The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies.

中文翻译：

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基于多源大数据融合的城市群边界划分-以粤港澳大湾区为例

准确划定城市群边界，不仅有助于更好地了解城市群之间城市之间的发展关系，而且有助于指导区域功能以及制定区域管理政策。同时，土地关系与城市内部关系的融合可以大大提高城市群边界划界的准确性。尽管如此，以前的研究仅从土地关系的角度划定了边界。在本研究中，首先，小波变换分别用于融合夜光数据（NTL），兴趣点（POI）数据和腾讯迁移数据。然后，通过多分辨率分割对图像进行分割，以勾勒出GBA的城市群边界。最后，对结果进行验证。结果表明，用NTL数据描述的城市群边界精度为85.57％，Kappa值为0.6256。融合POI数据后，准确度为88.97％，Kappa值分别为0.7011。而且，通过人口融合数据的连续融合勾勒出城市群边界的准确性达到了93.60％，Kappa值为0.8155。因此，可以得出结论，与仅基于土地关系划定城市群的边界相比，通过小波变换融合城市群的人口流动数据可以加强城市群之间城市之间的联系，并有助于城市群的精确划分。边界。更，