東京大学

関本研究室 / Sekimoto Lab. IIS, the University of Tokyo.

Evaluating the Economic Consequences of COVID-19: A
Comprehensive Study Utilizing probe vehicle data and POI data
Guocheng Tang, Yoshiki Ogawa, Chenbo Zhao, Yoshihide Sekimoto

Background
The COVID-19 pandemic, has had profound and far-reaching impacts on global economies, societies, and daily life. Tokyo provides a unique lensthrough which to study these changes. Probe vehicle data, which captures the movement and stoppage patterns of vehicles, and point of interest(POI) data, which provides information about various businesses and establishments, are two critical datasets that can offer insights into industry
changes before and after the pandemic.

ObjectiveDataset

1.
ETC(ElectronicTollCollectionSystem)
data(a)contains
severalThis study aims to analyze the economic impacts of the COVID-19
attributes
suchaspandemic in Tokyo using probe vehicle data and POI data.
2.
thecar’sID,driving
speed,etc.
Weuse
datafrom23
wardsinTokyo
Focuses on changes in rate of stay points of vehicles in different areas
every July for 3 years which are around 100 million. Meanwhile inover the past three years and estimate industries with significant changes
July2021,
Tokyoisin
astateofemergency
whichisthefocusofthein traffic flow due to the pandemic.
study.PoI data (Telepoint Pack (Zenrin CO., LTD.))(c) contains
information about geographic entities and their coordinates forethodology
different sectors inTable
Tokyo.
1 The number of records and IDs of ETC2.0

M

In the mesh grid(b), A is the product sum of the stay points and the numberof POI points of different industries, as shown in Equation (1). The numberof different POI points in different grids is P_i. The number of different
stop points is P_e, R represents the change rate of that industry in that meshover the two years of the analysis as shown in Equation (2).

ResultRecord numbers

2019

2021

2022

804,669,920

2,071,864,793 2,424,203,084

Unique ID numbers 1,882,723

3,042,734

3,666,554

Trip numbers

38,159,984

43,621,784(a)Probe data

17,533,510(b)Mesh-grid (500m)(c)POI data

Fig. 1 Study area and data used in this study

Fig. 2 Industry prosperity change rate map in each wards in (a) 2021 and (b) 2022 (vs. 2019).
Table shows the top five wards in the given industry‘s change rate.

Fig. 3 Radar map of change rate of each industry in Tokyo 23 wards(a) 2021
医療製造業
卸売業・小売業

建設業(b) 2022

Time variation was reduced in 16 sectors, except in the medical and welfare and transport sector. In
particular, the accommodation and manufacturing sectors were reduced in all 23 wards, with 2022 being
the largest number of infections on record, and the impact was greater than when the state of emergency
was declared in 2021.
Fig. 4 Results of principal component analysis
Table 2. Compared with 2019, the top five wards in the given industry‘s change rate in 2022.

The impact of COVID-19 varies by industry and region. The medical and welfare sector isinversely correlated with other sectors in 2021 and 2022. Demand of health and social care aregrowing, in contrast, demand from the manufacturing and wholesale trade sectors shows anincreasing decline in 2021-2022. This is because the number of infected persons increased themost in July 2022. For the construction industry, the trend differed between districts and years.

Conclusion
In this study, we developed the method to estimating the economic impact
of COVID-19 using probe data. It became clear that 15 sectors experienced
negative impacts, while medical and welfare and transport, experienced
positive impacts. The negative impacts on the hotel and retail sectors were
3 times and 2 times greater compared to other sectors and expanded in
2022. After PCA analysis of 23 wards, we quantitatively clarified thedifferences in impact around health/ welfare, and manufacturing.

The positional relationship by region can be divided into the construction industry (x-axis), which has a large
contribution from PC 1, and the medical and welfare industry (y-axis), which has a large contribution from PC
2. This allows a visualisation of similarities in the economic impact of different regions
For example, Setagaya and Shibuya wards, which are close to each other, show similar trends, while Chiyoda
and Suginami wards, which are further away, show opposite trends.

Future work
1.clarify the changes in intercity freight transportation during the pandemic.
2.analysis and the differences in the impact of the coronavirus on freighttransportation from the perspective of trip changes in different industries.

Sekimoto Lab. @ IIS Human Centered Urban Informatics, the University of Tokyo
Mail: tang-guocheng430@g.ecc.u-tokyo.ac.jp