Embedding Irregular Urban Regions With Multi-view Fusion Network
Pages 279 - 286
Abstract
The functions of urban regions are diverse and complex, making the accurate understanding and identifying these functions is crucial for urban planning and management. However, previous methods usually delineate urban regions based on regular network regions, failing to capture the functional variability of real-world regions segmented by road networks. In this paper, we focus on synthesizing and analyzing data from multiple sources to derive an embedded representation of irregular urban regions and propose a multi-view fusion network based on road information (MVFR). This approach reveals the functional characteristics of different urban regions and helps to realize cross-domain tasks. Firstly, we divide the regions based on the road network and construct a similarity topology map by fusing MicroBlog text data from social media and detailed point-of-interest(POI) data. Then, we introduce a multi-level cross-attention mechanism to efficiently learn comprehensive embeddings from multiple views and further utilize a multi-layer perceptron (MLP) and clustering analysis. This approach enables in-depth mining and prediction of the functional characteristics of urban regions while maintaining intra-view node interaction and inter-view information exchange. Finally, we validate the method on real data, and experimental results demonstrate its effectiveness in several related but different cross-domain prediction tasks.
References
[1]
Michael Batty. 2010. The New Science of Cities. Building Research and Information 38, 1 (2010), 123–126.
[2]
Chao Min Chang, Kuei Ching Lee, W. U. Ci-Hao, and Chia Heng Lin. 2022. TEXT-TO-VECTORIZED REPRESENTATION TRANSFORMATION.
[3]
Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu, Guochun Chen, Siyuan Feng, Ji Xiang, Junbo Zhang, and Yu Zheng. 2022. A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling. arXiv preprint arXiv:https://arXiv.org/abs/2206.00007 (2022).
[4]
Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, and Xiaolin Li. 2019. Efficient Region Embedding with Multi-View Spatial Networks: A Perspective of Locality-Constrained Spatial Autocorrelations. In National Conference on Artificial Intelligence.
[5]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. ACM (2020).
[6]
Li Hou, Qi Liu, Jamel Nebhen, Mueen Uddin, Mujahid Ullah, and Naimat Ullah Khan. 2021. Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data. Hindawi Limited (2021).
[7]
KANUNGO and T. 2004. A local search approximation algorithm for k-means clustering*1. Computational Geometry 28, 2-3 (2004), 89–112.
[8]
Guanghui Li, Tao Fang, Yuejin Zhang, Cheng Liang, Qiu Xiao, and Jiawei Luo. 2022. Predicting miRNA-disease associations based on graph attention network with multi-source information. BMC Bioinformatics 23, 1 (2022), 1–24.
[9]
Shuang Li, Jun Sasaki, Carlos Porcel Gallego, and Enrique Herrera-Viedma. 2020. Features Words Extraction Methods of POI for Japanese FIT Using Comments in a Tourism Web Site. In International Conference on Information Modelling and Knowledge Bases.
[10]
Yingming Li, Ming Yang, and Zhongfei Zhang. 2019. A Survey of Multi-View Representation Learning. IEEE Transactions on Knowledge and Data Engineering 31, 10 (2019), 1863–1883.
[11]
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. 2021. Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. arXiv e-prints (2021).
[12]
Yao Long, Lin Zhang, and Tao Yuan. 2020. Proximity-Based Aggregation Method for LBS Human Mobility Data. (2020).
[13]
Gang Lv, Yining Sun, and Fudong Nian. 2024. Video-text retrieval via multi-modal masked transformer and adaptive attribute-aware graph convolutional network. Multimedia systems 30, 1 (2024), 35.1–35.12.
[14]
D. W Ruck. 1990. Feature Selection Using a Multilayer Perceptron. Neural Network Comput 2 (1990), 40–48.
[15]
Thiago H. Silva and Mark S. Fox. 2024. Integrating social media data: Venues, groups and activities. Expert Systems with Application 243, Jun. (2024), 122902.1–122902.12.
[16]
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. 2015. Multi-view Convolutional Neural Networks for 3D Shape Recognition. IEEE (2015).
