Predictions for architectural and urban spaces in response to the COVID-19 pandemic: Delphi survey of Korean experts

Figures & data

Table 1. Review of the related works.

Category	Research	Description
Spatial Causes of COVID-19	Matthew et al., 2006	They discussed about various urban factors as the spatial causes of infectious diseases, such as population movement and urbanization, economic growth, land-use patterns, globalization, environmental change, and climate change.
	Menéndez & García, 2021	They discussed about diverse causes of the spread of SARS-CoV-2, the virus that causes COVID-19, including insufficient green spaces and connectivity.
	Yu, 2021	The study showed an increase in the correlation between population density and COVID-19 confirmed cases in April 2021 compared to December 2020 in Korean cities.
	Hamidi et al., 2020	They found that direct relationship between density and infectious disease is not clear through empirical analyses.
Social Changes	Dbouck & Drikakis, 2020	They analyzed the extent of the spread of the virus from the body of the infected on assumption of airborne transmission of COVID-19.
	Kwon et al., 2020	They showed that the virus of the first infected person could also infect people who were 6.5 meters away by the air conditioning wind in an enclosed space.
	Hwang et al., 2021	They suggested the possibility of infection through water and sewer lines in an apartment building.
	Lee & Choi, 2020	Their analysis of the impact of local commercial area due to the COVID-19 outbreak showed that sales near residences had a small impact, whereas sales declined significantly in the commercial area with a large floating population and/or a consumer base centered on college students.
	Edward et al., 2020	Their study on the correlation between changes in urban transportation use and the spread of COVID-19 in five U.S. cities showed that for 10% reduction in transportation utilization, the number of confirmed COVID-19 cases decreases by 19%.
	Kim et al., 2021	While public transport has seen a decline in utilization, personal means of transport, such as private cars and bikes, have shown that the change in utilization is negligible or rather increasing.
Future Prospects	Florida et al., 2020	The impact of COVID-19 on the built environment was analyzed in both macro and micro facets, and the future changes in cities in the post COVID-19 era were forecasted.
	Tricarico & Vidovich, 2021	They predicted the changes in cities caused by COVID-19 in both qualitative and quantitative terms, and proposed countermeasures to prepare for the future and overcome risks.
	Barbarossa, 2020	In response to changes in the transportation system in the post COVID-19 era, they expected that the revitalization of pollution-free mobility continues.
	Naglaa et al., 2020	They presented elements for a healthy and sustainable built environment in terms of urbanism, public spaces, housing, office, and construction strategies, and forecasted architecture and urbanism after the COVID-19.
	Kim, 2021	As the architectural countermeasures in response to COVID-19, Prevention of Infectious Disease Through Environmental Design and its six principles were proposed and validated.

Figure 1. Procedure of Delphi survey.

Table 2. The 35 keywords for trends and issues related to COVID-19.

Category	Keywords	Description
Drivers of Change	Physical distancing	Physical distance of 1.5–2 m between individuals to prevent infections from close contact can change architectural and urban spaces physically and behaviorally (floor tiles with distance markings, maintaining spaces in queues, etc.)
	Control of face-to-face contact	Physical and behavioral changes in architectural and urban spaces to reduce human density in spaces and minimize face-to-face exchanges as another method to prevent infections from close contact (one-way movement, installing partitions, etc.)
	Access control	It ranges from lockdown measures (e.g., closing off specific facilities, cities, and countries) to selective entry that involves specific confirmation (inspection) procedures upon entry
	Anti-virus air conditioning	A method to prevent airborne infections; it ranges from actively utilizing outdoor spaces to exposure to outdoor air, periodic natural ventilation, and changes of living spaces for anti-virus air conditioning in terms of architectural planning and building system.
	Sharing minimization	Reinforcing disinfection and sterilization and minimizing use of shared objects, devices, installations, and spaces to prevent infections through contact with surfaces (of objects, etc.) contaminated with viruses
Social Changes	Moving distance/frequency	Degree (distance or frequency) of spatial/physical movement of members within a society
	Individualized transportation means and services	Movement by bicycle, scooter, single-person vehicle, etc., and services specialized for them (drive-through services, etc.)
	Online remote services	Expansion of online remote services due to avoidance of face-to-face contact and maturity of ICT (online ordering, remote education, telemedicine, etc.)
	Unmanned services	Replacement of existing face-to-face services with intelligent terminals to reduce face-to-face contact. It carries side effect of reduced labor costs (kiosks, unmanned ticket offices, unmanned delivery boxes, unmanned stores, etc.)
	Telecommuting	Expansion of telecommuting and flexible work arrangements to minimize unnecessary face-to-face contact and improve face-to-face work efficiency, and consequent changes in architectural and urban spaces (changes in residential and office spaces, etc.)
	Work/task automation	Increase in automation devices, facilities and services due to reduction of face-to-face tasks, advancement in AI technology, etc.
	Population relocation	Departure of urban population due to spread of distancing and expansion of telecommuting
	Tourism industry	Changes in the tourism industry due to restrictions on face-to-face contact and movement (city vs. nature, overseas vs. domestic)
	Logistics industry	Changes in the traditional commercial/distribution industry and growth of the logistics industry due to avoidance of face-to-face contact
	Sharing economy	Reduction of sharing economy due to sharing control/minimization, or advancement of sharing economy in individualized transportation means
	Sustainability	Social response to continuous environmental destruction
Buildings	Diversification of housing types	Emergence of a new housing and residential behavior, or segmentation of housing types
	Residential area per capita	Average area occupied by one member in a living space increases as time spent in the house increases
	Complexification of housing functions	Along with increased telecommuting, roles and functions of housing become complex as activities outside the house (leisure activities, e.g., exercise) flow into the house.
	Intelligent buildings	IoT, sensing, and AI technologies deeply pervade architectural spaces (connected homes, smart buildings, etc.)
	Mixed-use buildings	Buildings containing spaces with two or more uses, or buildings that are used for different functions with time different according to user’s need
	Flexible buildings	Demand for architectural spaces that can flexibly respond and adapt to rapid social changes
Street blocks and complexes	Hyper-local living areas	Rise of the concept of hyper-local living areas owing to growing importance and use of spaces close to one’s residence due to voluntary self-quarantine, etc.
	Industrial complex recycling	Change of use or renovation of existing industrial complex spaces due to decline in use or changes in industrial structure
	Public movement spaces	Changes in the use of public spaces, such as pedestrian paths, roads, and bicycle paths
	Public rest spaces	Changes in the use of local parks, various medium/large-scale urban parks, etc.
	Green spaces	Changes in the use of natural parks, other forest spaces, etc.
	Commercial spaces	Changes in traditional markets, shopping streets, shopping mall, etc.
	Temporary change of use	Changes in the level of need to change use of spaces for a specific period of time
Cities	Land-use density	Increasing/decreasing of cases with high land-use density, such as high-rise building blocks
	Transportation facilities and services	Transportation infrastructure, pedestrian and bicycle path networks, etc., and related services
	Expansion of urban periphery	Explicit changes in cities, such as urban sprawl and suburbanization
	Urban – rural disparities	Disparities in population, economy, health, space use, etc. between urban and rural areas
	Smart city	Cities as spatial implementation of hyper-connection and hyper-intelligence
	Mega city	Metropolitan area that connects multiple cities to enable everyday living

Table 3. Agreement with and change patterns of the 35 keywords.

Category	Keyword	Agreement (ratio)						Change pattern (1: decrease/weaken ↔ 5: increase/strengthen)
		During COVID-19			Post-COVID-19			During COVID-19				Post-COVID-19
		Agree	Dis-agree	C.V.	Agree	Dis-agree	C.V.	Mean	Conver-gence	Consen-sus	Stabil-ity	Mean	Conver-gence	Consen-sus	Stabil-ity
Drivers of change	Physical distancing	1.00	0.00	1.00	0.44	0.56	−0.13	4.82	0.00	1.00	0.08	3.19	0.50	0.67	0.34
	Control of face-to-face contact	1.00	0.00	1.00	0.36	0.64	−0.28	4.79	0.00	1.00	0.08	3.22	0.50	0.67	0.28
	Access control	0.92	0.08	0.85	0.23	0.77	−0.54	4.56	0.50	0.80	0.13	3.00	0.00	1.00	0.26
	Anti-virus air conditioning	0.90	0.10	0.79	0.85	0.15	0.69	4.38	0.50	0.75	0.14	4.35	0.50	0.75	0.15
	Sharing minimization	0.92	0.08	0.85	0.51	0.49	0.03	4.17	0.50	0.75	0.17	3.65	0.50	0.75	0.17
Social changes	Moving distance /frequency	0.92	0.08	0.85	0.26	0.74	−0.49	2.51	1.50	−0.5	0.58	3.50	0.50	0.67	0.28
	Individualized transportation means and services	0.92	0.08	0.85	0.69	0.31	0.38	4.28	0.50	0.75	0.14	4.03	0.50	0.75	0.23
	Online remote services	1.00	0.00	1.00	0.95	0.05	0.90	4.85	0.00	1.00	0.07	4.47	0.50	0.80	0.18
	Unmanned services	0.97	0.03	0.95	0.95	0.05	0.90	4.53	0.50	0.80	0.15	4.26	0.50	0.75	0.18
	Telecommuting	1.00	0.00	1.00	0.95	0.05	0.90	4.69	0.50	0.80	0.10	3.84	0.00	1.00	0.22
	Work/task automation	0.74	0.26	0.49	0.82	0.18	0.64	4.10	0.50	0.75	0.17	4.15	0.50	0.75	0.19
	Population relocation	0.21	0.79	−0.59	0.18	0.82	−0.64	3.45	0.50	0.75	0.19	3.40	0.50	0.71	0.20
	Tourism industry	0.95	0.05	0.90	0.62	0.38	0.23	2.62	2.00	−1.0	0.67	3.81	0.88	0.56	0.24
	Logistics industry	1.00	0.00	1.00	0.95	0.05	0.90	4.77	0.00	1.00	0.09	4.39	0.50	0.75	0.14
	Sharing economy	0.90	0.10	0.79	0.74	0.26	0.49	2.86	1.00	0.33	0.45	3.50	0.50	0.75	0.27
	Sustainability	0.72	0.28	0.44	0.74	0.26	0.49	3.77	1.00	0.50	0.31	4.13	0.50	0.75	0.23
Buildings	Diversification of housing types	0.62	0.38	0.23	0.74	0.26	0.69	3.88	0.00	1.00	0.22	4.21	0.50	0.75	0.16
	Residential area per capita	0.67	0.33	0.33	0.72	0.28	0.64	3.96	0.13	0.94	0.22	4.07	0.00	1.00	0.16
	Complexification of housing functions	0.92	0.08	0.85	0.90	0.10	1.00	4.11	0.50	0.75	0.20	4.23	0.50	0.75	0.16
	Intelligent buildings	0.74	0.26	0.49	0.87	0.13	0.95	4.00	0.25	0.88	0.22	4.23	0.50	0.75	0.14
	Mixed-use buildings	0.56	0.44	0.13	0.71	0.29	0.42	3.88	0.00	1.00	0.21	4.14	0.00	1.00	0.15
	Flexible buildings	0.67	0.33	0.33	0.69	0.31	0.59	4.07	0.50	0.75	0.21	4.36	0.50	0.75	0.13
Street blocks and complexes	Hyper-local living areas	0.92	0.08	0.85	0.62	0.38	0.23	4.36	0.50	0.75	0.12	4.04	0.13	0.94	0.21
	Industrial complex recycling	0.15	0.85	−0.69	0.31	0.69	−0.38	3.33	0.50	0.75	0.28	4.07	0.00	1.00	0.15
	Public movement spaces	0.72	0.28	0.44	0.74	0.26	0.49	3.50	1.00	0.50	0.33	4.17	0.50	0.75	0.16
	Public rest spaces	0.87	0.13	0.74	0.82	0.18	0.64	3.56	0.50	0.75	0.31	4.15	0.50	0.75	0.18
	Green spaces	0.90	0.10	0.79	0.82	0.18	0.64	4.06	0.50	0.75	0.24	4.39	0.50	0.75	0.16
	Commercial spaces	0.77	0.23	0.54	0.69	0.31	0.38	2.68	1.25	−0.25	0.52	3.10	1.00	0.33	0.36
	Temporary change of use	0.92	0.08	0.85	0.59	0.41	0.18	4.33	0.50	0.75	0.17	3.83	0.00	1.00	0.21
Cities	Land-use density	0.41	0.59	−0.18	0.38	0.62	−0.23	3.00	1.00	0.33	0.44	3.88	0.63	0.69	0.22
	Transportation facilities and services	0.85	0.15	0.69	0.82	0.18	0.64	3.79	0.00	1.00	0.29	4.09	0.50	0.75	0.20
	Expansion of urban periphery	0.26	0.74	−0.49	0.26	0.74	−0.49	3.83	0.13	0.94	0.14	4.00	0.25	0.88	0.18
	Urban – rural disparities	0.74	0.26	0.49	0.74	0.26	0.49	4.34	0.50	0.75	0.17	4.34	0.50	0.75	0.16
	Smart city	0.92	0.08	0.85	0.95	0.05	0.90	4.33	0.50	0.75	0.15	4.49	0.50	0.80	0.13
	Mega city	0.21	0.79	−0.59	0.31	0.69	−0.38	3.70	0.88	0.50	0.27	4.23	0.50	0.75	0.14

