In a blog post last year, the scientist Stephen Wolfram, creator of the Mathematica software and the “computational knowledge engine” Wolfram|Alpha, suggested that the next revolution would be in “personal analytics”. He demonstrated this idea by collating and charting his life using his vast archive of personal data, including every email he had sent since 1989. In this way, he could see which years were the most busy or what times of the day he sent the most emails. His desktop calendar also revealed data about the course of an average day, while his phone records showed who he was talking to and for how long.
Wolfram’s company has already released a similar app on Facebook, so that people can chart their personal data to see everything from which friends live the highest above sea level to how networks of contacts interweave. We are, Wolfram might suggest, the sum of our information trail. Our lives have become data sets to be probed, charted and, once collated, analysed for efficiencies and savings.
In the new age of “Big Data”, does the same go for our cities? Just as Wolfram has reduced his life to packets of data, many urban thinkers now believe that the city is no longer just a place but a living field of information to be harvested.
Big claims are being made for this notion. Le Corbusier once called for the rationalisation of the city, making it a machine for living; today, many think that data, in the words of Assaf Biderman, the associate director of MIT’s Senseable City Lab, will make our cities “more human”.
Urban living used to be an art. Now, it is a science, burdened with the heavy-sounding label of “quantitative urbanism”. It is preached with the moral fervour of a Victorian public health official and involves some of the biggest names in the software, consultancy and infrastructure industries: IBM, Cisco, Philips, McKinsey & Company and Booz Allen Hamilton, among others.
Yet away from the hard sell, does this quantitative approach to ourselves and our cities tell us anything? Is the accumulation of data the same as the development of knowledge?
In 2003, the British-born physicist Geoffrey West started to study the metabolism of cities and soon came up with some surprising results. West wanted to find out whether the zoological rules first devised in the 1930s by Max Kleiber – which showed how all forms of life, from a fly to an elephant, follow the same equation that combines size, energy use and life expectancy – might apply to something as large and chaotic as a city.
West and his team at the Santa Fe Institute gathered together a huge data set: measurements of scale for urban centres in the US of over 50,000 citizens; statistics on “gross metropolitan product”; crime figures; the amount of money made by petrol stations in all 50 states; patents, as well as tax returns. Then, they put it all together into one database. They also included figures from the National Bureau of Statistics of China and Eurostat and even measurements of road surfaces from across Germany, as well as the amount of copper used in overhead wiring.
Surprisingly, the results reduced the life of a city to a mathematical rule: a Kleiberesque “unified theory of urban living”. So, while we can view individual cities as having their own particular history and personality, underlying rules apply that mean they have a lot in common with each other.
Yet cities do not follow Kleiber’s law exactly – rather than slowing down as they get bigger, cities speed up: they become more productive, creative, efficient and sustainable. As West points out, if you tell him the size and population of a city, he can cal – culate its crime rate, the number of patents it produces a year, how many petrol stations it needs, how many HIV-positive people reside there. According to West, the essential characteristics of a city can be reduced to an equation. Size matters, it seems.
Other urban thinkers, meanwhile, are starting to use the mathematics of complexity in an attempt to rethink how cities work. In this method, our understanding of networks and their emergent properties allow us to see how cities might work like beehives, ant or termite hills, the flow of liquids or the neural patterns of the brain.
This new urbanism – which views the city as a combination of networks and information – does not, in the words of John Keats, unweave the rainbow but forces us to question some of our long-held assumptions: what we consider to be the ideal size for a city; how we can use the qualities of complexity to rethink how the city is organised. Often, these discussions are conducted in the esoteric language of calculus and network theory. However, this can only have an impact when it is once again translated back into the language of the city – a place made up of people.
However, it would be wrong to think that data is the story. Information is the message, not the medium, and we need to be careful that this full-throttle embrace of data does not wash away the many other ways of looking at the city.
Just as Wolfram’s personal analytics do not show us the full extent of his life story, quantitative urbanism does not give us a complete picture of the modern city with all its elements. As complexity theory tells us, one of the characteristics of a self-organ – ising system – such as a city or a beehive – is that it will always be more than the sum of its parts.