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2021 | OriginalPaper | Chapter

4. 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

Author : Haruka Kato

Published in: Urban Informatics and Future Cities

Publisher: Springer International Publishing

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Abstract

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.

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Title: 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
Author: Haruka Kato
Publisher: Springer International Publishing
Book: Urban Informatics and Future Cities
Print ISBN: 978-3-030-76058-8

Electronic ISBN: 978-3-030-76059-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-76059-5_4

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