This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection of vast amounts of urban data. This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. However, this shift also raises significant methodological and ethical concerns, particularly regarding privacy, reliability, and accuracy. The paper discusses the theoretical underpinnings of urban vitality, current technological advancements, and the challenges and future directions in urban studies. It highlights the need for an interdisciplinary approach to fully harness the potential of emerging technologies in developing livable, sustainable, and responsive cities.

Firms may grow and decline, and their impact on the regional economy can be attributed not only to each firm but also to their inter-firm networks in the region. Therefore, firms and their connectivity should be understood within the context of the regional industrial ecosystem. To empirically show the role of inter-firm networks for the sustainability of the regional industrial ecosystem, this study analyses the automotive industry in South Korea. The Automobile Parts Yearbook, the main data source for the study, provides the addresses of 892 firms and the connectivity between five major automakers and their subcontractors. A network-based approach is chosen to untangle the complex production network and compare the network structure by region. Specifically, the number of nodes, links and connections, as well as density and modularity measures, are analytically compared across six sub-regions in the country. There are more links within the groups than between the groups, which suggests preferential attachment in the network structure. Multiple centralised structures are observed to exist around the five major automakers in sub-regions in South Korea. The empirical results of the paper imply that firms with multiple trading networks in the regional industrial ecosystem tend to recover from an industrial crisis or employment shock crisis more successfully than companies with a single trading network. Overall, these findings highlight the importance of understanding the role of inter-firm networks in regional industrial ecosystems for promoting sustainability and resilience.

Abstract The COVID-19 pandemic has fundamentally changed human mobility patterns in cities. Lockdowns, social distancing and flexible working hours have restructured pre-existing dynamics between two opposing forces in major cities: centripetal and centrifugal. To scrutinise the new dynamics, human mobility in the Seoul Metropolitan Area in early 2020 was investigated using big data collected from cell phone activity. By suggesting a network-based approach to untangle complex human mobility in the urban network, this research contributes to understanding how the COVID-19 shock impacted human mobility patterns in everyday life and how human behaviours adapted to the new normal.

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.

초록·키워드 목차 오류제보하기 등록된 정보가 없습니다. I. 들어가며II. 책의 구성III. 아시아 연구에 기여하는 의의IV. 네트워크적 관점의 발전을 위한 제언참고문헌

This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection of vast amounts of urban data. This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. However, this shift also raises significant methodological and ethical concerns, particularly regarding privacy, reliability, and accuracy. The paper discusses the theoretical underpinnings of urban vitality, current technological advancements, and the challenges and future directions in urban studies. It highlights the need for an interdisciplinary approach to fully harness the potential of emerging technologies in developing livable, sustainable, and responsive cities.

Firms may grow and decline, and their impact on the regional economy can be attributed not only to each firm but also to their inter-firm networks in the region. Therefore, firms and their connectivity should be understood within the context of the regional industrial ecosystem. To empirically show the role of inter-firm networks for the sustainability of the regional industrial ecosystem, this study analyses the automotive industry in South Korea. The Automobile Parts Yearbook, the main data source for the study, provides the addresses of 892 firms and the connectivity between five major automakers and their subcontractors. A network-based approach is chosen to untangle the complex production network and compare the network structure by region. Specifically, the number of nodes, links and connections, as well as density and modularity measures, are analytically compared across six sub-regions in the country. There are more links within the groups than between the groups, which suggests preferential attachment in the network structure. Multiple centralised structures are observed to exist around the five major automakers in sub-regions in South Korea. The empirical results of the paper imply that firms with multiple trading networks in the regional industrial ecosystem tend to recover from an industrial crisis or employment shock crisis more successfully than companies with a single trading network. Overall, these findings highlight the importance of understanding the role of inter-firm networks in regional industrial ecosystems for promoting sustainability and resilience.

Abstract The COVID-19 pandemic has fundamentally changed human mobility patterns in cities. Lockdowns, social distancing and flexible working hours have restructured pre-existing dynamics between two opposing forces in major cities: centripetal and centrifugal. To scrutinise the new dynamics, human mobility in the Seoul Metropolitan Area in early 2020 was investigated using big data collected from cell phone activity. By suggesting a network-based approach to untangle complex human mobility in the urban network, this research contributes to understanding how the COVID-19 shock impacted human mobility patterns in everyday life and how human behaviours adapted to the new normal.

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.