The differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connections, rather than urban entities that are essentially the spatial extents of human activity agglomerations. Thus, this study has attempted to map and evaluate global urban entities (2000–2020) from a perspective of an updated urban concept of urban entities based on the consistent remotely sensed nighttime light data. First, a K-means algorithm was developed to cluster urban and non-urban pixels automatically in consideration of global region division. Then, a post-processing was conducted to enhance the temporal and logical consistency of urban entities during the study period. Rationality assessment indicates that urban entities derived from remotely sensed nighttime light data more effectively reflect the spatial agglomeration extents of human activities than those of physical urban areas. Global urban entities increased from 157,733 km² in 2000 to 470,632 km² in 2020 accompanied by a differentiated urban expansion at global, continental, and national levels. Our study provides long-time series and fine-resolution datasets (500 m) and new research avenues for spatiotemporal analysis of global urban entity expansion with the improvement of the understanding of urbanization and the emergence of effective urban mapping theories and approaches.

The nighttime economy has always been regarded as an important part of the economy. Monitoring and evaluating the nighttime economic level is of great significance for promoting consumption and economic growth and optimizing industrial structure. However, it is difficult to evaluate the nighttime economy in China due to the data being unavailable. Hence, the objective of this study is to identify and evaluate the nighttime economy in China from different perspectives. First, a comprehensive nighttime economic index (CNEI) was constructed by integrating the nighttime light intensity and the points of interest data to represent the nighttime economic level. The CNEI was then verified using the business report data and socioeconomic statistical data. The results show that the CNEI is highly correlated with the verified data. We also found that Shanghai, Chengdu, Guangzhou, and Shenzhen have the highest CNEI values, and the CNEI values of southern cities are generally higher than those of northern cities. This is mainly because the differences in the lifestyles, climatic factors, and cultural customs in the north and south determine the nighttime economic activities. Counties with very high CNEI values are mostly located in the capital cities of each province. The spatial agglomeration at the county level performed more strongly than that at the prefecture level. The study will not only help better understand the nighttime economic level on different scales but also contribute to city-level policymaking on urban planning and economic development.