발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.1] A Study on the Quantification System of Social Distance Using Unstructured SNS Data |
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| 논문투고자 |
Jin-An Cha, Jin-Soo Kim |
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| 논문내용 |
In recent years, numerous respiratory diseases such as SARS and MERS have become popular around the world, and now the respiratory virus epidemic is serious due to the outbreak of corona-19. As a result, each country is focusing its capabilities on preventing the spread of viruses. South Korea is also making efforts to reduce the risk of spreading the virus around by sending in medical personnel on standby to conduct real-time tests when suspected cases occur, and by informing the location and number of confirmed cases in real time. According to WHO and other health organizations, the most important and basic prevention method to prevent the spread of respiratory viruses is social distance compliance. However, there is a limit to the practice of social distance keeping, just by recommending people to follow the guidelines for social distance keeping because the criteria for compliance with social distance are different for each individual and difficult to objectively implement. To solve this
problem, this paper proposes a system that can be quantitatively monitored by scoring individuals' compliance with social distance based on user movement, number of visits by processing large unstructured data such as GPS coordinates, TAG data, and TXT data on SNS with Hadoop and Spark-based distributed processing systems. |
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