BIG CITY, BIG IDEAS: Data Innovation and City Governance

Earlier this year, as part of my sabbatical at the Munk School of Global Affairs, I gave a public lecture in Toronto on the theme “Data Innovation and City Governance”. This was part of the University of Toronto’s ‘Big City, Big Ideas’ series, and I was following in the distinguished footsteps of speakers such as Richard Florida, Mayor Naheed Nenshi, Michael Storper, Meric Gertler, and others.

You can view the webcast of my talk here.

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London House Prices – a Borough Cartogram

Even though I am in Toronto I couldn’t resist looking at this excellent House Price cartogram for London. I agree with  Ollie O’Brien when he says ‘We like the simple, “grid of squares” concept and the addition of the Thames. Cartograms are hard to produce in a way that makes them familiar to an audience familiar with Google Maps, but with this concept, that challenge may have been met.’  I think I would go further. By limiting the cartographic verisimilitude, and producing a bold simplified map that works intuitively – red to green for prices, and compass points in the right place – it is in the excellent and radical tradition of Harry Beck, creator of the familiar London Underground map. Sometimes less really is more.

London to Toronto

First thoughts on arrival in TO:

1. BA is right that the 787 Dreamliner is much quieter than earlier jets, and the air seemed fresher etc. BA critics are right that the leg-room is poor.

2. Rapid transit to Pearson will start in May, not a moment too soon.

3. Presto smart card system will finally get rolled out across,TTC this year, not a moment too soon.

4. My sixth time in Toronto, but my first for an extended stay. As in London, palpable sense of economic and population growth, transit and housing issues to the fore. Immigration is a key driver of economic growth and vice versa.

5. I walked around West Queen Street West on Sunday, a classic gentrification moving frontier and according to Vogue (who am I to disagree?) the second coolest neighbourhood on the planet.

6. This city takes food and eating seriously.

7. Sunday afternoon was sunny and relatively warmer. At the first sign of better weather, Toronto residents are out in the parks and on porches, making the absolute best of it, a great (and I understand Canadian) characteristic.

Big Data and Cities – “Nobody’s good at this”

This article by John Lorinc in the Globe and Mail sets out in a balanced way both the opportunities and the potential pitfalls of applying Big Data to a host of urban issues. Lorinc sees New York, Chicago and Boston as leading the pack, driven forward by activist Mayors. He also looks at how Toronto and other Canadian cities are beginning to get into the game, while arguing that there is some catching up to do. Lorinc quotes Professor Stephen Goldsmith from Harvard’s Kennedy School of Government – with whom I shared a panel at the Smart Cities Expo in Barcelona last year – as saying that the application and intelligent analysis of data torepresents a sea change in thinking that could rival the shift to professional municipal management that marked the dawn of the Progressive Era over a century ago:  “Whenever we’re talking about data, we’re talking about modernizing how government works”.

Smart Cities needs smart clients – that is, we have to define the problem we are trying to solve, make best use of the data and tools we already use, and develop solutions from the bottom up before rushing to would-be top down comprehensive solutions.

Lorinc refers to the observations of Mike Flowers, formerly head of Data Analytics for Mayor Bloomberg in New York City and now with the Centre for Urban Science and Progress (CUSP) in New York.

Mr. Flowers points out that city officials shouldn’t be tempted to blindly make huge investments in “smart city” information technology in order to foster such insights. Indeed, his group relied on off-the-shelf spreadsheets to compile the data that led to New York’s dramatic analytics breakthroughs…While most municipalities in recent years have released large tranches of raw information – road-closure locations, transit schedules, and other intelligence – through so-called “open data” portals, the game-changing potential lies in interpreting those mountains of quotidian facts and finding new ways of putting them together. The analytics is equal parts art and science. As Mr. Flowers says, “Nobody’s good at this.”

This is very much the same as the approach we hope to develop in London through the Smart London Plan – ambitious in vision and scope, but realistic and sceptical (in the best sense of that word) in terms of next steps and above all conscious of the need to engage with and respond to citizens.

The Automatic Statistician: helping people make sense of their data

The fascinating site “The Automatic Statistician” gives a glimpse into the near-future where not just data analysis, but report-writing and conclusion-drawing will become a shared activity between humans and machines.

The creators of the site explain their purpose thus:

Making sense of data is one of the great challenges of the information age we live in. While it is becoming easier to collect and store all kinds of data, from personal medical data, to scientific data, to public data, and commercial data, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. The Automatic Statistician project aims to build an artificial intelligence for data science, helping people make sense of their data.

The current version of the Automatic Statistician is a system which explores an open-ended space of possible statistical models to discover a good explanation of the data, and then produces a detailed report with figures and natural-language text. While at Cambridge, James Lloyd, David Duvenaud and Zoubin Ghahramani, in collaboration with Roger Grosse and Joshua Tenenbaum at MIT, developed an early version of this system which not only automatically produces a 10-15 page report describing patterns discovered in data, but returns a statistical model with state-of-the-art extrapolation performance evaluated over real time series data sets from various domains. The system is based on reasoning over an open-ended language of nonparametric models using Bayesian inference.

Kevin P. Murphy, Senior Research Scientist at Google says: “In recent years, machine learning has made tremendous progress in developing models that can accurately predict future data. However, there are still several obstacles in the way of its more widespread use in the data sciences. The first problem is that current Machine Learning (ML) methods still require considerable human expertise in devising appropriate features and models. The second problem is that the output of current methods, while accurate, is often hard to understand, which makes it hard to trust. The “automatic statistician” project from Cambridge aims to address both problems, by using Bayesian model selection strategies to automatically choose good models / features, and to interpret the resulting fit in easy-to-understand ways, in terms of human readable, automatically generated reports. This is a very promising direction for ML research, which is likely to find many applications at Google and beyond.”

The project has only just begun but we’re excited for its future. Check out our example analyses to get a feel for what our work is about.