Urban Region Representation Learning: A Positional and Structural Graph Approach
Resumo
Urban environments are composed of intricate spatial structures and movement patterns, impacting various urban elements like transportation, land use, and economic activity. Firstly, in this dissertation, we review the geospatial artifical intelligence literature on urban region representation models, categorizing them based on learning paradigms, spatial data modalities, and architectural design. Furthermore, we highlight key challenges on region representation models such as the data quality issue, which some datasets are incomplete, presenting mising labeled data. Additionally, urban region models overlook spatial heterophily, where adjacent regions can have varied functions, being an intrinsic characteristic of geographic data. To resolve this, we present HAVANA (Hybrid Attentional Graph Convolutional Network for Semantic Venue Annotation), a model using Graph Neural Networks and human mobility data to advance POI classification improving geographic data quality. Building upon this improved data representation, we introduce a novel spatial heterophily-aware Graph Transformer named FisherGT which is incorporated on HAMURE (Heterophily-Aware Urban Multi-View-Based Region Embedding), a self-supervised multistage model. Hence, HAMURE improves representation quality, yielding better outcomes for land use clustering, crime prediction, population density estimation.
Referências
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Du, J., Zhang, Y., Wang, P., Leopold, J., and Fu, Y. (2019). Beyond geo-first law: Learning spatial representations via integrated autocorrelations and complementarity. In 2019 IEEE International Conference on Data Mining (ICDM), pages 160–169. IEEE.
Dwivedi, V. P. and Bresson, X. (2020). A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699.
Grekousis, G. (2019). Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis. Computers, Environment and Urban Systems, 74:244–256.
Guo, D., Yu, Y., Ge, S., Gao, S., Mai, G., and Chen, H. (2024). Spatialscene2vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation. International Journal of Applied Earth Observation and Geoinformation, 128:103743.
Klemmer, K., Rolf, E., Robinson, C., Mackey, L., and Rußwurm, M. (2023). Satclip: Global, general-purpose location embeddings with satellite imagery. arXiv preprint arXiv:2311.17179.
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P. (2021). Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618–21629.
Li, Y., Huang, W., Cong, G., Wang, H., and Wang, Z. (2023). Urban region representation learning with openstreetmap building footprints. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1363–1373.
Li, Z., Xia, L., Tang, J., Xu, Y., Shi, L., Xia, L., Yin, D., and Huang, C. (2024). Urbangpt: Spatio-temporal large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5351–5362.
Liu, P. and Biljecki, F. (2022). A review of spatially-explicit geoai applications in urban geography. International Journal of Applied Earth Observation and Geoinformation, 112:102936.
Ma, J., Li, B., and Mostafavi, A. (2024). Characterizing urban lifestyle signatures using motif properties in network of places. Environment and Planning B: Urban Analytics and City Science, (4):889–903.
Milias, V. and Psyllidis, A. (2021). Assessing the influence of point-of-interest features on the classification of place categories. Computers, Environment and Urban Systems, 86:101597.
Niu, H. and Silva, E. A. (2021). Delineating urban functional use from points of interest data with neural network embedding: A case study in greater london. Computers, Environment and Urban Systems, 88:101651.
Psyllidis, A., Gao, S., Hu, Y., Kim, E.-K., McKenzie, G., Purves, R., Yuan, M., and Andris, C. (2022). Points of interest (poi): a commentary on the state of the art, challenges, and prospects for the future. Computational Urban Science, 2(1):20.
Reia, S. M., Anderson, T., de Arruda, H. F., Atwal, K. S., Ruan, S., Kavak, H., and Pfoser, D. (2025). Function and form of us cities. Computers, Environment and Urban Systems, 116:102221.
Santos, G. B., Silva, F. A., Silva, T. R. B., and Aylon, L. B. (2025). Semantic motif-based analysis of post-pandemic mobility adaptation in brazil using mobile phone data. In 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1867–1872. IEEE.
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K. (2023). Exphormer: Sparse transformers for graphs. In International Conference on Machine Learning, pages 31613–31632. PMLR.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
Wang, H., Kifer, D., Graif, C., and Li, Z. (2016). Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 635–644.
