Assistant Professor
The Chinese University of Hong Kong (CUHK)
About me
I am an Assistant Professor in Department of Geography and Resource Managementat, The Chinese University of Hong Kong (CUHK).
My research addresses the interactions between human activities and the environment by developing innovative geospatial analytical methods. I strive to address the issues of the impact of the environment on human health through the lens of movement flows, transportation related emissions and traffic congestion, and built environment. My research primarily relies on large movement datasets (e.g., GPS trajectories), together with a variety of other data sources including individual GPS tracking, mobile sensing, remote sensing, land use, built environment and activity travel diaries (my CV).
Email: zihankan@cuhk.edu.hk
Office: Sino Building 237, The Chinese University of Hong Kong
Research Interests: Spatial data analysis and modeling; GPS trajectory mining; Human-environment interactions; Geospatial health studies
Opportunities
I am looking for highly self-motivated PhD students in the areas of GeoAI, human mobility modeling, greenspace quality, quantity, and accessibility analysis, as well as urban environmental sensing. If you’re passionate about these topics and wish to join my research group, please email me your CV and a brief research proposal.
Please note that my response may be slow due to my teaching and research schedule, as I am not on email very often. Thank you for your understanding.
What’s new (2026)
Award: The International Geographical Union (IGU) Early Career Award (2026).
Paper: [Not all greenspace indicators are equal. We build a framework to pick the right ones across contexts.] Su, L., Kan, Z*., Wang, J., Kwan, M.P. and Liu, D., 2026. Developing a Context-Sensitive Framework for Evaluating Urban Greenspace Indicators Across Diverse Geographic Settings. Environmental Research, p.126012. My student as the first author.
Paper: [Bus travel burdens stack up: congestion and crowding hit the same riders at once.] Wei, J., Kan, Z*. and Kwan, M.P., 2026. Activity-driven dual exposure to congestion and crowding: Exploring multiple constraints for peak-hour bus travel burdens using smart card data. Transportation Research Part A, 214, p.105263. My student as the first author.
Paper: [Park access equity for older adults depends on popularity and 3D walking networks, not just distance.] Liu, D., Nakaya, T., Cai, T., Huang, J., Luo, J., Wang, J., Liu, Y., Xian, Z. and Kan, Z*., 2026. A GeoAI-informed floating catchment area framework for assessing spatiotemporal equity of park accessibility for elderly population using dynamic popularity weighting and 3D pedestrian network. Urban Forestry & Urban Greening, p.129529.
Paper: [Dual exposure to traffic congestion and in-vehicle crowding among senior and regular cardholders, revealed through smart card data.] Wei, J., Kan, Z*. and Kwan, M.P., 2026. Examining dual exposure to traffic congestion and in-vehicle crowding among senior and regular cardholders using smart card data. Cities, 178, p.107379. My student as the first author.
Paper: [We measure mobility-related social exclusion at the network level.] Kan, Z., Wei, J., Schwanen, T., Kwan, M.P., Liu, D. and Huang, J., 2026. A network-based approach to understand the risk of mobility-related social exclusion in spatiotemporal activities. Journal of Transport Geography, 134, p.104712.
Paper: [Can AI make park access fairer for people with mobility impairments?] Kan, Z., Huang, J., Liu, D., Pereira, R.H. and Gilliland, J., 2026. Integrated AI framework combining routing, computer vision, and large language models to assess urban park access disparities among people with and without mobility impairments. Sustainable Cities and Society, p.107528.
Paper: [A new spatiotemporal network flow L function identifies multi-scale human flow clusters.] Zhao, Z., Kwan, M.P. and Kan, Z., 2026. Identifying multi-scale human flow clusters via spatiotemporal network flow L function. International Journal of Applied Earth Observation and Geoinformation, 152, p.105428.
Project: NSFC General Fund (2027–2030). 城市”人-车-物”活动流的“语义-场景”耦合表征与演化机制研究 (Semantic-Scene” coupling representation and evolution mechanisms of urban “human–vehicle–goods” activity flows). RA and Postdoc positions will be available at the CUHK-Shenzhen Research Institute (SZRI). Stay tuned for details!
Project: CUHK Teaching Development and Language Enhancement Grant (TDLEG, 2026–2028). Repositioning students as critical evaluators through disciplinary context and AI critique.
Project: CUHK Direct Grant (2026–2027). From Image to Experience: Modeling Passenger Perception in Indoor Transit Spaces.
Paper: [Reinforcement learning for dynamic non-motorized route planning.] Yang, X., Wu, C., Yan, Z., Kan, Z., Guan, Q. and Tang, L., 2026. Enhancing non-motorized travel using reinforcement learning: a novel dynamic route planning approach. Geo-spatial Information Science, pp.1-21.
Paper: [Generating tactile paving networks from street view images.] Yang, X., Zhang, Z., Zeng, Y., Kan, Z. and Tang, L., 2026. SAFNet: A Framework for Generating Tactile Paving Networks from Street View Images. Journal of Geovisualization and Spatial Analysis, 10(1), p.14.
Paper: [From micro mobility to macro patterns: a vector field-based framework extracts coherent micro–macro traffic dynamics.] Shi, H., Qi, H., Kan, Z*., Guo, X. and Tang, L., 2026. From micro mobility to macro patterns: a vector field-based framework powered by a data-driven flow model for extracting coherent micro-macro traffic dynamics. International Journal of Geographical Information Science, pp.1-29. My RA as the first author.
Paper: [PM2.5–influenza risk estimated from home–school–commute exposure modeling for schoolchildren in Guangzhou.] Jing, F., Hu, J., Zhou, S., Kan, Z., He, G., Hu, J., Liu, T., Zhang, M., Luo, L., Ma, M. and Lin, Z., 2026. Estimating PM2.5–influenza risk using home–school–commute exposure modeling from hourly monitoring data: schoolchildren in Guangzhou, China, 2014–2019. BMC Public Health.
Paper: [Sequential activity-travel patterns and spatial structures among older and younger adults from smart card data.] Wei, J., Kan, Z*. and Kwan, M.P., 2026. Revealing sequential activity-travel patterns and spatial structures among older and younger adults from smart card data. Transportation Research Part A: Policy and Practice, 203, p.104749. My student as the first author.
Paper: [A dual-focused machine learning evaluation of pandemic vulnerability through infection risk and vaccination accessibility.] Liu, D., Gilliland, J., Nakaya, T., Kan, Z*., Nagata, S. and Huang, J., 2026. Strengthening Urban Pandemic Preparedness: A Dual-Focused Machine Learning-Enhanced Spatial Evaluation of Pandemic Vulnerability Through Infection Risk and Vaccination Accessibility. Applied Spatial Analysis and Policy, 19(1), p.1.
Book chapter: [Applications in urban sensing for smart cities.] Liu, D., Kan, Z., Fei, T., Chen, Y., Zhang, Y. and Zheng, Q., 2026. Applications in urban sensing for smart cities. In Multimodal Remote Sensing Fusion and Classification (pp. 293-347). Elsevier.