diagnosis of VL in Brazilian municipalities, from 2001 to 2020, extracted from the Brazilian Information System for Notifiable Diseases. The Local Index of Spatial Autocorrelation (LISA) was used to identify contiguous areas with high incidence rates in different periods of the temporal series. Clusters of high spatio-temporal relative risks were identified using the scan statistics. The accumulated incidence rate in the analyzed period was 33.53 cases per 100,000 inhabitants. The number of municipalities that reported cases showed an upward trend from 2001 onward, although there was a decrease in 2019 and 2020. According to LISA, the number of municipalities considered a priority increased in Brazil and in most states. Priority municipalities were predominantly concentrated in the states of Tocantins, Maranhão, Piauí, and Mato Grosso do Sul, in addition to more specific areas of Pará, Ceará, Piauí, Alagoas, Pernambuco, Bahia, São Paulo, Minas Gerais, and Roraima. The spatio-temporal clusters of high-risk areas varied throughout the time series and were relatively higher in the North and Northeast regions. Recent high-risk areas were found in Roraima and municipalities in northeastern states. VL expanded territorially in Brazil in the 21st century. However, there is still a considerable spatial concentration of cases. The areas identified in the present study should be prioritized for disease control actions.

Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan.

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance.