Abstract
Identifying the region’s functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due to the diversified and ambiguity nature of urban regions, there are still some significant challenges to be resolved in urban POI demand analysis. To this end, we propose a novel framework, in which Region-of-Interest Demand Modeling is enhanced through the graph representation learning, namely Variational Multi-graph Auto-encoding Fusion, aiming to effectively predict the ROI demand from both the POI level and category level. Specifically, we first divide the urban area into spatially differentiated neighborhood regions, extract the corresponding multi-dimensional natures, and then generate the Spatial-Attributed Region Graph (SARG). After that, we introduce an unsupervised multi-graph based variational auto-encoder to map regional profiles of SARG into latent space, and further retrieve the dynamic latent representations through probabilistic sampling and global fusing. Additionally, during the training process, a spatio-temporal constrained Bayesian algorithm is adopted to infer the destination POIs. Finally, extensive experiments are conducted on real-world dataset, which demonstrate our model significantly outperforms state-of-the-art baselines.
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The codes and the corresponding dataset are available at https://github.com/AdvancedDataProcessing/VMAF.
Notes
https://bj.lianjia.com, Nov. 2020.
https://bj.ke.com, Nov, 2020.
https://lbs.amap.com, Mar. 2020.
https://developers.google.com/maps, Oct. 2019.
https://www.weibo.com, May 2017.
References
Chen W, Zhao L, Xu JJ et al (2015) Trip oriented search on activity trajectory. J Comput Sci Technol 30:745–761. https://doi.org/10.1007/s11390-015-1558-6
Fan D, Qin K, Kang C (2018) Understanding urban functionality from poi space. In: 2018 26th International conference on geoinformatics, IEEE, 1–6. https://doi.org/10.1109/geoinformatics.2018.8557122
Fu Y, Ge Y, Zheng Y, et al (2014) Sparse real estate ranking with online user reviews and offline moving behaviors. In: 2014 IEEE international conference on data mining, IEEE, 120–129. https://doi.org/10.1109/icdm.2014.18
Fu Y, Wang P, Du J, et al (2019) Efficient region embedding with multi-view spatial networks: a perspective of locality-constrained spatial autocorrelations. In: Proceedings of the AAAI conference on artificial intelligence, pp 906–913. https://doi.org/10.1609/aaai.v33i01.3301906
Gong L, Liu X, Wu L et al (2016) Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr Geogr Inf Sci 43(2):103–114. https://doi.org/10.1080/15230406.2015.1014424
Guo S, Lin Y, Feng N, et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI conference on artificial intelligence, pp 922–929. https://doi.org/10.1609/aaai.v33i01.3301922
Jin Y, Ma G, Yang S, et al (2020) Point-of-interest demand discovery using semantic trajectories. In: International conference on database systems for advanced applications, Springer, pp 280–296. https://doi.org/10.1007/978-3-030-59416-9_17
Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv:1611.07308https://doi.org/10.48550/arXiv.1611.07308
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105. https://doi.org/10.1145/3065386
Li Q, Yu Z, Guo B et al (2019) Housing demand estimation based on express delivery data. ACM Trans Knowl Discov Data 13(4):1–25. https://doi.org/10.1109/bigdata.2018.8621904
Liu Y, Liu C, Lu X, et al (2017) Point-of-interest demand modeling with human mobility patterns. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 947–955. https://doi.org/10.1145/3097983.3098168
Li R, Zhong T, Jiang X, et al (2022) Mining spatio-temporal relations via self-paced graph contrastive learning. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 936–944. https://doi.org/10.1145/3534678.3539422
Sun Y, Zhu H, Zhuang F, et al (2018) Exploring the urban region-of-interest through the analysis of online map search queries. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2269–2278. https://doi.org/10.1145/3219819.3220009
Tiebout CM (1956) A pure theory of local expenditures. J Polit Econ 64(5):416–424. https://doi.org/10.1086/257839
Wang B, Wang A, Chen F et al (2019) Evaluating word embedding models: methods and experimental results. APSIPA Trans Signal Inf Process 8:e19. https://doi.org/10.1017/atsip.2019.12
Wang P, Chen W, Huang J et al (2021) Location prediction for facility placement by incorporating multi-characteristic information. Intell Data Anal 25(5):1187–1210. https://doi.org/10.3233/ida-205420
Wang D, Fu Y, Wang P, et al (2020) Reimagining city configuration: automated urban planning via adversarial learning. In: Proceedings of the 28th international conference on advances in geographic information systems, pp 497–506. https://doi.org/10.1145/3397536.3422268
Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244. https://doi.org/10.1145/2783258.2783273
Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 186–194. https://doi.org/10.1145/2339530.2339561
Yu R, Ye D, Li J (2020) Repidem: a refined poi demand modeling based on multi-source data. In: IEEE INFOCOM 2020-IEEE conference on computer communications, IEEE, 964–973. https://doi.org/10.1109/infocom41043.2020.9155294
Zhang Y, Fu Y, Wang P, et al (2019) Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1700–1708. https://doi.org/10.1145/3292500.3330972
Zhang M, Li T, Li Y, et al (2020) Multi-view joint graph representation learning for urban region embedding. In: Proceedings of the 29th international joint conference on artificial intelligence. AAAI Press, pp 4431–4437. https://doi.org/10.24963/ijcai.2020/611
Zhou J, Cui G, Hu S et al (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001
Zhou X, Noulas A, Mascolo C, et al (2018) Discovering latent patterns of urban cultural interactions in wechat for modern city planning. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1069–1078. https://doi.org/10.1145/3219819.3219929
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under the Grant Numbers: 61876117, 62272332.
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The National Natural Science Foundation of China (61876117, 62272332).
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Pu Wang proposed the conceptualization, analyzed and validated the experimental results, and wrote the main manuscript text. Jingya Sun scrubbed and maintained the experimental dataset, conducted the experiments, and prepared all the figures and tables. Wei Chen and Lei Zhao reviewed and edited the entire manuscript text. All authors reviewed the final manuscript.
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Wang, P., Sun, J., Chen, W. et al. Towards effective urban region-of-interest demand modeling via graph representation learning. Data Min Knowl Disc 38, 3503–3530 (2024). https://doi.org/10.1007/s10618-024-01049-4
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DOI: https://doi.org/10.1007/s10618-024-01049-4