We can use open data to make smart cities “searchable”

As we all rush to surf upon the sweeping tide of smarter cities, it pays to just pause and think – what exactly is a smart city? Or to put it another way, what is it that makes a city “not so smart” to start with?

As urbanisation has been growing, distances have also been increasing between people. Communication systems have found ways to link people who are geographically distant; but they still have to catch up with the problem of how exactly to recreate the relationship between people and the environment, at a time when both are constantly changing.

That particular aspect is pretty stable in rural or close-knit environments, mainly due to the availability of information about one’s surrounding at all times. But metropolitan areas lack that – and that’s what I believe is what makes them “not so smart”. So the problem to solve here is one of providing, or rather exchanging, information. That is the crux of the idea of “smart cities”, especially in the digital domain.

Most of this information is already available, published or unpublished. But it’s usually holed up in silos, unavailable to the people when they might require it most. The recent push by many public organizations to publish this information as Open Data has helped, a bit; the next step, if it’s to be useful, is to make it available when it is actually required.

Unfortunately, a major chunk of this published open data is in archaic formats, or formats which are extremely difficult to use online: data published in PDF, for example, or in formats only readable by propriety software. This data serves well for one time research or study purposes, but is absolutely useless when a system needs a steady flow of information.

Open Data comes in all shapes and sizes, but, in context of smarter cities, probably the most relevant dimensions are space and time. That’s why I founded Noustix, a system which collects and stores spatio-temporal data from multiple open sources, and turns them into a single stream for third parties. In this way, we can combine multiple data sets to give fresh insights. Road networks data can be combined with mobile phone use data to show traffic congestion, for example. Or you can combine traffic accidents data with demographic data to observe patterns in areas with varying population and resource densities.

But the most interesting applications come when this open data is further combined with crowd sources data for a whole new set of applications. Geo-tagged tweets, for example, can be used to see the patterns of conversation at any place, or any time. There is an argument for using the tweets people send when, say, they are frustrated at traffic jams, to identify the congestion hot spots in a city.

This can be done by semantic and text analysis, but a much more accurate way of doing it is asking people to use particular hashtags for specific purposes: when people are stuck in traffic, alongside the usual string of frustrated rants and obscenities, they can maybe add an unimaginative tag #stuckinTraffic.

Over a period of weeks or months, when this particular tag is evaluated, it will reveal the traffic congestion patterns in city. Comparing and combining this with published open data of traffic and road networks will give you a very precise picture of troubled areas. Moreover, by using social data, apps can stream in data live and see congestion hot spots in real time. You can even throw news feeds from the media into the mix, too.

Combining open data with crowd sourcing is a powerful way to organise, sort, filter and analyse data, in a way that makes it more useful. The “crowd” part actually helps to validate the authenticity and relevancy of the data, in a way no individual or organization could.

As an analogy, consider comparing cities with the web. Until search engines came along, the web was all about making and keeping extensive lists and bookmarks of useful websites or forums.  But search engines, especially those which use human input to improve results, changed all that. Suddenly, all that you needed to find relevant information was a query.

That’s our aim for smart cities as well. A city is currently browse-able; we need to make it searchable. And social and crowd sourced data combined with open data can do that.

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