[17]
Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, and Yu Zheng. 2020. Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks. IEEE Transactions on Knowledge and Data Engineering PP, 99 (2020), 1–1.
[18]
Qixing Sun, Xiaodong Jia, and Xiao Yuan Jing. 2022. Addressing Contradiction Between Reconstruction andCorrelation Maximization inDeep Canonical Correlation Autoencoders. Springer, Cham (2022).
[19]
Stephan Tilgner, Daniel Wagner, Kathrin Kalischewski, Jan Christoph Schmitz, and Anton Kummert. 2021. Study on the Influence of Multiple Image Inputs of a Multi-View Fusion Neural Network Based on Grad-CAM and Masked Image Inputs. In 2020 28th European Signal Processing Conference (EUSIPCO).
[20]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[21]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:https://arXiv.org/abs/1710.10903 (2017).
[22]
Feng Wang and Yunpeng Gong. 2023. Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data. Springer, Cham (2023).
[23]
Hongjian Wang and Zhenhui Li. 2017. Region Representation Learning via Mobility Flow. In the 2017 ACM.
[24]
Ziyi Wang, Debin Ma, Dongqi Sun, and Jingxiang Zhang. 2021. Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLoS ONE 16, 5 (2021), e0251988–.
[25]
Felix Wu, Tianyi Zhang, Amauri Holanda De Souza, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. (2019).
[26]
Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, and Cheng Wang. 2022. Multi-Graph Fusion Networks for Urban Region Embedding. (2022).
[27]
Junpeng Xu, Min Meng, and Jigang Wu. [n. d.]. Enhanced Fusion Network through Redundancy Elimination for Deep Multi-view Clustering. Journal of Computer-Aided Design & Computer Graphics ([n. d.]).
[28]
Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan. 2022. Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps. arXiv preprint arXiv:https://arXiv.org/abs/2212.09034 (2022).
[29]
Zhian Yang, Hao Jiang, Lingyue Huang, and Yiming Liu. 2024. Multi Feature Extraction and Trend Prediction for Weibo Topic Dissemination Network. Journal of Signal Processing Systems 96, 2 (2024), 113–129.
[30]
Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, and Hui Xiong. 2018. Representing Urban Functions through Zone Embedding with Human Mobility Patterns. In Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18.
[31]
Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. ACM (2012).
[32]
Nicholas Jing Yuan, Yu Zheng, and Xing Xie. 2012. Segmentation of Urban Areas Using Road Networks. microsoft technical report (2012).
[33]
Mingyang Zhang, Tong Li, Yong Li, and Pan Hui. 2021. Multi-view joint graph representation learning for urban region embedding. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 4431–4437.
[34]
W. Zhao, Q. Li, and B. Li. 2011. Extracting hierarchical landmarks from urban POI data. Journal of Remote Sensing (2011).
[35]
Li Yu Zheng. 2019. Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning. SIGKDD explorationsUdisk (2019).
[36]
Qinghai Zheng, Jihua Zhu, Zhongyu Li, Zhiqiang Tian, and Chen Li. 2023. Comprehensive Multi-view Representation Learning. Information Fusion (2023).
[37]
Yang Zhenshan, Long Ying, and Nicolas Douay. 2015. Opportunities and limitations of big data applications to human and economic geography:the state of the art. Progress in Geography (2015).
[38]
Silin Zhou, Dan He, Lisi Chen, Shuo Shang, and Peng Han. 2023. Heterogeneous Region Embedding with Prompt Learning. (2023).
[39]
Xiaolu Zhou and Liang Zhang. 2016. Crowdsourcing functions of the living city from Twitter and Foursquare data. Cartography and geographic information science5 (2016), 43.
Index Terms
- Embedding Irregular Urban Regions With Multi-view Fusion Network
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October 2024
448 pages
ISBN:9798400717482
DOI:10.1145/3704323
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Published: 07 January 2025
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ICCPR 2024
ICCPR 2024: 2024 13th International Conference on Computing and Pattern Recognition
October 25 - 27, 2024
Tianjin, China
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