Nur Atikah, Nadia Kholifia, Dyah Laillyzatul Afifah, Jamaliatul Badriyah Badrodin
The emergence and spread of COVID-19 prompted several studies focusing on spatial analysis in this disease's visualization, investigation, and modeling. This study aims to determine the geographical distribution and grouping of COVID-19 cases and identify hotspots. Reports from the COVID-19 task force are used to collect monthly data on reported cases. Moran's I and Anselian local Moran's I global spatial autocorrelation approaches investigated regional groupings. Getis-ord G* was used to examine changes in hotspots and coldspots, followed by mapping using ArcGIS. The hotspots with a 99% confidence level are DKI Jakarta, West Java, Central Java, East Java, Yogyakarta, and Bali. The coldpots with a 90% confidence level are North Maluku and West Papua. © 2022 American Institute of Physics Inc.. All rights reserved.
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia; School of Mathematical Sciences, University of Southampton, Southampton, United Kingdom