UNIVERSITY OF HONG KONG
FACULTY OF ARCHITECTURE

05 Nerves

Director/Producer: Tom COZENS
Music: Tom COZENS
Script: Tom COZENS and Professor Chris WEBSTER

Executive Producers: Professor Chris WEBSTER and Dr Eric SCHULDENFREI
Assistant Producer: Alex TAIT
Production Assistant: Winnie YEUNG
Editor: Nick BRIER

Academic Contributors: Dr Mengdi GUO, Dr Jianxiang HUANG, Minjung MAING and Anqi ZHANG
Actors: Daniel CHAN, Dr Guibo SUN and Yi SUN

Acceleration of wireless and sensing technologies such as 5G means that cities can be wired up and automated like factories began to be half a century ago. What efficiencies will ‘smart city’ technologies achieve? Many current claims are trivial or even undesirable (who wants a micro-climate-sensing computer to select your clothes in the morning?). For the first 50 years after the steam engine was invented, labour productivity and wages remained flat or increased very slowly. The same has been true of the first 50 years of the silicon revolution. The efficiency dividend of new technology, be it labour-replacing or labour-enhancing, takes a long time. Will smart city tech turn out to be a bottle-neck technology, the arrival of which will help release the long-awaited productivity and social benefits of the silicon age? Or will the smart city turn out to be something no one really wanted or needed? Will it become something sinister?

Chris Webster, HKU, 2021

Featured Locations:

Pak Wai Flower
School of Architecture, The Chinese University of Hong Kong

Featured HKUrbanLabs:

iLab (urban informatics and AI, BIM, smart cities, construction automation)
Sustainable High Density Cities Lab (urban climate science and sustainable city planning)
Centre of Urban Studies and Urban Planning (advanced urban analytics)

Readings:

Chen, K., Lu, W., Xue, F., Tang, P., & Li, L. H. (2018). Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge. Automation in Construction, 93, 22-34. https://doi.org/10.1016/j.autcon.2018.05.009

Liu, X., Song, Y., Wu, K., Wang, J., Li, D., & Long, Y. (2015). Understanding urban China with open data. Cities, 47, 53-61. https://doi.org/10.1016/j.cities.2015.03.006

Ma, J., & Cheng, J. C. P. (2016). Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology. Applied Energy, 183, 182-192. https://doi.org/10.1016/j.apenergy.2016.08.079

Zhao, Z., Koutsopoulos, H. N., & Zhao, J. (2018). Individual mobility prediction using transit smart card data. Transportation Research Part C: Emerging Technologies, 89, 19-34. https://doi.org/10.1016/j.trc.2018.01.022

Zhu, J., & Yeh, A. G.-O. (2012). A self-learning short-term traffic forecasting system. Environment and Planning B: Planning and Design, 39(3), 471-485. https://doi.org/10.1068/b36174

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