Data-driven approach to characterize urban vitality: how spatiotemporal context dynamically defines Seoul’s nighttime

ABSTRACT

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.

KEYWORDS:

• Big data
• nighttime economy
• spatiotemporal analysis
• uncertain contextual effects
• functional principal component analysis

Acknowledgments

This article benefited from constructive feedback from anonymous reviewers and the editors, which is highly appreciated. The author first thanks Kyunghee Han and Chanwoo Jin who provided insight that greatly inspired me to begin this research. The constructive comments from Yuko Aoyama, Anthony Bebbington, Samuel Ratick, and Jung Won Sonn are also gratefully appreciated. Yooinn Hong, Prajjwal Panday, Kristen Shake, and Renee Tapp read a draft and gave productive feedback. This is a substantially revised version of an earlier paper presented in the Seoul Research Competition organized by the Seoul Institute and the Student Paper Competition hosted by the Spatial Analysis and Modeling Specialty Group of the American Association of Geographers. The usual disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the author.

Data and codes availability statement

The codes that support the findings of this study are available in figshare.com with the identifier http://doi.org/10.6084/m9.figshare.10302614. The pedestrian traffic and bank card transaction data cannot be made publicly available to protect privacy and consent with the data provider.

Notes

1. Jongno, Jung, Yeongdeungpo, Gangnam, Seocho, and Songpa Districts.

Additional information

Notes on contributors

Young-Long Kim

Young-Long Kim is currently Research Fellow at the Gyeonggi Research Institute in South Korea after getting his PhD in Geography at Clark University. To answer questions on the complex nature of human dynamics and economic development in cities, he seeks new data sources and methodologies. Most recently, he cooperated with the Seoul Institute to measure urban vitality using big data and the location of Wi-Fi hotspots. With his strong background in computer languages, he has coded in C++, PHP, and Python for various projects.