@article{ref1,
title="Analysis of foot traffic due to COVID-19 using mobile network big data",
journal="International journal of membrane science and technology",
year="2023",
author="Lee, A.-Jeong and Park, Tae-Rang and Jwa, Jeong-Woo",
volume="10",
number="1",
pages="254-260",
abstract="The COVID-19 pandemic has hit the tourism industry with a drop in the number of domestic and foreign tourists and loss of revenue. We can prepare for future pandemics by analyzing the impact of COVID-19 on tourist numbers before and after the outbreak. In this paper, we analyze the change in the foot traffic of tourists and resident population before and after the outbreak of COVID-19 using mobile communication company call records (CDR) and WiFi access log data. Analysis of changes in the foot traffic due to COVID-19 is analyzed based on the number of confirmed cases, recoveries, and deaths by administrative dong, daily, and gender. The hierarchical cluster analysis of distribution population for administrative dongs (classified as k = 5 clusters) was performed to classify areas with high changes in foot traffic due to COVID-19. We also predict the change in the foot traffic due to the epidemic using the Xgboost (Extreme Gradient Boosting) algorithm.<p /> <p>Language: en</p>",
language="en",
issn="2410-1869",
doi="10.15379/ijmst.v10i1.1453",
url="http://dx.doi.org/10.15379/ijmst.v10i1.1453"
}