The night-time economy serves as a pivotal engine driving consumption upgrading, and the identification of its spatio-temporal differentiation laws is of great significance for optimizing urban resource allocation. Taking the six central districts of Tianjin as the study area, this paper integrates social media data with the Spatial, Temporal, and Space-Time Scan Statistics Software (SaTScan) spatiotemporal scan statistics method to systematically reveal the spatio-temporal dynamic characteristics of night-time economic activities. The main conclusions are drawn as follows: (1) Night-time economic activities exhibit significant seasonal fluctuations, with the intra-day activity showing a single-peak distribution. (2) The spatial pattern of night-time economic activities presents a gradient characteristic of core-periphery, where Heping District and its surrounding streets have a high level of activity. (3) The cultural and tourism belt along the Haihe River and transportation hubs reflect the waterfront economy mode and transportation-oriented mode, respectively, and there are significant differences in the intensity and duration of night-time economic activities across different time periods and locations. This study can provide methodological and case support for research on urban night-time economic vitality.
References
[1] Son, N. N., Thu, N. T. P., Dung, N. Q., Huyen, B. T. T., Xuan, V. N. (2023) Determinants of the sustained development of the night-time economy: the case of Hanoi, capital of Vietnam. Journal of Risk and Financial Management, 16(8), 351.
[2] Brands, J., Schwanen, T., Van Aalst, I. (2015) Fear of crime and affective ambiguities in the night-time economy. Urban Studies, 52(3), 439-455.
[3] Li, Z., Yu, S., Li, X. (2025) Study on the spatial characteristics and influencing factors of night-time economic forms from the perspective of the integration of culture and tourism. Sustainability, 17(22), 10063.
[4] Tuong, N. T. (2022) Developing night-time economy: international experience and policy implications for Da Nang City, Vietnam. Journal of Language and Linguistic Studies, 18(1).
[5] Martí, P., Serrano-Estrada, L., Nolasco-Cirugeda, A. (2019) Social media data: challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74, 161-174.
[6] Chen, M., Liu, Y., Ye, Z., Wang, S., Zhang, W. (2024) Vivid London: Assessing the resilience of urban vibrancy during the COVID-19 pandemic using social media data. Sustainable Cities and Society, 115, 105823.
[7] Li, C., Wu, K., Gao, X. (2020) Manufacturing industry agglomeration and spatial clustering: evidence from Hebei Province, China: C. Li et al. Environment, Development and Sustainability, 22(4), 2941-2965.
[8] Xu, T., Yang, X., Wang, S., Han, J., Chang, L., Yue, W. (2020) Imaging velocity fields analysis of space camera for dynamic circular scanning. IEEE Access, 8, 191574-191585.
[9] Wang, G., Wang, Y., Li, Y., Chen, T. (2023) Identification of urban clusters based on multisource data – an example of three major urban agglomerations in China. Land, 12(5), 1058.
[10] Li, M., Shi, X., Li, X., Ma, W., He, J., Liu, T. (2019) Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method. International Journal of Geographical Information Science, 33(11), 2125-2152.
Share and Cite
Xie, L. (2025) Spatio-temporal Differentiation and Identification of Active Zones of Urban Night-time Economy in Tianjin. Hong Kong Financial Bulletin, 1(6), 51-57. https://doi.org/10.71052/hkfb2025/EDZC8668
