The nighttime economy, or NTE—the combination of nocturnal specialties and the extension of the diurnal economy into the night—has been implemented as an effective boosting instrument set to “revitalize the urban space”. The instrument, applied in regions around world, develops new socioeconomic dynamics and poles of growth within cities. Although some cases emphasize the functional success of NTE practices, disequilibrium between urban elements—social groups, communities, and the cultures in which they live—are ongoing. The present article argues that urban nights must be considered within a broader reflection on the question of sustainability because a closer integration between brands, cultural elements, employment, and capital in different scales is demonstrated at night. Based on growth pole theory, this study combines data mining, spatial modeling, and other complementary approaches, and successfully (1) identifies the growth poles of the NTE in Lanzhou City, a postindustrial city transitioning towards a nighttime economy that forms dotted and non-uniform nocturnal zones through its geography and demography; (2) characterizes their socioeconomic organization, and (3) analyzes various causes and manifestations of the disequilibrium.

Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.