Chang Tzu-Cheng, Tang Jia-Hong, Chan Ta-Chien
Department of Geography, National Taiwan Normal University, Taipei, Taiwan.
Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.
PLoS One. 2025 Jun 11;20(6):e0325696. doi: 10.1371/journal.pone.0325696. eCollection 2025.
Nighttime light (NTL) data serve as a valuable proxy for accessing urbanization and socio-economic activities at various scales. This study investigated the spatiotemporal evolution of NTL intensity in Taipei City from January 2018 to June 2023 using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) via the Google Earth Engine (GEE) platform. A grid system comprising 1,211 cells (500-m resolution) was established to integrate land use, road networks, population, electricity consumption, and business prosperity into temporal, spatial, and spatiotemporal models using Integrated Nested Laplace Approximations (INLA). Additionally, spatiotemporal patterns were analyzed through the space-time cube in ArcGIS Pro. This finding highlights the strong influence of commercial activities and electricity consumption on NTL intensity, with persistent hotspots in commercial and industrial areas and cold spots in forested and agricultural zones. This study underscores the potential of NTL data to capture the interplay between urbanization, land use, and socioeconomic factors. Emphasizing land use as a central analytical focus provides a scalable framework for future urban studies and policy development that can be applied to diverse urban contexts.
夜间灯光（NTL）数据是在不同尺度上获取城市化和社会经济活动的宝贵替代数据。本研究利用谷歌地球引擎（GEE）平台上的可见红外成像辐射计套件（VIIRS）昼夜波段（DNB）数据，调查了2018年1月至2023年6月台北市NTL强度的时空演变。建立了一个由1211个单元格（500米分辨率）组成的网格系统，使用集成嵌套拉普拉斯近似法（INLA）将土地利用、道路网络、人口、电力消耗和商业繁荣整合到时间、空间和时空模型中。此外，通过ArcGIS Pro中的时空立方体分析了时空模式。这一发现突出了商业活动和电力消耗对NTL强度的强烈影响，商业区和工业区存在持续的热点，而森林和农业区则存在冷点。本研究强调了NTL数据在捕捉城市化、土地利用和社会经济因素之间相互作用方面的潜力。将土地利用作为核心分析重点，为未来的城市研究和政策制定提供了一个可扩展的框架，可应用于不同的城市背景。
