How Far Away Are We From Data-Driven Smart Cities?

There is a clamour for them, but how near are they?


Metropolitan areas around the world are looking for new ways to be ‘smart’. They are investing heavily in technology that makes them more sustainable, improves the efficiency of public services, and enhances their citizens’ quality of life. It is estimated that by 2020, governments across the world will be spending in excess of $400bn a year building smart cities, incorporating the Internet of Things and Big Data on an unprecedented scale. The latest report from Navigant Research forecasts that annual smart city technology investment will amount to $27.5 billion by 2023 in the US alone, and a total cumulative investment over the next decade of nearly $175 billion.

Big Data is already being used to develop systems for waste management agencies, public transportation, law enforcement, and energy use. Sensors are being installed city wide to monitor all aspects of public life. In waste management, for example, garbage trucks are set be alerted to the location of refuse that needs collecting. Such methods allow for resources to be directed to where they are needed, cutting back the wastage of going to points where they are not needed simply to check.

Sustainability is one of the key areas that Big Data will have a tremendous impact, particularly in helping to improve energy efficiency. Sensors attached to street lights and other outside urban furniture will measure footfall, noise levels and air pollution so that things can only be used when necessary, and so that strategies can be put in place to keep them at an acceptable level.

The city of Seattle recently announced that it was joining forces with Microsoft and Accenture to reduce the area's energy usage by 25%. The project will collect and analyze hundreds of data sets collected from four downtown buildings' management systems and store them on Microsoft's Azure cloud. Predictive analytics will then be used to discover what's working and what's not — i.e. where energy can be used less, or not at all.

There are also numerous implications for public transport in a city, which could be of huge benefit to the environment and convenience. Sensors in our cars will direct us towards available parking spaces. Ben Wellington’s popular TED Talk went into detail about how much Big Data could tell us about city transportation. He looked at average taxi trip speed to discover that they peak at 5.18am, at 24mph, then they decline until 8.35am when they level out for the entire day. Such discoveries may seem trivial, but they have the capability to revolutionize how city transportation is managed if leveraged by municipalities correctly.

One example of a city using such measures is Minneapolis. They are now using IBM’s Smarter Cities analytics solution for a growing list of functions, and is even looking to target landlords who violate city codes by pulling data from multiple, formerly unlinked databases, and looking at that data in conjunction with citizen complaint reports.

This will not happen overnight. Many have valid concerns about the ability of hackers to get into the technology and wreak havoc. There is also privacy issues around the collection of data from some of the most intimate aspects of people’s lives. Even with the massive investment expected, there is still much that needs to change in order for it to work. Installing sensors across an existing city is terrifically time consuming and expensive, and an element of selectivity is required. In a new city in China, it makes sense to build from the ground and add sensors to everything as they go. Big Data is excellent at finding correlations, but it still needs humans to establish causation, and applying analytics on such a scale required to find the number of problems in a city, with a huge, complex population and billions of moving parts, could take years more. We are, however, on our way there.


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