1. Introduction
The COVID-19 pandemic has caused an unprecedented crisis worldwide, which has seriously affected economic development, social function, and the lives and health of people globally [
1]. Moreover, the spatiotemporal variability of the outbreak led to great challenges in the prevention and control of the pandemic. The timely aggregation of time series data related to the pandemic, statistical analysis of the pandemic’s spatiotemporal distribution pattern and its changes in characteristics over time, and rapid visualization of pandemic information, can provide effective information support for decision-making, the formulation of measures, and evaluation of its effects. This assists COVID-19 pandemic prevention and control [
2]. However, the complexity of COVID-19 makes it difficult to analyze the spatiotemporal distribution characteristics of infected people globally. One of the major challenges is determining how to use geographic information system (GIS) visualization technology and spatiotemporal statistical analysis methods to quickly and accurately obtain spatiotemporal information about the COVID-19 pandemic, to aid decision-making for social management, and pandemic prevention and control. Most previous research on the spatiotemporal analysis of the pandemic is limited to specific countries or regions, and Asia and the Americas are the geographic regions where the most included studies have been carried out [
3]. Although some researchers have studied the global overview offered by climatic models [
4] and ecological niche models [
5] that indicated the possible fostering of the virus expansion due to climatic conditions at the earliest stage of the COVID-19 pandemic, a global overview of the pandemic’s spatiotemporal distribution characteristics is lacking [
6]. Thus, an investigation of the spatiotemporal evolution of the pandemic that involves mining and analyzing the spatiotemporal variation characteristics of COVID-19 infections from a global perspective is warranted.
One of the most important characteristics of the pandemic is the spatiotemporal uncertainty of its spread. Understanding the spatiotemporal dynamics of COVID-19 is critical to reducing its impact, as this would help clarify the scope of the pandemic and facilitate decision-making and community action [
7]. Thus, we used geographic information technology and various spatial statistical methods to determine the spatiotemporal dynamics of the pandemic’s spread. In this way, we obtained scientific information to help mitigate the outbreak. Spatiotemporal analysis of the pandemic is reflected in the analysis of the geographical distribution characteristics and impact factors related to persons infected with COVID-19 using classical statistical and spatial statistical methods. The classical statistical methods used mainly include statistical description [
8], regression analysis [
1,
6,
9], and correlation analysis [
8,
10], and the spatial statistical methods used mainly include density analysis [
9], spatial autocorrelation [
10,
11,
12,
13], and hot spot analysis [
14]. Meanwhile, classical statistical and spatial statistical methods are often combined to analyze the spatiotemporal distribution characteristics of the pandemic in practical research work.
Classical statistical methods are more frequently used to analyze the correlations between the COVID-19 pandemic and socioeconomic factors. The risk of the impact of the pandemic on each country has been assessed on a global scale, and the correlation between its rating on the vulnerability index of infectious diseases and the number of elderly people in the population has been analyzed based on multiple regression models [
1,
6]. Researchers have investigated whether the prevalence of COVID-19 in Spain has shown seasonal changes due to changes in temperature, humidity, and daylight hours by spatiotemporal analysis of environment-related factors [
15]. Moreover, spatiotemporal analysis technology was used to explore the effect of the daily mean temperature on the cumulative number of COVID-19 cases in Spanish provinces [
16]. The spatial scan statistics method was used to identify the clustering characteristics of COVID-19 cases in New York City, and Box plot and Pearson correlation measure models were used to determine the degree of association between the clustering results and environmental factors [
8]. In addition, spatial autocorrelation and Spearman rank correlation were used to study the spatiotemporal patterns of the COVID-19 pandemic and the factors impacting them [
10]. The spatiotemporal variations of country-specific COVID-19 doubling time and effective reproduction number were examined to provide real time pandemic progression [
17]. The diffusion-based cartograms for visualization of the COVID-19 pandemic were proposed according to the country-level statistical data of the first 150 days of the outbreak [
18].
Spatial statistical methods mainly involve analysis of the spatial distribution characteristics of the COVID-19 pandemic. Getis-Ord
statistics were used to conduct a hotspot analysis of the pandemic in Bangladesh, and the vulnerability zones of COVID-19 were analyzed in combination with the hierarchical model to assess and analyze its spatial and temporal transmission dynamics [
14]. Based on geotagged data, the spatial and temporal distributions of COVID-19 cases in Wuhan, China were analyzed using kernel density analysis and ordinary least-squares’ regression methods [
9]. The spatial heterogeneity of the epidemic in Kenya was analyzed by constructing an epidemic vulnerability index based on geospatial analysis [
19]. The spatial prevalence dynamics of COVID-19 in China were explored with the Moran index [
12]. A spatiotemporal risk source model was developed to predict the geographical distribution of confirmed cases and the areas of high transmission risk in the early stages of the epidemic in China using mobile phone data to measure people’s movement [
20]. Combined with the Moran index and logical model, the spatial and temporal distribution characteristics of COVID-19 in China were analyzed [
13]. A spatiotemporal simulation of the COVID-19 outbreak was conducted using an agent-based modelling method to evaluate the impact of the adopted epidemic control strategy on the prevention and control of the outbreak [
21].
