Understanding Community Mobility through Life Satisfaction, Human Development, and ICT Development: a Data Mining Approach

Abstract

Prior studies have investigated community mobility to understand the spread of Covid-19 cases, especially during the early months. The goal of this study was to explain community mobility through social measures. Three composite measures, namely the social life satisfaction index, human development index, and ICT development index, were selected as social-related measures to explain community mobility. The data mining approach was adopted using the Knime Analytical Platform as the software and the Cross-Industry Standard Process for Data Mining as a process framework. The analysis covered the mobility fluctuation among 34 provinces in Indonesia using the data from Google Mobility Report from July 2020 to August 2021. Cluster analysis with the k-medoids algorithm grouped provinces into higher and lower mobility provinces. The findings indicated an association between mobility fluctuation among provinces and the social life satisfaction index, human development index, and ICT development index. Four provinces, namely Bali, Yogyakarta, Jakarta, and Riau Islands, had higher mobility, human development index, and ICT development index. The study provides evidence of factors explaining human mobility and thus enriches the literature on human mobility and the social impact of the Covid-19 pandemic. The finding also enhances the literature on applying data mining to social research at a country level. However, the generalization of this finding is limited as the analysis covers Indonesian data only. This study could be extended to other countries to arrive at more generalizable results across countries.