GCN2CDD: A Commercial District Discovery Framework via Embedding Space Clustering on Graph Convolution Networks
2021
Modern enterprises attach much attention to the selection of commercial locations. With the rapid development of urban data and machine learning, we can discover the patterns of human mobility with these data and technology to guide commercial district discovery. In this paper, we propose an unsupervised commercial district discovery framework via embedding space clustering on graph convolution networks (GCN2CDD) to solve the problem of commercial district discovery. Specifically, the proposed framework aggregates human mobility features according to geographic similarity by graph convolution networks. Based on the graph convolution networks embedding space, we apply hierarchical clustering to mine the latent functional regions hidden in different human patterns. Then with the kernel density estimation, we can obtain the semantic labels for the clustering results to discover commercial districts. Finally, we analyze the multi-sources data of the Xiaoshan District and Chengdu City and experiments verify the effectiveness of our framework.
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