E104

Economic Impact Assessment of Flood Disasters Using Population Mobility Data
〇Huan LIU・Yoshio KAJITANI・ Hirokazu TATANO
Industrial sectors are increasingly exposed to the risks posed by flood disasters, which threaten business continuity
and result in significant losses. The current practice of resilience analysis in industrial sectors mainly relies on
surveys and statistical data, which brings about challenges regarding the time and cost involved in data collection,and difficulties in quantifying the impact of floods on industrial sectors. In light of this, this research proposes amethod to assess the impact of flooding to industrial sectors by leveraging large-scale, high spatial-temporal
resolution mesh Origin-Destination mobility data that are real time and aggregated to 500 m grid scale. Specifically,
this research utilizes the mobility data to identify the home and work location of population in the flood affected
area, and further quantify the changes of number of visitors and labor supply in various types of industrial sectors
under flood disaster. The effectiveness of the proposed approach is validated through a case study of the 2018 Heavy
Rain Event in Japan. By enabling multiple data sources for impact assessment in industrial sectors, this research
provides decision-makers with timely empirical evidence to develop effective post-disaster recovery strategies.

1

measurement of changes in commuting and visitors inIntroduction

Industrial sectors are exposed to increasing risks ofindustries due to disasters and is applied into the caseflood disasters that threaten the business continuity andstudies of the 2018 Heavy Rain Event in Japan.

cause losses. Therefore, rapidly and accurately loss

2

assessment due to flood disasters are urgently needed2.1 The Heavy Rain Event of July 2018

for disaster prevention and mitigation. Currently,

From June 28 to July 8, 2018, Japan experienced andataset collected through the questionnaire surveys

exceptionally intense period of rainfall, officiallyhave been commonly used to estimate the indirecttermed the “Heavy Rain Event of July 2018” affecting

damage caused by disasters, particularly for estimating

extensive regions. The agricultural, forestry, andbusiness interruption losses and human well-being

fishery industries suffered damages amounting to ¥629

losses. While utilizing questionnaires for post-disasterbillion, while public infrastructure, including levees,

data collection is valuable, but it presents several

railways, and roads, incurred losses totaling ¥465

challenges. First, conducting surveys immediately afterbillion. Hiroshima, Okayama, and Ehime bore the bruntdisasters is challenging, as untimely surveys mayof the calamity, experiencing a substantial economic

introduce bias into the collected data. Second, surveys

setback equivalent to 23–30% of their GDP.

are time-consuming and costly. Third, there may be

2.2 The population mobility datainsufficient

inadequate

This research employs population mobility data toinformation for analysis or generalization, and disasters

evaluate the impact of the Heavy Rain event on

can heighten the risk of survey bias. Therefore, thisindustries

research proposes a methodology using mesh mobility

encompasses the trajectories of 26,473 users indata to estimate the impact of floods on industrial

Hiroshima prefecture from June 24 to July 23, 2018. As

sectors. The methodology allows for the quantitative

depicted in Figure 1, an individual user's trajectory (𝑢)responses,

leadingtoStudy area and datainHiroshima

prefecture.

Thedata

is represented as a temporally ordered sequence oftuples: 𝑇𝑢 = {(𝑙1 , 𝑡1 ), (𝑙2 , 𝑡2 ), … , (𝑙3 , 𝑡3 )}Where 𝑙1 = (𝑙𝑜𝑛𝑖 , 𝑙𝑎𝑡𝑖 ) is a location, 𝑙𝑜𝑛𝑖 and 𝑙𝑎𝑡𝑖
the latitude and longitude coordinates of the location,and 𝑡𝑖 is the corresponding timestamp (YY/MM/DD).

Fig.3.Validation of identified home and work locations

Fig.1. Illustration of recorded daily individual
trajectories in the mobility data

3Methodology

The methodology employed in this research involves a
multi-step processes. Firstly, home and work locations
are identified and validated at both the city and 500m
mesh levels. Subsequently, Origin-Destination (OD)

Fig.4. Number of daily visitors by industry sectors

flow, commuting flow, and commute time are derived.
Lastly, the analysis extends to estimating the number of

5visits for each types of industrial sectors through Point

Understanding and reducing the impacts of flooding onof Interest (POI) analysis.

the industrial sector is crucial for minimizing disaster4losses and risks. The proposed methodology aims toPreliminary results

Discussion and concluding remarks

monitor industrial recovery in real time, offering timely
evidence for industries and the government to shapeeffective post-disaster recovery strategies.
Reference:Yabe Takahiro, et al., 2022. “Toward Data-Driven,
Dynamical Complex Systems Approaches to Disaster
Resilience.” Proceedings of the National Academy ofSciences of the United States of America 119 (8): 1–7.
Acknowledgement
This work was supported by JST [MoonshotR&D][Grant Number: JPMJMS2281], Japan.
Fig.2. Spatial distribution of identified home locations(above) and work locations (below).