Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.

Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.