Spatiotemporal Evolution Patterns of the Covid-19 Pandemic Using Space-Time Aggregation and Spatial Statistics
A Global Perspective
Bibliographic Data
| ID | 22031357 |
|---|---|
| Authors | Zechun Huang (Southwest Jiaotong University, corresponding author) |
| Year | 2021 |
| Volume | 10 |
| Issue | 8 |
| Pages | 519 |
| Publication date | 2021-07-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10080519 |
| OpenAlex | W3193232363 |
| Language | EN |
| Citations received | 7 |
| References cited | 30 |
Unlike previous regionalized studies on a worldwide crisis, this study aims to analyze spatial distribution patterns and evolution characteristics of the COVID-19 pandemic, using space-time aggregation and spatial statistics from a global perspective. Hence, various spatial statistical methods, such as the heat map, global Moran’s I, geographic mean center, and emerging hot spot analysis were utilized comprehensively to mine and analyze spatiotemporal evolution patterns. The main findings were as follows: Overall, the spatial autocorrelation of confirmed cases gradually increased from the initial outbreak until September 2020 and then decreased slightly. The geographic centroid migration ranges of the pandemic in Asia, Europe, and Africa are wider than those in South America, Oceania, and North America. The spatiotemporal evolution pattern of the global pandemic mainly consisted of oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. This study provides auxiliary decision-making information for pandemic prevention and control
Biology · Cartography · Centroid · Cold spot · Common spatial pattern · Economic geography · Geography · Outbreak · Pandemic · Physical geography · Regional science · Remote sensing · Spatial analysis · Spatial distribution · Spatial ecology · Statistics · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence · Ecology
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Space—Time Surveillance of Covid-19 Seasonal Clusters
Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of Covid-19 and Its Influencing Factors in Mainland China
Spatial Patterns of the Spread of Covid-19 in Singapore and the Influencing Factors
Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System
An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion
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Spatial analysis and GIS in the study of Covid-19. A review
Spatial epidemic dynamics of the Covid-19 outbreak in China
A modified Mann-Kendall trend test for autocorrelated data
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Comparing implementations of global and local indicators of spatial association
Spatial analysis of Covid-19 clusters and contextual factors in New York City
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The Analysis of Spatial Association by Use of Distance Statistics
Global and Temporal Covid-19 Risk Evaluation
Exploring Urban Spatial Features of Covid-19 Transmission in Wuhan Based on Social Media Data
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A Spatio‐Temporal Analysis of the Environmental Correlates of Covid‐19 Incidence in Spain
Spatial Statistics and Influencing Factors of the Covid-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China
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Local Indicators of Spatial Association—Lisa
| Unique citing works | 7 |
|---|---|
| Citations per year | 1,75 |
| Citation span | 2022 - 2024 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |