CREATech 2017

Notes from a Smart Cities panel I was part of at CREATech 2017

What is a Smart City?

Theres a one line answer which is probably very obvious: smart cities use data and technology to make cities better. The important part though is that they make cities better for people – that is happier, healthier and wealthier.

So how do we as Space Syntax fit into this?

We’re an employee owned company that spun out of UCL more than 25 years ago as a way of taking a technology that links the street networks of cities to patterns of human activity and interaction.

The company spun out to make impact on real projects around the world, with the aim to make cities better for people – to create the conditions that allow people to be healthier, wealthier and happier.

And starting with people is a good way to get in to the conversation around smart cities, but also future cities, urban tech and cities in general.

Firstly, cities are for people, and unless we can link the way that city systems are designed and operated, to the way that people use them, they won’t continue to be the social and economic engines that they are.

By using urban analytics and predictive analytics, it means that we can start to understand the way that cities work in a very detailed way, and it means that we can look at how the things in a city that can be changed – how the street network, where jobs are, where houses are, where public transport lines run – how these systems create opportunities or restrictions for the people who live in them.

We’ve been using this to develop a better design process – to use data and evidence to understand why things happen in certain places. With this understanding it means we can tailor a very precise intervention or improvement, and it also means we can test future scenarios before they are implemented.

In the last five years or so the wider attitude to this approach has changed a huge amount.

And in that time there’s also been a huge increase in the amount of open data available, and also in the capabilities of software and hardware. What this means is that we can now make bigger models, using more data, covering a wider range of factors, and we can analyse them more quickly.

And while this quite quickly begins to set up a whole series of technical questions about  how to combine different data, the key question is actually why?

Through our experience we’ve found something slightly counter-intuitive which is that the more complicated the model, the more simple its outputs. But more importantly, it also means that we can look at the way a city works from the point of a number of different users:

This could be in the way that a city resident might ask them, – things like: “How easily can I get to work?”; “Do I need a car if I live here?”, or; “What’s my choice of decent schools or doctors if live here?”

But also a policy maker – “Does this part of a city encourage people to walk or cycle?’; “Will this create a socially isolated community?” or; “Will older people here be more lonely?”

And this focus on the way that urban systems combine to affect people in the city, provides a potential shift to the way cities have been planned and designed. It provides a common focus for the different disciplines and professions involved in making decisions around cities.

Over many years these disciplines have become very sophisticated but tend to run in parallel to each other, using different approaches, data, models, and even language. This means that each urban system tends to be optimised by itself, which makes it more difficult for cities to continue being succesful.

So the application of technology in this way provides the potential to tie these strands together around a common outcome.

While tech has the potential to bring disciplines together, it also has the potential to create its own new disciplinary silo. The risk of this is that with an even higher requirement for domain and technical expertise, this silo becomes impenetrable, making decisions less transparent not more.

Part of the solution to this is technical, but linking back to people again, perhaps a bigger part of this challenge is to develop working practices and cultures that make the most of this potential.

If we can do this it means we can make cities better for people, creating the urban conditions for them to be healthier, wealthier, and happier.