（別紙様式６）(Attached Form 6)

学位論文の要旨（論文の内容の要旨）
Summary of the Dissertation (Summary of Dissertation Contents)
論 文 題 目Dissertation title
Exploring Associational Factors to COVID-19 and Evaluating Non-pharmaceutical Interventions andRecovery Measures: Perspectives of Built Environment and Human Mobility
広島大学大学院国際協力研究科
Graduate School for International Development andCooperation,
Hiroshima University
博士課程後期開発科学専攻DoctoralDivision of Development Science
Program
学生番号D196414
Student ID No.
氏
名SHUANGJIN LI
Name
Seal

The whole world has been attacked by COVID-19 (i.e., Coronavirus Infectious Disease, emerged in2019) since the end of 2019, leading to more than 5.2 billion infections (infection rate: 6.6%) and morethan 6.2 million deaths (1.2% of infected persons and 0.1% of the whole population). Even though suchpandemics are not the first time in human history, the above data suggest that human society has not welllearned lessons from the history. Badly, the growth of total infection cases in 2022 (up to May 2022) wasmuch faster than that in both 2020 and 2021. This means that countries all over the world may have notwell learned lessons from each other during the COVID-19 pandemic, either. It is predicted that such
public health threats will occur in future, too. Thus, how to prepare for future pandemics is an urgent
global issue. In response to such a global concern, this research aims to present scientifically soundinsights into pandemic policymaking from perspectives of human mobility and the built environment, byexploring associational factors to COVID-19 and evaluating non-pharmaceutical interventions andrecovery measures based on massive multi-source data and advanced modeling approaches.
For achieving the above aim, this research was implemented through the following three tasks.

1.
2.
3.

Exploring virus spread mechanisms: Exploring associations of the spread of COVID-19 with thebuilt environment and human mobility
Balancing pandemic control and economic development: An integrated pandemic-tourismtransport-employment analysis
Examining influential factors to the recovery from COVID-19: The urban recovery associatedwith the built environment factors and pre-pandemic socio-economic patterns

