Development of a Spatio-Temporal Analysis Method to Support the Prevention of COVID-19 Infection: Space-Time Kernel Density Estimation Using GPS Location History Data
Development of a Spatio-Temporal Analysis Method to Support the Prevention of COVID-19 Infection: Space-Time Kernel Density Estimation Using GPS Location History Data
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DOI：
10.1007/978-3-030-76059-5_4
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发表时间：
2021-01-01
期刊：
影响因子：
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通讯作者：
Kato, Haruka
中科院分区：
文献类型：
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作者：
Kato, Haruka
本研究旨在开发一种时空分析方法，以支持预防COVID-19感染的规划。该方法利用GPS定位历史数据进行时空核密度估计。该数据是在用户同意的情况下定期从智能手机获取的GPS位置数据。研究方法是对茨木市2019年4月和2020年4月的面板数据进行分析。2020年4月，日本政府实施软封锁。因此，本研究开发了一种时空分析方法，可视化的时空与高人口密度。利用这些方法，地方政府可以通过指定特定的时空区域来限制人们的生活。此外，该方法有助于公民改变他们的生活方式行为，并合作预防COVID-19感染。该方法是日本基于紧急声明的软封锁的替代方案。未来，这种方法将用于未来智慧城市的数据分析。
This study aims to develop a spatio-temporal analysis method to support planning for the prevention of a COVID-19 infection. The method focused on the space-time kernel density estimation using the GPS location history data. The data is GPS location data obtained at regular intervals from smartphones with the consent of the users. The research method was a panel data analysis for April 2019 and April 2020 with Ibaraki City. In April 2020, the Japanese government implemented a soft lockdown. As a result, this study developed a spatio-temporal analysis method that visualizes the space-time with high population density. Using these methods, local governments can restrict people's lives by designating specific space-time areas. In addition, the method helps citizens to change their lifestyle behaviors and cooperate in the prevention of COVID-19 infection. The method is an alternative to the Japanese soft lockdown, which was based on an emergency declaration. In the future, this method will be utilized for data analysis in future smart cities.