The above-mentioned studies on the spatiotemporal analysis of the COVID-19 pandemic have used GIS thematic maps, such as classification, proportion, and density maps, and line charts to visually present the analysis results. However, effective integration of infected persons’ spatial location and time information and a time series analysis has not been sufficiently considered. Due to the large number of COVID-19 cases, the visual effect expressed by scatter diagrams and scale thematic diagrams is not intuitive. Although thematic maps can express the spatial distribution of the pandemic, they are all mapped for large administrative divisions, such as countries, provinces or states, and the granularity of visual expression is relatively coarse. Moreover, it is not easy to analyze the spatiotemporal variation characteristics and patterns of the pandemic from dual dimensions of time and space. Furthermore, line charts can only express the temporal dimension information of pandemic time series data simply, without considering spatial location information. Using the above visualization methods, it is difficult to effectively aggregate spatiotemporal series data to analyze the changes in characteristics of the spatiotemporal distribution pattern of the COVID-19 pandemic over time. Meanwhile, a single statistical analysis method cannot be used to fully analyze the spatiotemporal pattern of the pandemic and its evolution.
In summary, previous studies that have analyzed the spatiotemporal distribution characteristics of the COVID-19 pandemic have mainly focused on local or regional epidemic situations, and the depiction of the spatiotemporal evolution from a global perspective is lacking. The problem brought by data gaps and heterogeneous reporting has been the principal hindrances of the spatiotemporal analysis model of the COVID-19 pandemic, especially the global-scale model. Meanwhile, spatiotemporal analysis research has failed to fully aggregate the time series data of the spatial, temporal, and attributes of the pandemic. Hence, it is difficult to effectively explore the trends in the evolution of the pandemic over time by using the conventional single information visualization method. In response to these limitations, the purpose of this study is to mine the spatial distribution pattern and evolution characteristics of the COVID-19 pandemic on a global scale by using space-time aggregation and spatial statistics methods. This study uses worldwide data on the COVID-19 pandemic, to create a multidimensional data model with a space-time cube and spatial database to obtain time series data for time slices on different time scales. To analyze the spatiotemporal evolution process of the pandemic from a global perspective, spatiotemporal statistical methods, such as the heat map, geographic mean center, spatial autocorrelation, and emerging spatiotemporal hot spot analysis were comprehensively used to mine and analyze the spatiotemporal distribution characteristics and variation pattern of the pandemic. Such analysis of the COVID-19 pandemic based on spatiotemporal statistics could help countries or regions worldwide to understand the spatiotemporal distribution pattern of the pandemic, allowing for dynamic improved supervision of pandemic prevention and important decision-making support for pandemic prevention and control.
4. Discussion and Conclusions
4.1. Temporal and Spatial Changes in the Global COVID-19 Pandemic
To excavate and analyze the spatiotemporal variation trends in the COVID-19 pandemic from a global perspective, based on global pandemic time series data, we comprehensively analyzed the spatial distribution pattern and its evolution for confirmed cases from multiple dimensions by using a heat map, spatial autocorrelation, geographic mean center, and emerging spatiotemporal hot spot analysis methods. The main findings are as follows: The global spatial and temporal distribution of the confirmed cases is uneven over time and space. The high-density area observed in the heat map of the outbreak, spread from Asia and Europe to North America and South America, while Africa and Oceania were always low-density areas. Asia, Europe, and Africa have had wider geographical centroid migration, while South America, Oceania, and North America have had relatively concentrated geographical centroid migration. There is a significant global spatial autocorrelation of the confirmed cases. Overall, the spatial autocorrelation of confirmed cases gradually increased from the outbreak until September 2020 and then decreased slightly. The spatiotemporal patterns of cold and hot spots of the global pandemic are mainly characterized by oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. Moreover, the spatial distribution range of the cold spot variation pattern is much larger than that of the hot spot variation pattern.