Wu, Q., Zhao, W., Yang, C., Zhang, H., Nie, F., Jiang, H., Bian, Y., and Yan, J. (2023). Sgformer: Simplifying and empowering transformers for large-graph representations. Advances in Neural Information Processing Systems, 36:64753–64773.
Xiao, C., Zhou, J., Huang, J., Xu, T., and Xiong, H. (2023). Spatial heterophily aware graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2752–2763.
Yang, M., Verma, H., Zhang, D. C., Liu, J., King, I., and Ying, R. (2024). Hypformer: Exploring efficient transformer fully in hyperbolic space. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3770–3781.
Yang, Y., Pentland, A., and Moro, E. (2023). Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Science, 12(1):15.
Yao, Z., Fu, Y., Liu, B., Hu, W., and Xiong, H. (2018). Representing urban functions through zone embedding with human mobility patterns. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).
Ye, M., Shou, D., Lee, W.-C., Yin, P., and Janowicz, K. (2011). On the semantic annotation of places in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 520–528.
Zhang, Q., Huang, C., Xia, L., Wang, Z., Li, Z., and Yiu, S. (2023a). Automated spatio-temporal graph contrastive learning. In Proceedings of the ACM Web Conference 2023, pages 295–305.
Zhang, Q., Huang, C., Xia, L., Wang, Z., Yiu, S. M., and Han, R. (2023b). Spatial-temporal graph learning with adversarial contrastive adaptation. In International Conference on Machine Learning, pages 41151–41163. PMLR.
Zhang, Z., Wang, M., Xiang, Y., Huang, Y., and Nehorai, A. (2018). Retgk: Graph kernels based on return probabilities of random walks. Advances in Neural Information Processing Systems, 31.
Zhu, A.-X. and Turner, M. (2022). How is the third law of geography different? Annals of GIS, 28(1):57–67.
Bittencourt, T. A. and Giannotti, M. (2021). The unequal impacts of time, cost and transfer accessibility on cities, classes and races. Cities, 116:103257.
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio, P., and Bronstein, M. M. (2023). On over-squashing in message passing neural networks: The impact of width, depth, and topology. In International Conference on Machine Learning, pages 7865–7885. PMLR.
Dong, L., Duarte, F., Duranton, G., Santi, P., Barthelemy, M., Batty, M., Bettencourt, L., Goodchild, M., Hack, G., Liu, Y., et al. (2024). Defining a city—delineating urban areas using cell-phone data. Nature Cities, 1(2):117–125.
dos Santos, G. B., Melos, G. C., Figueiredo, L. J., Silva, F. A., Silva, T. R., and Loureiro, A. A. (2025). Spatial optimization of charging networks for heavy-duty evs using hexagonal discrete models. Journal of Internet Services and Applications, 16(1):268–286.
dos Santos, G. B., Silva, P. H., Silva, F. A., Silva, T. R. B., and Aylon, L. B. (2024). Havana: Hybrid attentional graph convolutional network semantic venue annotation model. In Brazilian Conference on Intelligent Systems, pages 291–305. Springer.
Du, J., Zhang, Y., Wang, P., Leopold, J., and Fu, Y. (2019). Beyond geo-first law: Learning spatial representations via integrated autocorrelations and complementarity. In 2019 IEEE International Conference on Data Mining (ICDM), pages 160–169. IEEE.
Dwivedi, V. P. and Bresson, X. (2020). A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699.
Grekousis, G. (2019). Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis. Computers, Environment and Urban Systems, 74:244–256.
Guo, D., Yu, Y., Ge, S., Gao, S., Mai, G., and Chen, H. (2024). Spatialscene2vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation. International Journal of Applied Earth Observation and Geoinformation, 128:103743.
Klemmer, K., Rolf, E., Robinson, C., Mackey, L., and Rußwurm, M. (2023). Satclip: Global, general-purpose location embeddings with satellite imagery. arXiv preprint arXiv:2311.17179.
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P. (2021). Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618–21629.
Li, Y., Huang, W., Cong, G., Wang, H., and Wang, Z. (2023). Urban region representation learning with openstreetmap building footprints. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1363–1373.
