1. Governance
June 9, 2015updated 20 Jul 2021 3:38pm

Here’s what London can learn from New York’s data-driven approach to smart cities

By Eddie Copeland

How should London go about becoming a smart city? As the capital seeks to meet record demand on its infrastructure and public services, it’s a question that has been occupying the minds of City Hall, policymakers, academics and industry alike.

Some believe London should emulate the technology-led approach of cities like Rio de Janeiro, with its network of urban sensors and NASA-style operations centre. Today, in a report for the Capital City Foundation, I argue a better starting point would be to learn from the comparatively low-tech, but data-driven, methods of New York City.

The Mayor’s Office of Data Analytics

Michael Bloomberg made his fortune providing data analytics for the financial sector. So when he became New York mayor, he wanted to prove that data could benefit cities, too. To that end, he created the Mayor’s Office of Data Analytics (MODA), a small team of data analysts who can combine, interrogate and seek insights from data sourced from public sector organisations across the entire city.

MODA has applied its data expertise to improve the efficiency of public services, predict problems and prevent them from arising, target the city’s resources more effectively, boost economic growth and support tax enforcement.

To illustrate the benefits of such an approach, consider how it worked with the New York Fire Department. Every year, FDNY inspects more than 25,000 buildings it believes may be at risk of future fires. It used to prioritise buildings for inspection based on a list of criteria created by fire fighters themselves. As MODA’s first director, Mike Flowers, has put it:

“Veteran fire fighters know what dangerous buildings look like. They know how important it is for a building to have an operable sprinkler system, the impact that the improved building and fire codes have had over centuries of construction, and what type of business activity is most frequently correlated with dangerous fires.”

MODA worked with FDNY to see if data could be used to strengthen fire fighters’ natural intuition. By analysing data from past fires, they were able to create a much more accurate prediction model.

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The results are highlighted below. On the left is a map showing the results of the original fire prediction model. The map in the centre shows the predicted location of fires according to MODA’s analysis. On the far right is where past fires had actually occurred. The contrast is striking. Whereas the old model failed to identify high-risk zones in areas such as Harlem, Downtown Manhattan and the Rockaways, the new model very closely reflected reality.

Location of fires as predicted before and after the use of MODA’s model. Source: NYC Mayor’s Office of Data Analytics, Annual Report, 2013

Prior to applying MODA’s analysis, the first 25 per cent of FDNY inspections typically resulted in 21 per cent of the most dangerous buildings being discovered. Using their prediction algorithm, the first 25 per cent of inspections now result in more than 70 per cent being discovered. The use of data has dramatically reduced the number of days that New Yorkers are at serious risk.

A MODA for London?

Like New York, London has numerous public sector organisations operating across the capital, not to mention the 32 boroughs and the City of London. Each is guardian of its own data: in very few cases is this information joined up and acted upon. Remarkably, even City Hall does not systematically collect data from London boroughs, except for that required for statutory purposes, such as population and school place statistics.

If London is to meet the needs of its 8.6m residents, London cannot continue to act as 33 separate islands. Instead, the city needs its own MODA team, led by a chief analytics officer reporting directly to the mayor.

Today’s report outlines how, by combining and analysing data from different public sector organisations (and indeed private sector firms such as mobile phone operators), a London MODA could tackle diverse problems. Dealing with “beds in sheds” (that is, illegally converted outbuildings); improving food safety inspections; identifying empty homes; helping new businesses decide where to set up shop, and fighting tax and benefits fraud. The list of potential applications is essentially limitless.

The fact is that all cities are flooded with data – but by itself, data is of little value. To have an impact, it needs to be joined up. It requires people with the time, skills and resources to interpret it and act upon it.

Currently, few of those things are in place in the capital. If London is serious about becoming a smart city, before it rushes to add new technology that would give it even more data, it must first make sure it has the ability to use what it already has.

Eddie Copeland is the head of technology policy at Policy Exchange. He tweets as @EddieACopeland.

His full report, “Big Data in the Big Apple”, is available here.

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