Clustering Regions Based on Socio-Economic Factors Which Affected the Number of COVID-19 Cases in Java Island

Around 60% of COVID-19 positive cases in Indonesia have occurred in Java Island. This study provides clustering adjacent regions (cities and regencies) in Java Island into some groups based on some socio-economic factors that are suspected to affect the COVID-19 infection rates (positive cases per 1...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-03, Vol.1863 (1), p.12014
Main Authors: Rahardiantoro, Septian, Sakamoto, Wataru
Format: Article
Language:English
Subjects:
Cluster analysis
Clustering
Coronaviruses
COVID-19
Data analysis
Decision making
Economic factors
Infections
Physics
Social factors
Socioeconomic factors
Tessellation
Vector quantization
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Summary:Around 60% of COVID-19 positive cases in Indonesia have occurred in Java Island. This study provides clustering adjacent regions (cities and regencies) in Java Island into some groups based on some socio-economic factors that are suspected to affect the COVID-19 infection rates (positive cases per 100,000 residents), which could be useful for decision making by government. The factors involved in this study are poverty percentage, Human Development Index (HDI), average of expenditure per month, and open unemployment rate. There are two steps in our data analysis: first, we determined the factors that affected the infection rate significantly by using lasso, and then we estimated region-specific effects of each significant factor by using generalized lasso. In the generalized lasso, two types of spatial structure were considered, namely, regions divided by province, and neighbourhood regions based on k-means clustering and Voronoi tessellation. The tuning parameter in both lasso and generalized lasso was selected by 5-folds cross-validation. Based on the first step, three variables were found to affect the infection rate significantly. Then in the second step, the three variables had spatially varying coefficients in the generalized lasso using regions divided by provinces. On the other hand, HDI provided spatially varying coefficient in the generalized lasso using region based on k-means clustering and Voronoi tessellation.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1863/1/012014