Li, Z., Xia, L., Tang, J., Xu, Y., Shi, L., Xia, L., Yin, D., and Huang, C. (2024). Urbangpt: Spatio-temporal large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5351–5362.
Liu, P. and Biljecki, F. (2022). A review of spatially-explicit geoai applications in urban geography. International Journal of Applied Earth Observation and Geoinformation, 112:102936.
Ma, J., Li, B., and Mostafavi, A. (2024). Characterizing urban lifestyle signatures using motif properties in network of places. Environment and Planning B: Urban Analytics and City Science, (4):889–903.
Milias, V. and Psyllidis, A. (2021). Assessing the influence of point-of-interest features on the classification of place categories. Computers, Environment and Urban Systems, 86:101597.
Niu, H. and Silva, E. A. (2021). Delineating urban functional use from points of interest data with neural network embedding: A case study in greater london. Computers, Environment and Urban Systems, 88:101651.
Psyllidis, A., Gao, S., Hu, Y., Kim, E.-K., McKenzie, G., Purves, R., Yuan, M., and Andris, C. (2022). Points of interest (poi): a commentary on the state of the art, challenges, and prospects for the future. Computational Urban Science, 2(1):20.
Reia, S. M., Anderson, T., de Arruda, H. F., Atwal, K. S., Ruan, S., Kavak, H., and Pfoser, D. (2025). Function and form of us cities. Computers, Environment and Urban Systems, 116:102221.
Santos, G. B., Silva, F. A., Silva, T. R. B., and Aylon, L. B. (2025). Semantic motif-based analysis of post-pandemic mobility adaptation in brazil using mobile phone data. In 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1867–1872. IEEE.
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K. (2023). Exphormer: Sparse transformers for graphs. In International Conference on Machine Learning, pages 31613–31632. PMLR.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
Wang, H., Kifer, D., Graif, C., and Li, Z. (2016). Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 635–644.
Wu, Q., Zhao, W., Yang, C., Zhang, H., Nie, F., Jiang, H., Bian, Y., and Yan, J. (2023). Sgformer: Simplifying and empowering transformers for large-graph representations. Advances in Neural Information Processing Systems, 36:64753–64773.
Xiao, C., Zhou, J., Huang, J., Xu, T., and Xiong, H. (2023). Spatial heterophily aware graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2752–2763.
Yang, M., Verma, H., Zhang, D. C., Liu, J., King, I., and Ying, R. (2024). Hypformer: Exploring efficient transformer fully in hyperbolic space. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3770–3781.
Yang, Y., Pentland, A., and Moro, E. (2023). Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Science, 12(1):15.
Yao, Z., Fu, Y., Liu, B., Hu, W., and Xiong, H. (2018). Representing urban functions through zone embedding with human mobility patterns. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).
Ye, M., Shou, D., Lee, W.-C., Yin, P., and Janowicz, K. (2011). On the semantic annotation of places in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 520–528.
Zhang, Q., Huang, C., Xia, L., Wang, Z., Li, Z., and Yiu, S. (2023a). Automated spatio-temporal graph contrastive learning. In Proceedings of the ACM Web Conference 2023, pages 295–305.
Zhang, Q., Huang, C., Xia, L., Wang, Z., Yiu, S. M., and Han, R. (2023b). Spatial-temporal graph learning with adversarial contrastive adaptation. In International Conference on Machine Learning, pages 41151–41163. PMLR.
Zhang, Z., Wang, M., Xiang, Y., Huang, Y., and Nehorai, A. (2018). Retgk: Graph kernels based on return probabilities of random walks. Advances in Neural Information Processing Systems, 31.
Zhu, A.-X. and Turner, M. (2022). How is the third law of geography different? Annals of GIS, 28(1):57–67.
Publicado
19/07/2026
Como Citar
SANTOS, Germano B.; SILVA, Fabrício A..
Urban Region Representation Learning: A Positional and Structural Graph Approach. In: CONCURSO DE TESES E DISSERTAÇÕES DA SBC (CTD-SBC), 39. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 150-159.
ISSN 2763-8820.
DOI: https://doi.org/10.5753/ctd.2026.19457.