The first task focuses on China during a short period before and after Wuhan was locked down inJanuary 23, 2020. This short period before the COVID-19 pandemic was declared by WHO allows us
to approximate the representation of the virus spread mechanisms, without being seriously affected byexternal interventions, in a relatively reliable way, even without detailed epidemiological data.
The second task attempts to provide scientifically sound answers about how to balance pandemic
control and economic development in the context of tourism in Cambodia. Tourism is targeted because
it is one of the most seriously affected sectors by COVID-19, which is closely related to economic
development, especially in countries that are highly dependent on tourism. Cambodia is such atourism-dependent country. The above case study country, China, was not targeted because it has
adopted zero-tolerance pandemic policies, making the analysis of co-existence with COVID-19
impossible. At least, all developing countries have not such an ability like China to implement zerotolerance pandemic policies, while many developed countries do not have such a social-politicalsystem like China, allowing them to follow the zero-tolerance policies.
The third task attempts to provide scientifically sound answers how to make cities resilient topandemics, especially in the field of urban planning. Here, Nagoya, Japan, other than cities in China
or Cambodia, is targeted, mainly because human mobility data at smaller spatial scales could only beobtained Nagoya, but similar data could not be obtained from China and Cambodia.
Massive multi-source data
Massive data used for the above analyses include: population mobility data, such as trains informationfrom the epicenter of pandemic to other cities in China, inter-city number of flights and population
flow in China, inter-township connections across townships in China, mobile phone signaling data inNagoya, time-series international flight in Cambodia, changes in human public transportation utilizing
in Cambodia; raster data such as nighttime light satellite across China and Nagoya, Japan, aerial
images in Nagoya; land use and built environment data such as distribution and density of publicfacility (public open space, comprehensive hospital and clinic, point of interests, travel time to thenearest hospital, etc.) in China and in Nagoya, transportation facilities (road network, roadintersections, stations, etc.) in China and in Nagoya, land use in Nagoya at block level and built-up
area in China at township level; spatial administrative unit data, such as township-level administrative
boundaries, community-level administrative boundaries; social-economic data, such as total tourist
consumption, contribution of tourism to employment, hospital beds in Cambodia and the aging
population in China across cities.
Context-sensitive modeling approaches
The following modeling approaches are combined for implementing different tasks, including local
indicators of spatial association (LISA), bivariate local indicators of spatial association (BiLISA),
mixed geographically weighted regression (MGWR) and network analysis in the field of spatial
analysis, random forest (RF), partial dependence plots (PDP), convolutional neural network (CNN)
model, and guided Grad-CAM (GG-CAM) model in the field of machine learning, system dynamic(SD) in complexity science field, and K-means, Getis-Ord Gi in statistical analysis field.
Integrated findings
Firstly, both the spread of COVID-19 virus and the urban recovery from it are highly associated withthe built environment, which is the main part of urban planning, land management and design of urbanfunctions. This suggests that preparing for future pandemics should be given sufficient attention inurban policy, where spatial disparities should be well addressed. Secondly, the built environment isclosely related to human mobility, which is at the core of balancing pandemic control and maintaining
economic activities, where strict quarantine measures and key stakeholders’ (tourists and firms)
cooperation are most important, where spatial heterogeneities should be better reflected. Thirdly,
human mobility is a powerful driver for the socio-economic development. Both pandemic control andrecovery policymaking should be carefully made at the facility level that is directly connected withindividuals’ activity participation and trip making, where individual heterogeneities should beproperly incorporated. Finally, methodologically, this study has figured out that mixed data-driven
methodologies (related to deep learning and spatial analysis, etc.) are useful to simultaneously analyze
multi-source and multi-scale massive data, involving various complexities, for deriving the above
integrated findings and policy implications related to pandemic-sensitive urban planning and design
as well as mobility policies.
Integrated policy implications
Considering that the COVID-19 virus is invisible, seamless and comprehensive pandemic
policymaking must be implemented by involving various stakeholders, covering the pre-pandemic,
during-pandemic and after-pandemic recovery periods. All findings also presents useful insights into
the development of post-pandemic society.
[Importance of preparedness] Lack of reliable data has been a serious barrier for taking effective
pandemic policy measures. In this regard, policies supporting data sharing for emergent crises must
be made. Because careful self-protection can keep people away from the infection, it is necessary to
encourage more and more people to form a virus-sensitive lifestyle, as a key part of public education.
[Exploring a proper level of the growth of human mobility] It is true that human mobility is one of themost important drivers of the virus spread and economic development; however, it is necessary tofigure out a proper level of the growth of human mobility for sustainable development for mitigating
the various serious impacts of “Great Acceleration” (https://www.anthropocene.info/greatacceleration.php) on both human society and nature.
[Making economic activities to be human friendly] The current pandemic re-emphasizes theimportance of human-scale urban development. Long-distance commuting should be avoided.
Traditional business practices of having to start working at the same time (e.g., 08:00 am or 09:00 am)
should be completely abolished for releasing people from painful peak-hour commuting.
Neighborhoods supporting people’s daily lives should be redeveloped at a human scale, at least bymeeting people’s fundamental needs in life.
[Human-scale urban development in association with planetary health] Distancing and working-fromhome pandemic policies allows policymakers to re-recognize the roles of green and open spaces in
improving people’s quality of life, especially nearby people’s residential areas. Quality urban
development should be human-oriented rather than economy-oriented, where concerns about bothhuman and nature health as well as social exclusion should be well addressed: i.e., planetary healthshould be better positioned in urban development.
[Digitalizing the anchoring roles of points of interest (POIs) in pandemic control and urban
recovery/resilience] Key urban facilities are not only associated with the virus spread but also related
to long-lasting urban recovery and resilience. For controlling the pandemic, key POIs should beeffectively managed. When the pandemic-influenced situations become a new normal, services within
the key POIs could be better re-designed for attracting more people while keeping virus infections ata very low level. Digital technologies should be better used to manage the anchoring roles of POIs in
both pandemic control and urban recovery/resilience.

備考

論文の要旨はＡ４判用紙を使用し，4,000字以内とする。ただし，英文の場合は1,500語以内とする。
Remark: The summary of the dissertation should be written on A4-size pages and should not exceed
4,000 Japanese characters. When written in English, it should not exceed 1,500 words.