Abstract
Due to the heterogeneity of spatial data, spatial co-location patterns are not all global prevalent patterns. There are regional prevalent patterns that can only appear in specific local areas. Regional co-location pattern mining (RCPM) is designed to discover co-location patterns like these. The regional co-location patterns can reveal the association relationships among spatial features in the local regions. However, most studies only divide the functional regions through density of instances, ignoring the spatial correlation within, which makes the identification results biased towards a higher number of instances (such as restaurants, convenience stores, etc.), and may not present the functional characteristics of regional differences effectively. In the stage of RCPM, we propose a new algorithm for mining regional co-location patterns. By using the method of representation learning to extract the feature vectors of POI types with the help of the word embedding model, and then the functional areas of the city are divided. This method uses word vector to represent the semantic information of words, so that semantically similar words are close to each other in the representation space, and the division of regions is more reasonable. Compared to the existing algorithms, our method demonstrates a greater potential, as evidenced by experimental results.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (62276227, 62306266), the Project of Innovative Research Team of Yunnan Province (2018HC019), the Yunnan Fundamental Research Projects (202201AS070015) and the Scientific Research Fund Project of Yunnan Provincial Department of Education (2023Y0248).
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Cai, Y., Wang, L., Zhou, L., Chen, H. (2024). RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_10
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