SPATIAL CLUSTERING PHENOMENA OF COVID-19 CASES IN SELANGOR: A HOTSPOT ANALYSIS AND ORDINARY LEAST SQUARES METHOD

• Published in: GEOINFORMATION WEEK 2022, VOL. 48-4
• Main Authors: Mohammad, Nur Syuhadah; Rasam, Abdul Rauf Abdul; Ghazali, Rosmadi; Idris, Roslina; Abu Bakar, Rabieahtul
• Format: Proceedings Paper
• Language: English
• Published: COPERNICUS GESELLSCHAFT MBH 2023
• Subjects: Computer Science; Remote Sensing
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001185691400032
• ISSN: 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-4-W6-2022-237-2023

An increase in number of Coronavirus disease 2019 (COVID-19) cases will lead to more cluster discovery in Malaysia. Furthermore, with the increasing population, city growth, workplace income needs, high-risk groups, and other relevant factors can contribute to the formation of the new clusters. The cluster distribution of the disease could be seen by mapping and spatial analysis to understand their spatial phenomena of the disease dynamics. The purpose of the study is to analyse the spatial distribution of COVID-19 cluster cases in Selangor for year 2020. Two objectives of the study are i) to determine the hotspot location of the COVID-19 cluster, and ii) to examine the spatial distribution of the factors affecting the COVID-19 cluster. The data processing was conducted using hotspot analysis and ordinary least squares (OLS) in ArcGIS Pro and Microsoft Excel to explore the local disease phenomena. The COVID-19 cases was most prevalent in the Petaling district, followed by Hulu Langat and Klang. The virus had the least impact in Sabak Bernam, Hulu Selangor, Kuala Selangor, Sepang, Kuala Langat, and Gombak. Three environmental factors of population density, the effects of urbanisation, and workplace cases were influential variables at the local clusters. These findings could help the local agencies to facilitate and control the spread mode of the virus in a spatial human environment.