Doctoral Dissertation

Exploring Associational Factors to COVID-19 and
Evaluating Non-pharmaceutical Interventions and Recovery
Measures: Perspectives of Built Environment and HumanMobility(Summary)

SHUANGJIN LI

Graduate School for International Development and Cooperation
Hiroshima University

September 2022

The whole world has been attacked by COVID-19 (i.e., Coronavirus Infectious Disease,emerged in 2019) since the end of 2019, leading to more than 5.2 billion infections(infection rate: 6.6%) and more than 6.2 million deaths (1.2% of infected persons and
0.1% of the whole population). Even though such pandemics are not the first time inhuman history, the above data suggest that human society has not well learned lessons
from the history. Badly, the growth of total infection cases in 2022 (up to May 2022)
was much faster than that in both 2020 and 2021. This means that countries all over theworld may have not well 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 sucha global concern, this research aims to present scientifically sound insights into
pandemic policymaking from perspectives of human mobility and the built environment,by exploring associational factors to COVID-19 and evaluating non-pharmaceuticalinterventions and recovery 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.

Exploring virus spread mechanisms: Exploring associations of the spread of
COVID-19 with the built environment and human mobility
2. Balancing pandemic control and economic development: An integrated
pandemic-tourism-transport-employment analysis
3. Examining influential factors to the recovery from COVID-19: The urban
recovery associated with the built environment factors and pre-pandemic
socio-economic patterns
The first task focuses on China during a short period before and after Wuhan waslocked down in January 23, 2020. This short period before the COVID-19 pandemic
was declared by WHO allows us to approximate the representation of the virus spreadmechanisms, without being seriously affected by external interventions, in a relatively
reliable way, even without detailed epidemiological data.

The second task attempts to provide scientifically sound answers about how tobalance pandemic control and economic development in the context of tourism inCambodia. Tourism is targeted because it is one of the most seriously affected sectors
by COVID-19, which is closely related to economic development, especially incountries that are highly dependent on tourism. Cambodia is such a tourism-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 zero-tolerance pandemic policies, while many developed countries do nothave such a social-political system like China, allowing them to follow the zerotolerance policies.
The third task attempts to provide scientifically sound answers how to make cities
resilient to pandemics, 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 be obtained 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 information from the epicenter of pandemic to other cities in China, inter-city
number of flights and population flow in China, inter-township connections acrosstownships in China, mobile phone signaling data in Nagoya, 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 densityof public facility (public open space, comprehensive hospital and clinic, point ofinterests, travel time to the nearest hospital, etc.) in China and in Nagoya, transportation
facilities (road network, road intersections, stations, etc.) in China and in Nagoya, land
use in Nagoya at block level and built-up area in China at township level; spatialadministrative unit data, such as township-level administrative boundaries, communitylevel administrative boundaries; social-economic data, such as total tourist
consumption, contribution of tourism to employment, hospital beds in Cambodia andthe 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 ofspatial association (BiLISA), mixed geographically weighted regression (MGWR) andnetwork analysis in the field of spatial analysis, random forest (RF), partial dependenceplots (PDP), convolutional neural network (CNN) model, and guided Grad-CAM (GGCAM) 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 with the built environment, which is the main part of urban planning, land
management and design of urban functions. This suggests that preparing for future
pandemics should be given sufficient attention in urban policy, where spatial disparities
should be well addressed. Secondly, the built environment is closely related to humanmobility, which is at the core of balancing pandemic control and maintaining economicactivities, 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 and recovery policymaking should be carefully made at thefacility level that is directly connected with individuals’ activity participation and trip
making, where individual heterogeneities should be properly 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. Allfindings 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 awayfrom the infection, it is necessary to encourage more and more people to form a virussensitive lifestyle, as a key part of public education.
[Exploring a proper level of the growth of human mobility] It is true that humanmobility is one of the most important drivers of the virus spread and economic
development; however, it is necessary to figure out a proper level of the growth ofhuman mobility for sustainable development for mitigating the various serious impactsof “Great Acceleration” (https://www.anthropocene.info/great-acceleration.php) on
both human society and nature.

[Making economic activities to be human friendly] The current pandemic reemphasizes the importance of human-scale urban development. Long-distancecommuting 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 forreleasing people from painful peak-hour commuting. Neighborhoods supporting
people’s daily lives should be redeveloped at a human scale, at least by meetingpeople’s fundamental needs in life.
[Human-scale urban development in association with planetary health] Distancing andworking-from-home pandemic policies allows policymakers to re-recognize the rolesof 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 both human and nature health as well associal exclusion should be well addressed: i.e., planetary health should be better
positioned in urban development.
[Digitalizing the anchoring roles of points of interest (POIs) in pandemic control andurban 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 thepandemic, key POIs should be effectively managed. When the pandemic-influenced
situations become a new normal, services within the key POIs could be better redesigned for attracting more people while keeping virus infections at a very low level.
Digital technologies should be better used to manage the anchoring roles of POIs in
both pandemic control and urban recovery/resilience.