London should establish its own office for data analytics, recommends a report that takes New York's experience of using data to improve services and asks how we could do the same.
Eddie Copeland, the report's author, recommends the creation of a Mayor's Office for Data Analytics here in London. Our capital is obviously more complex than New York City, having 33 rather than five boroughs, and our boroughs are used to a greater level of independence. Our mayor also doesn't have the power that New York's does. But all the boroughs hold vast amounts of information that could make all of our lives safer and better if it was properly shared and collated — after all, problems don't respect borough boundaries.
By better using data, we could map sewage outflow (no, really) and analyse pest infestations to pinpoint likely areas of illegal housing. We could identify more empty homes and levy 'empty homes charges' on them. We could better target food safety inspections. As Copeland writes in CityMetric:
"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."
One prime example of how better data analysis could benefit us is NYC's fire prevention model. Inspectors from New York's Fire Department used to predict fire risk by focus grouping with firefighters about potential risks and the criteria for dangerous buildings. But, after starting to feed data from actual fires into its risk model, FDNY discovered it had been underestimating the risk in areas like Harlem and downtown Manhattan. This graphic shows a stark demonstration of the accuracy of the new model:
By being able to more accurately predict where fires are likely to happen, the fire department was able to better target its prevention visits and prioritise complaints. The first 25% of visits used to reveal 21% of the most severe violations; using the new model, the first 25% of inspections revealed over 70% of the most severe violations and so created the opportunity for better action on fire prevention earlier. Smartly put: better data saves lives.