Based on epidemic time series data, heat maps can be used to qualitatively display the spatial distribution density of confirmed cases in different time slices. The heat map aggregates a large amount of discrete pandemic data in a two-dimensional histogram, and it uses a progressive color band to represent the analysis results [
24]. This type of display can intuitively show the density or frequency of confirmed cases, which is better than the display effect of scatter diagrams and proportional bubble diagrams. Therefore, through analyzing the change of heat maps over time, the information of the spatiotemporal distribution characteristics of the COVID-19 outbreak worldwide has been provided from a global perspective. The global Moran index uses spatial location and attribute information of confirmed cases to build a statistical model [
26], and it can be used to quantitatively measure the spatial clustering intensity of confirmed cases globally. Therefore, it can be used to analyze changes in the clustering intensity over time based on the time series data of the confirmed cases, thus making up for the fact that the heat map cannot quantitatively express the spatial distribution density of the pandemic. Thus, the information of the change trend of clustering intensity of the COVID-19 pandemic has been provided by using the global Moran index from a global scale. A quantitative analysis of the geographic mean center migration of outbreaks across six continents was conducted because the index of the geographic mean center considers both spatial locations and the number of confirmed cases at each location. Consequently, the shift range and concentration of the pandemic can be determined by the direction and distance of the migration path of the geographic mean center. Hence, the results of geographic barycenter shift analysis can provide information on the migration path, migration range, and migration direction of the COVID-19 pandemic from a regional scale.
The three abovementioned methods can be used to analyze the spatial distribution characteristics of the pandemic to a certain extent, but the statistical model has some limitations in dealing with time dimension information. It is difficult to analyze spatiotemporal information synchronously and comprehensively using conventional statistical analysis methods. The emerging spatiotemporal hot spot analysis method can be used as a new approach to analyze the spatiotemporal change pattern for the global COVID-19 pandemic, because it can aggregate spatiotemporal information in a multidimensional data model and meet the requirements of time and space continuity [
31]. Meanwhile, the Mann–Kendall method is useful to analyze time series data for the global pandemic, because the test range and effect of this method are not affected by outliers, and the data are not required to follow a certain distribution trend. This method is more suitable for time series analysis without the distribution law [
30]. Therefore, combined with the time series data for the COVID-19 pandemic, the emerging spatiotemporal hot spot analysis method can effectively identify spatiotemporal change patterns of cold and hot spots during the pandemic according to the spatiotemporal data aggregation model, making up for the inability of the conventional hot spot analysis method to fully reveal the evolution pattern of the spatial distribution of the pandemic over time. Thus, through mining and analyzing of the cold and hot spots of the pandemic, the information of the cold and hot spot change patterns of the COVID-19 pandemic worldwide has been provided from a local or regional scale.
When only one conventional statistical method is used, only certain aspects of the spatiotemporal variation characteristics of the COVID-19 pandemic can be analyzed. Therefore, based on global COVID-19 pandemic time series data, the integrated use of a variety of spatiotemporal statistical analysis methods can be more comprehensive, allowing better analysis of the spatiotemporal patterns and trends of confirmed cases from a global perspective [
33]. This avoids the limitation of conventional visualization methods, such as line charts, which cannot consider the shortage of spatial location information for confirmed cases when analyzing pandemic time series data. The World Health Organization believes that the timely use of mathematical models plays an important role in assessing pandemics, making health decisions, and assessing the effectiveness of interventions [
34]. Timely access to the spatial patterns and trends of global pandemics can help governments in various countries or regions to develop control measures based on regional classifications. More so, it can promote precise prevention and control and the overall healthy functioning of society. In the present study, the spatiotemporal patterns and trends of global pandemic spread were explored in a multi-dimensional manner using a variety of spatiotemporal statistical methods. Our results provide information about the spatiotemporal variation of the COVID-19 pandemic from a global perspective that can aid in decision-making and provide a scientific basis to prevent pandemic spread and making pandemic prevention decisions. Therefore, it is of great global significance to continue to strengthen strategies to prevent new cases, control the pandemic and respond to possible changes.
4.2. Influences of the Accuracy and Granularity of COVID-19 Data on Analysis Results
The accuracy of data reporting and the difference in data granularity are the common limitations in the COVID-19 spatiotemporal analysis, and this study is no exception. Because this is a difficult problem to solve, it has become a major obstacle to analyze the spatiotemporal evolution of the COVID-19 pandemic on a global scale. Due to the different methods and standards for nucleic acid testing, and the different epidemic prevention policies in each country or region, the COVID-19 cases reported by each country worldwide may not represent the actual number of infected people. The experimental data used in this study came from the COVID-19 database of Hopkins University, which has collected, processed, and updated the pandemic data reported by various countries or regions worldwide. Although these data are constantly updated, there may be data differences due to different nucleic acid test standards. Because of the above reasons, there may be some differences between the spatiotemporal analysis results based on the reported cases and the spatiotemporal evolution of the COVID-19 pandemic in reality.
