Nighttime light (NTL) data have become increasingly practical and are now widely used in studies on urbanization, energy consumption, population estimation, socio-economic evaluation, etc. Based on NTL data and the basic tourism economy (TE) data from 31 provinces of China in 2019, this paper adopted a geographic concentration index, inconsistency index, spatial agglomeration coupling index, global and Local Moran’s index and geographical detector to explore the spatial relationship between NTL and TE. The results of the study were as follows. Firstly, there is a high spatial correlation between NTL and TE. Secondly, the concentration degree, as well as the concentrated distribution area of NTL and TE, are very similar, roughly showing a higher concentration in East and South-Central China. Thirdly, NTL and TE show a type of coordinated development in East and North China, and a TE surpassing NTL in Southwest and South-Central China. The spatial agglomeration coupling index is higher in North China, South-Central China and the coastal regions of East China, and relatively lower in Southwest and Northwest China. Furthermore, in the spatial agglomeration distribution of NTL and TE, there is an obvious high–high and low–low agglomeration. Finally, the geographical detector analysis showed that the driving factor of tourism economy level (TEL) also has a great influence on NTL. The spatial distribution of NTL and TE is integrated to reasonably allocate tourism resources for different areas and promote the sustainable development of NTL and TE among regions.
The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.
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