Publications

You can also find my articles on my Google Scholar profile and latest CV.

Journal Articles

• Hong, Y., Stüdeli, E., and Raubal, M. (2023). Evaluating geospatial context information for travel mode detection. Journal of Transport Geography, 113, 103736.
• Hong, Y., Zhang, Y., Schindler, K., and Raubal, M. (2023). Context-aware multi-head self-attentional neural network model for next location prediction. Transportation Research Part C: Emerging Technologies, 156, 104315.
• Martin, H., Hong, Y., Wiedemann, N., Bucher, D., and Raubal, M. (2023). Trackintel: An open-source Python library for human mobility analysis. Computers, Environment and Urban Systems, 101, 101938.
• Hong, Y., Martin, H., Xin, Y., Bucher, D., Reck, D. J., Axhausen, K. W., and Raubal, M. (2023). Conserved quantities in human mobility: from locations to trips. Transportation Research Part C: Emerging Technologies, 146, 103979.
• Yao, Y., Guo, Z., Dou, C., Jia, N., Hong, Y., Guan, Q., and Luo, P. (2023). Predicting mobile users’ next location using the semantically enriched geo-embedding model and the multilayer attention mechanism. Computers, Environment and Urban Systems, 104, 102009.
• Wiedemann, N., Martin, H., Suel, E., Hong, Y., and Xin, Y. (2023). Influence of tracking duration on the privacy of individual mobility graphs. Journal of Location Based Services.
• Yao, Y., Zhou, J., Sun, Z., Guan, Q., Guo, Z., Xu, Y., Zhang, J., Hong, Y., Cai, Y., and Wang, R. (2023). Estimating China’s poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniques. Geo-Spatial Information Science, 1-17.
• Guan, Q., Yao, Y., Ma, T., Hong, Y., Bie, Y., and Lyu, J. (2022). Under the Dome: A 3D Urban Texture Model and Its Relationship with Urban Land Surface Temperature. Annals of the American Association of Geographers, 112(5), 1369-1389.
• Yao, Y., Wang, J., Hong, Y., Qian, C., Guan, Q., Liang, X., Dai, L. and Zhang, J. (2021). Discovering the homogeneous geographic domain of human perceptions from street view images. Landscape and Urban Planning, 212, 104125.
• Yao, Y., Liu, Y., Guan, Q., Hong, Y., Wang, R., Wang, R., and Liang, X. (2021). Spatiotemporal distribution of human trafficking in China and predicting the locations of missing persons.Computers, Environment and Urban Systems, 85, 101567.
• Zhang, J., Li, X., Yao, Y., Hong, Y., He, J., Jiang, Z., and Sun, J. (2021). The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types. International Journal of Geographical Information Science, 35(1), 193-211.
• Yao, Y., Wu, D., Hong, Y., Chen, D., Liang, Z., Guan, Q., Xun, L. and Dai, L. (2020). Analyzing the Effects of Rainfall on Urban Traffic-Congestion Bottlenecks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 504-512.
• Chen, D., Zhang, Y., Yao, Y., Hong, Y., and Guan, Q. (2019). Exploring the spatial differentiation of urbanization on two sides of the Hu Huanyong Line – based on nighttime light data and cellular automata. Applied Geography, 112, 102081.
• Yao, Y., Liu, P., Hong, Y., Liang, Z., Wang, R., Guan, Q., and Chen, J. (2019). Fine-scale intra- and intercity commercial store site recommendations via multisource big data. Transactions in GIS, 23(5), 1029-1047.
• Hong, Y., and Yao, Y. (2019). Hierarchical community detection and functional area identification with OSM roads and complex graph theory. International Journal of Geographical Information Science, 33(8), 1569-1587.
• He, J., Li, X., Yao, Y., Hong, Y., and Zhang, J. (2018). Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science, 32(10), 2076-2097.
• Yao, Y., Hong, Y., Wu, D., Zhang, Y., and Guan, Q. (2018). Estimating the effects of “community opening” policy on alleviating traffic congestion in large Chinese cities by integrating ant colony optimization and complex network analyses. Computers, Environment and Urban Systems, 70, 163-174.
• Yao, Y., Zhang, J., Hong, Y., Liang, H., and He, J. (2018). Mapping fine–scale urban housing prices by fusing remotely sensed imagery and social media data. Transactions in GIS, 22(2), 561-581.
• Liu, X., He, J., Yao, Y., Zhang, J., Liang, H., Wang, H., and Hong, Y. (2017). Classifying urban land use by integrating remote sensing and social medias data. International Journal of Geographical Information Science, 31(8), 1675-1696.
• Yao, Y., Liu, X., Li, X., Liu, P., Hong, Y., Zhang, Y., and Mai, K. (2017). Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science, 31(12), 2452-2479.

Conference Proceedings

• Wiedemann, N., Hong, Y., and Raubal, M. (2023). Predicting visit frequencies to new places. In Proceedings of the 12th International Conference on Geographic Information Science (GIScience ‘23), (pp.84:1–84:6). Leeds, UK. Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
• Hong, Y., Martin, H., and Raubal, M. (2022). How do you go where? improving next location prediction by learning travel mode information using transformers. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL ‘22), (pp. 1-10). Seattle, USA. Association for Computing Machinery.
• Martin, H., Wiedemann, N., Suel, E., Hong, Y., and Xin, Y. (2022). Influence of tracking duration on the privacy of individual mobility graphs. In Proceedings of the 17th International Conference on Location-Based Services, (pp.78–88). Munich, Germany. Technical University of Munich.
• Hong, Y., Xin, Y., Martin, H., Bucher, D., and Raubal, M. (2021). A clustering-based framework for individual travel behaviour change detection. In Proceedings of the 11th International Conference on Geographic Information Science (GIScience 2021) - Part II, (pp.4:1–4:15). Online. Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
• Martin, H., Bucher, D., Hong, Y., Buffat, R., Rupprecht, C., and Raubal, M. (2020). Graph-resnets for short-term traffic forecasts in almost unknown cities. In Proceedings of the NeurIPS 2019 Competition and Demonstration Track, (pp.153–163). Vancouver, Canada. PMLR.

Preprints and Working Papers

• Li, J., Xin, Y., Hong, Y., and Raubal, M. (2023). Interpreting Deep Learning Models for Traffic Forecast: A Case Study of UNet. Under review.
• Timans, A., Wiedemann, N., Kumar, N., Hong, Y., and Raubal, M. Uncertainty Quantification for Image-based Traffic Prediction across Cities. Under review.
• Dirmeier, S., Hong, Y., Xin, Y., and Perez-Cruz, F. Uncertainty quantification and out-of-distribution detection using surjective normalizing flows. Under review.
• Dirmeier, S., Hong, Y., and Perez-Cruz, F. Synthetic location trajectory generation using categorical diffusion models. Under review.
• Hong, Y., Xin, Y., Dirmeier, S., Perez-Cruz, F., and Raubal, M. Revealing behavioral impact on mobility prediction networks through causal intervention. In preparation.