Big data and downtown revival: when is the microphone too close to the speaker?

I have just spent the afternoon at a fascinating event organised by Montreal’s downtown SDC (the Société de Développement Commercial, somewhat similar to a Business Improvement District). It consisted of three panels and a couple of presentations about the role data – particular big-data – could play in downtown’s revival.

“The revival of downtown by data” – Photo: Richard Shearmur
Can data really revive downtown?

I approached the event with interest, but also with a little scepticism: data will not revive downtown unaided, so the afternoon’s title “The revival of downtown by data” was announcing an unlikely process.

I was pleasantly surprised. The SDC has taken the initiative of partnering with a variety of data providers, such as Google and Moneris. This will allow the SDC to generate and make available key indicators that will help the area position itself relative to other Canadian cities, to chart how it is evolving through time, and to assist downtown businesses in understanding the environment within which they are operating.

…of course data won’t revive downtown, but…

The presentations by Google and Moneris gave some insight into how the data they gather can be used to understand, and even forecast, what is happening downtown. Big data is here: so compiling it and using it in order to better understand and assist downtown revival is a useful, indeed a necessary, endeavour.

The three panels gathered a variety of public and private sector data users, some from large organisations, others from smaller groups. Participants discussed the potential uses of data, the types of data they need and produce, and the types of data that may be helpful to further the SDC’s ambition of ensuring open, timely and useful information for its members. Each panel had a different theme: mobility, real-estate, and tourism, each with a different take on data and how it can be best used.

When is the microphone too close to the speaker?

I left the session fascinated by the potential, and impressed by the participants and by the SDC’s intiative (hats off to Glenn Castanheira).

I also have one concern. Unquestioning fascination with big-data is waning – which is a good thing. Organisations such as the SDC are now partnering with data gatherers, who use their impressive arsenal of data and analytics to produce practical, useable, and almost real-time information. Big-data is becoming a useful tool.

Predictive algorithms are such that organisations such as Google can even provide fairly accurate, if short-term, predictions.

In this context, there is a danger that data cease to be predictive: rather, as users obtain the best and most accurate predictions, they will begin to shape their behaviour as a response to these predictions.

Data will no longer predict the future, but shape it.

This is a real issue: if data begin to shape the future, the future becomes trapped in the past (data, after all, are always historic, even if gathered only a few seconds ago). Predictions are, likewise, projections of the past : sophisticated predictions are based on an understanding of past behaviour, on complex interactions, on imagination, and on trends – but they still rest on the past.

So a question arises: at what point do up-to-date data, that can usefully be used to understand structural trends as well as short-term fluctuations, have a causal impact on the trends and fluctuations they predict?

At what point does this feedback, which all data participate in to some extent, become so powerful that it destabilises the system?

When is the microphone too close to the speaker?

I have no answer – but it is important for the SDC to consider this. Whilst positive trends and predictions may lead to an upward spiral, if trends turn negative a downard spiral is equally likely…. needless to say, downward spirals are more difficult to recover from!

Downtown does not need wild oscillations: rather it would benefit from stable and detailed data to help medium- to long- term decision-making. How can data be gathered and marshalled to this end?

Published by Richard Shearmur

I am a professor at McGill's School of Urban Planning. I perform research on innovation, on how we locate work activities (in a world where people often work from many places), and on urban and regional economic geography. I used to work in real-estate, and teach a course on this. I am an urban planner, member of the Ordre des Urbanistes du Québec and of the Canadian institute of Planners.

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