When disaster strikes, speed is of the essence when it comes to moving people to safe locations, for example, or ensuring emergency vehicles are directed to the scene as efficiently as possible.
Real-time data is crucial in such scenarios. But combining it with a historical 'library' of data takes things to a new level and enables lessons to be learned from previous incidents.
We've all now become familiar with the term 'big data' but – as the above example highlights – the real value of data becomes clear when it's linked back to people. And that's particularly true when we look at spatial big data.
Adding in a geographical element gives a new dimension to data, quite literally, and makes it possible to harness more information than ever before to improve the lives of individuals and wider population groups.
The smart city is an example of this – it's not really about how 'smart' a city is, it's about making people's lives better in densely populated urban areas.
Our state-of-the-art spatial big data platform gives city planners analysis and visualisations of traffic movements, commuter routes, bottlenecks and so on – which can then be used to make a real difference to the people who live or work in the city every day.
Transport can be made more efficient – if a train breaks down and disrupts services, taxis and buses can be rerouted to pick up stranded passengers. And those passengers can be grouped together according to their destinations, using information from their travelcards to work out where they're normally heading at 8am on a Tuesday.
Data from pollution sensors, street-light sensors, waste sensors and so on can inform strategic decisions at a city level to improve life for local residents. The powerful advantage of adding the geospatial element to this information is that it provides a way to correlate completely disparate data sets. It gives a sense of place to allow much greater exploration of all kinds of data.
A data set of national energy use throughout the year, for example, would reveal peaks and troughs over the months. Combining that with meteorological data or information about national events taking place would enable National Grid operators to see historical links and effects on usage – and potentially use that information to predict future usage fluctuations. Looking ahead using weather simulations or event schedules could inform planning for how to spread the load across the grid to keep it stable as much of the time as possible.
But all these examples are not the end of the story – they are just the beginning. When it comes to spatial big data, the world really is your oyster.