*C.V. : Content Validity.

Figure 2. Response results on the importance of the 35 keywords.

Figure 3. Response results on the architectural and urban spaces expected to undergo major changes.

Matthew, R. A., and B. McDonald. 2006. “Cities Under Siege: Urban Planning and the Threat of Infectious Disease.” Journal of the American Planning Association 72 (1): 109–117.

 Web of Science ®Google Scholar

Menéndez, E., and E. García. 2021. “Urban Sustainability versus the Impact of COVID-19.” The Planning Review 56 (4): 64–81.

 Google Scholar

Hamidi, S., S. Sabouri, and R. Ewing. 2020. “Does Density Aggravate the COVID-19 Pandemic? Early Findings and Lessons for Planners.” Journal of the American Planning Association 86 (4): 495–509.

 Web of Science ®Google Scholar

Dbouk, T., and D. Drikakis. 2020. “On Coughing and Airborne Droplet Transmission to Humans.” Physics of Fluids 32 (5): 053310.

 PubMed Web of Science ®Google Scholar

Kwon, K. S., J. I. Park, Y. J. Park, D. M. Jung, K. W. Ryu, and J. H. Lee. 2020. “Evidence of Long-Distance Droplet Transmission of SARS-CoV-2 by Direct Air Flow in a Restaurant in Korea.” Journal of Korean Medical Science 35 (46 e415).

 PubMed Web of Science ®Google Scholar

Hwang, S. E., J. H. Chang, B. Oh, and J. Heo. 2021. “Possible Aerosol Transmission of COVID-19 Associated with an Outbreak in an Apartment in Seoul, South Kore A, 2020.” International Journal of Infectious Diseases 104: 73–76.

 PubMed Web of Science ®Google Scholar

Lee, S., and S. Choi. 2020. “Analysis of the Impact of COVID-19 on Local Market Areas Using Credit Card Big Data: A Case of Suwon.” Space and Environment 30 (3): 167–208.

 Google Scholar

Kim, J., J. Kim, S. Choi, Y. Cho, S. Kim, and J. Kim. 2021. “Prevalence of Respiratory Viral Infection Using Multiplex Reverse Transcriptase-Polymerase Chain Reaction.” Korean Journal of Family Practice 11 (3): 210–216.

 Google Scholar

Florida, R., A. Rodriguez-Pose, and M. Storper. 2020. “Cities in a Post-COVID World, Papers in Evolutionary Economic Geography (PEEG), 2041.” Utrecht University, 1–28.

 Google Scholar

Tricarico, L., and L. D. Vidovich. 2021. “Proximity and Post-COVID-19 Urban Development: Reflections from Milan, Italy.” Journal of Urban Management 10: 302–310.

 Web of Science ®Google Scholar

Barbarossa, L. 2020. “The Post Pandemic City: Challenges and Opportunities for a Non-Motorized Urban Environment.” An Overview of Italian Cases, Sustainability 12: 1–19.

 PubMed Web of Science ®Google Scholar

Kim, J., D. Ki, and S. Lee. 2021. “Analysis of Travel Mode Choice Change by the Spread of COVID-19 : The Case of Seoul, Korea.” Journal of Korea Planning Association 56 (3): 113–129.

 Google Scholar