It is within this context that I was invited to participate at the International Design Week for Digital Urban Design by South East University Nanjing (SEU) and the International Society of City and Regional Planners (ISOCARP) in Nanjing, China.

The organisers of the event assembled four teams of architecture, landscape architecture and planning students from some of China’s top universities to participate. Each of the groups was supported by an international expert and a local expert as well as faculty staff from SEU.

The focus of the week was to use city data in the regeneration of an existing neighbourhood in the north-west of the city. The challenge was to use the data to not only understand the dynamics of the neighbourhood but to demonstrate how it influenced design decisions as well as considered the ongoing impact and integration of technology and data on the future evolution of the area.

The intense five-day programme involved site visits, tutorials, lectures and a final presentation to a panel of judges made up of senior Professors from SEU, experts in digital technologies from the University of Sheffield and Sebastien Goethals - the vice-president of ISOCARP.

Students were given a baseline set of city data which included information on existing land uses, buildings, points of interest, mobile phone and Mobike movement data but were also encouraged to source and explore other publically available data.

Some teams explored social media ‘chatter’ to understand how people felt about their neighbourhood and whether residents had an emotional attachment to place. Other groups used data mining techniques to extract thousands of streetview photos and used machine learning to analyse the images and assess the quality of the streetscape and the quantum and connection to greenspace, or used data models to simulate the movement of people after design interventions had been made.

There are some things that machines can do very well and things that they can’t. Humans and machines can also have a different perspective on the same things. When the teams visited the site we tended to point our cameras at things that interested us or caught our eye and therefore took a more emotive but filtered perspective to the site.

With machines this isn’t the case, they are systematic and comprehensive and able to process vast amounts of information that humans can’t. No one-way is correct but there should a recognition that both have value.

There was a recognition that not all segments of society have equal access to technology and that this could be a disadvantage for them. The teams quickly found that not having data or finding gaps in the data could tell you something too – it often correlated with areas of older residents who did not use mobile phones as often as younger residents.

In a neighbourhood that had a large ageing population the discussion became focused on how can technology and access to data itself be made more accessible to older residents and how design decisions shouldn’t be prejudiced by data generated by a narrow segment of society.