GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries
The outbreak of COVID-19 has caused many losses, unprecedented threats, and a change of life in many ways. The daily records of cases and other related data contain important information to reflect the severity, trend, and risk level of each country over time. Thus, this study aims to examine the tr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/24102/ http://journalarticle.ukm.my/24102/1/23_33%20Paper_3.pdf |
| _version_ | 1848816012836732928 |
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| author | Jiun, Guan Wong Min, Jun Lim Siok, Kun Sek Khang, Yi Sim |
| author_facet | Jiun, Guan Wong Min, Jun Lim Siok, Kun Sek Khang, Yi Sim |
| author_sort | Jiun, Guan Wong |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | The outbreak of COVID-19 has caused many losses, unprecedented threats, and a change of life in many ways. The daily records of cases and other related data contain important information to reflect the severity, trend, and risk level of each country over time. Thus, this study aims to examine the trend, severity, and change of the pandemic situation in Asia over four periods. In this study, the data are collected from 48 Asian countries. The four periods are selected to represent different stages of the outbreak based on the daily records for comparison. The four periods are 6th January 2020, 6th January 2021, 6th July 2021, and 6th June 2022. The data include the daily record of confirmed cases, the number of deaths, the number of vaccinations, and the number of recoveries. Besides, this study also examines the accumulated cases up to 6th July 2022. The accumulated data includes the four data points mentioned and the severity index. The Local Indicators of Spatial Association (LISA) is applied to detect the clustering pattern and hotspot area as well as the existence of spatial effects in the data. The GIS mapping reveals that China has the most severe situation in Period 1. Nevertheless, from Periods 2 to 3, the pandemic is spreading speedily and widely over the Asian region. The deadly situation (confirmed cases, high fatality, and vaccination) is centred around Southeast Asia and West Asia. Nonetheless, with the exception of China, the situation is improving in Period 4. |
| first_indexed | 2025-11-15T00:59:07Z |
| format | Article |
| id | oai:generic.eprints.org:24102 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:59:07Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:241022024-09-06T00:47:38Z http://journalarticle.ukm.my/24102/ GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries Jiun, Guan Wong Min, Jun Lim Siok, Kun Sek Khang, Yi Sim The outbreak of COVID-19 has caused many losses, unprecedented threats, and a change of life in many ways. The daily records of cases and other related data contain important information to reflect the severity, trend, and risk level of each country over time. Thus, this study aims to examine the trend, severity, and change of the pandemic situation in Asia over four periods. In this study, the data are collected from 48 Asian countries. The four periods are selected to represent different stages of the outbreak based on the daily records for comparison. The four periods are 6th January 2020, 6th January 2021, 6th July 2021, and 6th June 2022. The data include the daily record of confirmed cases, the number of deaths, the number of vaccinations, and the number of recoveries. Besides, this study also examines the accumulated cases up to 6th July 2022. The accumulated data includes the four data points mentioned and the severity index. The Local Indicators of Spatial Association (LISA) is applied to detect the clustering pattern and hotspot area as well as the existence of spatial effects in the data. The GIS mapping reveals that China has the most severe situation in Period 1. Nevertheless, from Periods 2 to 3, the pandemic is spreading speedily and widely over the Asian region. The deadly situation (confirmed cases, high fatality, and vaccination) is centred around Southeast Asia and West Asia. Nonetheless, with the exception of China, the situation is improving in Period 4. Penerbit Universiti Kebangsaan Malaysia 2024-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24102/1/23_33%20Paper_3.pdf Jiun, Guan Wong and Min, Jun Lim and Siok, Kun Sek and Khang, Yi Sim (2024) GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries. Journal of Quality Measurement and Analysis, 20 (2). pp. 23-33. ISSN 2600-8602 http://www.ukm.my/jqma |
| spellingShingle | Jiun, Guan Wong Min, Jun Lim Siok, Kun Sek Khang, Yi Sim GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title | GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title_full | GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title_fullStr | GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title_full_unstemmed | GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title_short | GIS-based and Geospatial analysis: mapping and visualizing the trend of Covid-19 data in selected Asian countries |
| title_sort | gis-based and geospatial analysis: mapping and visualizing the trend of covid-19 data in selected asian countries |
| url | http://journalarticle.ukm.my/24102/ http://journalarticle.ukm.my/24102/ http://journalarticle.ukm.my/24102/1/23_33%20Paper_3.pdf |