Part of managing a town or city is about measurement: using data from various places to tell whether we are having a positive influence or not. But that variety is also a problem: many disparate sources, all telling us different things. What if we could join them all together into a single, detailed model – a virtual and complete copy of our place – that we could measure, forecast and experiment with?
When Sven Latham first launched Noggin, it was in response to a local city gathering data but doing very little with it. They wanted to know what they could do with all the footfall data they had & knew it had value but didn’t have the resources to turn that value into a positive. Pretty soon after, it became clear that one monitoring device (footfall camera) wasn’t enough and, with budgets constrained, Sven developed a series of cheap sensors to help them see more of the city; to build a bigger picture.
This still wasn’t enough. Anybody running high street events will know huge footfall doesn’t necessarily relate to spending at the till, or if it does, it depends on the event itself. So, while we could see where the visitors were, there was a whole heap of unanswered questions about what attracted them, where they came from and how to encourage spending while they were here.
Meanwhile, technology – particularly for the high street – has progressed enormously over recent years, as has the availability of data. We now have public wi-fi with detailed visitor metrics; loyalty card schemes gathering enormous amounts of data; charge card systems that open up spend data in local shops; mobile data aggregators that show how people move in and out of towns. These are just a few of the options. Traditional stalwarts of high street data are evolving as well, whilst their data provides important national trends and commentary.
Other data sources are becoming ever more available: councils regularly collect data on traffic; many car parks broadcast their live space counters. Environment sensors document our local environment … and with smart cities edging ever closer, that data is expected to grow significantly.
But what’s the outcome?
It’s one thing to have all this data. It’s an entirely separate thing to use it; to understand and communicate it.
Here at Noggin, we firmly believe that the next step in this journey is in modelling. Essentially, creating a virtual copy of our towns in a simulation, and using all of this data to perfect the model. With that, we open up the means to interrogate the data more effectively, in ways that aren’t limited by physical capability.
Imagine clicking on a map of your town at any point, and seeing all the footfall for that location, even if there isn’t actually a camera there.
Then imagine seeing a profile of all those people. What their interests are. Why they’re in town. How much they could spend. How they feel about the look & feel of the area.
Think about the value for commercial property agents – the perfect location finder for a new coffee shop or hairdressing salon. For the existing retailers, to see how they might target advertising and when best to open. Or even better: when best to close up for a much needed break.
But even this is only the start of the story…
People don’t tend to throw darts at a map to figure out where to shop or have lunch. Much is based on experience, marketing and need. So, now imagine how we might start by understanding why people choose your town. How they heard about it, and whether your social media campaign might’ve influenced their decisions.
And when they come in, do they take the car or a train? Do they use the park & ride or prefer to spend more on inner-city parking? What is the environmental consequence – per person – of a parking space in the centre of town?
And, after a (hopefully) nice day out, they return home. What did they think? Will they come back or recommend to friends?
Nowadays, all of this is in some degree measurable. We can already collect data on much of this, but how can we possibly see the entire story?
The ability to create a simulation of a town allows us to use the data we collect to improve it; constantly make it better and more accurate. So, a model could potentially tell us not just how many people there were, but all the factors and consequences of their visit (positive or negative), at any point in time and at any location.
The primary trade-off is accuracy vs cost.
Using just a few cheap data sources will give relatively poor accuracy. Buying the biggest and the best will cost a small fortune, but accuracy will be much better. We suspect many will settle for the middle ground.
Importantly, these sources can be substituted. Footfall can be collected by wi-fi systems – it’s just a little different. A cheap camera will be less accurate than a more expensive one, but it’ll still give you something of use. The individual providers you choose will again be a trade-off between cost and accuracy (as well as other value-add considerations), but they can all contribute to the model as a whole.
Once you’ve built a model, you can start to forecast. Now we look into the future, based on the experience of the past and the advice of analysts. What will next Christmas look like?
With a forecast model, we can also have a what-if model. What if it rains for the Christmas Lights Switch-on this year? What if you close the High Street for urgent roadworks in January versus April? What if your big brand anchor store closes? What if Brexit kills off foreign tourism to the UK, or encourages it?
It’s not such a new thing
Some of this might sound recognisable, and that’s because many parts of it already exist. Highways engineers have long used forecasting models to work out traffic levels in 2025, for instance. It’s also how we talk about the business case for HS2 or a third Heathrow runway. These are based on a similar philosophy: collect data, add experienced opinion, ask the model.
And this sort of work is already happening in cities – to a certain extent. There’s an annual exhibition in Olympia to showcase companies building models for all kinds of pedestrian movements. One UK company is building a fascinating platform – originally intended for video games – but re-purposed to create virtual cities.
But we need to go further…
Not just understanding people in town, but why and how: the implications of town centres extend far beyond a visit. They influence where we choose to live, work and relax.
As Sven adds, “This is my goal: to better understand towns, cities and all those that interact with them. It’s stupidly ambitious – I’m merely putting a flag in the ground and don’t expect to achieve it all myself! But everything I do now is influenced by this master plan, and I want to work with others who share these sorts of ideas.”
So, please, let us know your thoughts; findings; opinions. Are there any towns or cities elsewhere in the world already attempting this?
First presented at British BIDs, November 2017. Image credit: en.reset.org