In addition, the COVID-19 data used in this study came from nearly 4000 sampling locations around the world. These sampling locations could be in the capital of a relatively small country or the administrative center of a province or state of a large country. As a result, the data used in this study have different spatial scales. For example, in the United States, the COVID-19 data is collected by county-level sampling locations, while in some African countries, there is only one sampling location, namely, the capital of the country. We know that the cold and hot spots, geographic mean centers, and heat maps were calculated according to the samples of the sampling locations. Hence, the spatiotemporal analysis results of countries like the United States with county-level data will be more accurate than those of African countries, because the data granularity is higher. Due to the uncertainty of the spatial location of the COVID-19 pandemic, the spatial resolution of these data obtained by taking the national capital or the administrative center of provinces and states as the sampling location is still low. If we can get higher granularity data, the analysis results of the spatiotemporal evolution characteristics of COVID-19 will be more accurate.
4.3. Limitations and Future Work
Due to differences in data collection procedures or health policies, there is considerable uncertainty about the data available on the COVID-19 pandemic. Except for the United States, spatial information on confirmed cases is only accurate at the national or provincial level. This could have caused a significant amount of spatial information to be ignored. Although this would have affected the accuracy of the spatial analysis to some extent, its influence on the analysis of the spatiotemporal variation characteristics of the pandemic would be relatively small from the perspective of a global scale. It should be noted that this study did not consider the relationships between the spatiotemporal variation characteristics of the pandemic and other impact factors, such as changes in the mobility of people, population density, and temperature. Correlations between the spatiotemporal evolution patterns of the global pandemic and human and natural environmental impact factors could be further explored based on the time series data of the COVID-19 pandemic in future work. In addition, the experimental analysis results for the present study were generated by desktop software, which is inconvenient for global real-time sharing of spatiotemporal variation trends of the COVID-19 pandemic. Hence, it is necessary to further study the use of Web online visualization technology based on time series data for heat map visualization, geographic mean center migration, and the change patterns of cold and hot spots during the COVID-19 pandemic. In the future, an information-sharing service platform could be built to provide timely visual information services to show the spatiotemporal process evolution characteristics of the global COVID-19 pandemic.
4.4. Conclusions
Research on the spatiotemporal analysis of the COVID-19 pandemic has mainly focused on local regions, and analysis from a global perspective is lacking. Furthermore, the use of only one statistical analysis method to analyze spatiotemporal change trends for the global situation is difficult. Thus, based on slice data of the global pandemic on different time scales, this study analyzed the spatiotemporal distribution patterns and evolution from a global perspective using spatiotemporal statistical methods such as heat maps, geographic mean centers, spatial autocorrelations, and an emerging spatiotemporal hot spot analysis.
The experimental analysis results show the following: First, the time sequence analysis of the heat map of the pandemic shows that the high-density area of the outbreak gradually spread from Asia and Europe to North America and South America; Africa and Oceania have consistently been classified as low-density outbreak zones. In terms of the geographical center migration distance of the global pandemic, Asia, Europe, and Africa had relatively wider outbreak ranges, while South America, Oceania, and North America had relatively concentrated outbreak ranges. The analysis of the spatial autocorrelation time series showed significant global spatial autocorrelation for the confirmed cases. Overall, the spatial autocorrelation of the confirmed cases gradually increased from the outbreak until September 2020 and then decreased slightly. The main evolution patterns of the global COVID-19 pandemic were oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. Oscillating hot spots were identified as the main hot spot pattern, mainly distributed in the Americas, Europe, the Middle East, and some parts of southern Asia. The spatial distribution of the cold spot patterns of the confirmed cases was found to be much wider than that of the hot spot patterns. The identified evolution patterns of cold spots mainly included intensifying cold spots, persistent cold spots, and diminishing cold spots, which were mainly found to be distributed in Oceania, most of Asia and Africa, and very small parts of both the Americas and Northern Europe.
Compared with a single statistical analysis method, the comprehensive application of multiple spatial statistical methods can be used to more intuitively and effectively analyze the evolution trends of the spatial and temporal distribution patterns of global confirmed COVID-19 cases from multiple perspectives. The results of this study can provide the following auxiliary decision-making information for the COVID-19 pandemic prevention and control: (1) the information of the spatiotemporal distribution characteristics and clustering intensity of the COVID-19 outbreak worldwide has been provided from a global scale; (2) the migration paths, migration scopes, and migration directions of the COVID-19 pandemic in each continent have been supplied from a continent scale; (3) the cold and hot spot change patterns of the COVID-19 pandemic in different regions or countries worldwide have been mined from a local or regional scale. Relevant research results can provide a scientific basis for the precise prevention and control of the global pandemic and help governments of various regions formulate preventative and control measures tailored to local conditions. In view of the fact that the factors affecting the spatiotemporal dynamics of the pandemic are not considered, and the experimental results are not easy to share in real time. Therefore, in the future, correlations between the spatiotemporal evolution pattern of the global pandemic and human and natural environmental factors should be explored further based on COVID-19 pandemic time series data, and online visualization information service technology should be used to study the spatiotemporal variation characteristics of the global COVID-19 pandemic.