Yang, Tianren 楊天人

Tianren Yang is an Assistant Professor in the Department of Urban Planning and Design at the University of Hong Kong. He is interested in developing advanced urban analytics and modelling to provide an all-round understanding of how cities evolve, particularly in relation to technology, policy and human behaviour. His current research explores integrated policy perspectives to measure and predict how to maximise economic, environmental and social benefits through the spatial coordination of various urban developments (e.g. housing, jobs and transport).

Tianren received a PhD in architecture (applied urban modelling) from the University of Cambridge, an MSc in urban design from the Georgia Institute of Technology, and an MEng in urban planning and a BEng in landscape architecture from Tongji University. As a chartered urban planner, he consults widely for industry in real estate and city development, endeavouring to apply research insights to practical decision-making. He has worked for the China Development Bank, Boston Consulting Group, and EPSRC Centre for Smart Infrastructure and Construction on analysing and modelling large-scale urban changes. So far, he has taken part in the development of computer models for several dominant city regions in the UK (Greater London and Cambridge), the USA (Chicago Metropolitan Area) and China (Greater Shanghai and Beijing-Tianjin-Hebei).

Tianren is accepting applications for PhD students and is available for consultancy.

Fields of Interest

• Urban analytics and modelling
• Urban spatial structure and travel
• Planning support systems for decision-making

Teaching

• URBA6008 Spatial Planning Analytics
• URBP6002 Urban Development Theories

Awards & Achievements (Selected)

• Lincoln Institute Scholar
• Early Career Researcher Award (shortlisted), Royal Town Planning Institute
• International Award for Planning Excellence (commended), Royal Town Planning Institute
• Regional Studies Association PhD Student Award

Publications (Selected)

• Fang, Z., Jin, Y., & Yang, T. (2022). Incorporating planning intelligence into deep learning: A planning support tool for street network design. Journal of Urban Technology 29(2): 99-114. DOI: https://doi.org/10.1080/10630732.2021.2001713
• Fang, Z., Qi, J., Fan, L., Huang, Y., Jin, Y., & Yang, T. (2022). A topography-aware approach to the automatic generation of urban road networks. International Journal of Geographical Information Science. DOI: https://doi.org/10.1080/13658816.2022.2072849
• Wan, L., Yang, T., Jin, Y., Wang, D., Shi, C., Yin, Z., Cao, M., & Pan, H. (2021). Estimating commuting matrix and error mitigation – A complementary use of aggregate travel survey, location-based big data and discrete choice models. Travel Behaviour and Society, 25, 102-111. DOI: https://doi.org/10.1016/j.tbs.2021.04.012
• Yang, T., Jin, Y., and Fang, Z. (2021). Decision-making for urban planning and design with multi-source data: Applications with urban systems models and artificial intelligence. Urban Planning International, 36(2), 1-6. (In Chinese)
• Yang, T. (2020). Understanding commuting patterns and changes: Counterfactual analysis in a planning-support framework. Environment and Planning B: Urban Analytics and City Science, 47(8), 1440-1455.
• Pan, H., Yang, T.*, Jin, Y., Dall’Erba, S., & Hewings, G. (2020). Understanding heterogeneous spatial production externalities as a missing link between land use planning and urban economic futures. Regional Studies, 55(1), 90-100. *Corresponding author.
• Hu, L., Yang, J., Yang, T., Tu, Y., & Zhu, J. (2020). Urban spatial structure and travel in China. Journal of Planning Literature, 35(1), 6-24.
• Yang, T., Jin, Y., Yan, L., & Pei, P. (2019). Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai. Environment and Planning B: Urban Analytics and City Science, 46(7), 1264-1280.
• Yang, T., Pan, H., Hewings, G., & Jin, Y. (2019). Understanding urban sub-centers with heterogeneity in agglomeration economies—Where do emerging commercial establishments locate? Cities, 86, 25-36.