Quantifying the nighttime economy–housing separation from a human activity standpoint: A case study in Shenzhen, China

• Published in: Cities Vol. 148; p. 104894
• Main Authors: Li, Mingxiao, Tu, Wei, Tong, Huan, Cao, Jinzhou, Zhong, Chen, Zhang, Hengcai, Zhu, Jiasong, Li, Qingquan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024
• Subjects: Human activity; Mobile phone data; Nighttime economy; Nighttime economy–housing separation; Mobile phone data; Nighttime economy; Nighttime economy–housing separation; Human activity
• ISSN: 0264-2751; 1873-6084
• DOI: 10.1016/j.cities.2024.104894

Disparities between the supply of nighttime economic services and the demand of local residents have caused a series of problems. By linking massive mobile phone data and an anchor-based activity inference algorithm, we propose a data-driven framework to quantify the separate development of the nighttime economy and housing from a human activity standpoint. The framework includes three perspectives: individual travel distance, imbalance ratio distribution, and the spatial structure of the nighttime economy–housing interactions. Using the city of Shenzhen as the case study area, we explored the corresponding nighttime economy–housing separation patterns. A series of comparative analyses with the job-housing separation were conducted for comparison. The analysis results indicate that the separate development of the nighttime economy and housing is a common and alarming phenomenon. Over 15 % of the nighttime economic activities occurred over 5 km from the residents' homes. Residents preferred to conduct their nighttime economic activities closer to home than commuting for the same. Residents' nighttime economic activities have formed a relatively fixed spatial structure. The possible causes are explored in terms of economic development, scale effects, and administrative divisions. This study contributes to a more holistic understanding of the nighttime economy. Our findings can promote the nighttime economy development and inform urban renewal policy. •Geotagged human activity data enhanced the spatiotemporal granularity of nighttime economy research.•Residents generally travel shorter for nighttime economic activities compared to commuting.•The nighttime economy-housing separation is evident and exhibits a spatial community structure.