Slow Data in the Age of the Smart City

Cities stand to benefit from ever-increasing technological advances. Digital information is helping solve our most pressing urban challenges. Yet the rising level of data we are now capable of generating can obscure the original intention and purpose of this work if we don’t stay mindful of the social dynamics at play by engaging with the people that are meant to benefit from it.

Smart and connected technologies embedded across city infrastructures can help monitor, anticipate and manage urban issues in new and effective ways. From spotting economic trends and improving health to combating crime and optimizing traffic flows, intelligent infrastructure has the potential to help us make more informed decisions for solving some of the greatest issues that cities face.

Much of the optimism surrounding intelligent infrastructure, however, relies on concepts that can be easily misunderstood or overhyped, particularly those related to smart buildings and smart cities such as the Internet of Things (IoT), artificial intelligence (AI), and Big Data. “Smart” should be understood not as something that you simply install as an add-on; rather, it is an enabler of larger outcomes, something that requires human intervention and implementation. To really get the most out of these technologies, in other words, we first need to take a step back — and maybe even slow down.

Take Big Data, for example. Broadly defined, Big Data is the use of large and often complex data sets for predictive or analytical purposes. It relies on machine learning and isolates variables to identify patterns that would otherwise be too complex to see. It helps to make the invisible visible. Big Data gains insight from the scale and complexity of data points, but lacks resolution and depth.

On the other hand, there is the concept of “Slow Data”. It’s an idea inspired by Slow Food, the global movement which grew in response to the growth of the fast food industry. Proponents of Slow Data — or Thick Data, as it’s sometimes called — believe that in our rush to expand data analytics into every aspect of our lives, we’ve lost sight of the reason for collecting data in the first place.

Slow Data differs from Big Data in that it uses qualitative information to provide a deeper understanding of the conditions under which the data has been captured. It relies on human learning and reveals the social context of connections between the data. What Slow Data loses in scale and complexity it gains in depth and insight.

The trade-off between number of data points and depth of insights
[Adapted from Ethnography Matters]

Smart city technologies – Big Data in particular – can be extremely helpful and powerful tools in helping to overcome urban problems. But many of these issues can only be solved by making better use of our brains, not by collecting more data. It can be used to mobilize people and prompt change, but only when it becomes embedded within a wider social context. Simply put, for intelligent infrastructure to be successful, we also need old-fashioned community engagement.

The Plaça del Sol neighbourhood in Barcelona offers a compelling example of how intelligent infrastructure and community engagement can be used together to make meaningful change. Barcelona has long been a pioneer in smart cities, with an urban digital policy that promotes “technological sovereignty” and opens digital platforms to greater scrutiny, participation and engagement amongst its citizens.

Residents of Plaça del Sol had for years complained about the increasing levels of noise in and around the public square. But it wasn’t until 2017, when they started to participate in a community-led environmental monitoring project called Making Sense, that conditions started to improve. Residents attended meetings and workshops and were provided with sensors to monitor noise levels throughout the day. This allowed them to compare their experience with officially permissible noise levels, refer to scientific studies about the related health impacts, and correlate their measurements to different activities in the square throughout the day.

This process provided them with the information they needed to engage with the local council to find solutions to reduce noise: different materials to dampen sound were explored; gardens were put in place of the steps where people would loiter late at night; signage reminding people to respect noise were put in hotspot areas; a movable children’s playground is being explored. Residents will be able to monitor how these interventions improve quality of life over time.

It was by rendering their day-to-day experience into data — and vice versa — that local citizens could come to a workable solution. Or, as one person described it, “collective data gathering proved more potent than decibel levels alone.”

Other tools and frameworks are starting to emerge that combine information from smart and connected infrastructure with community engagement – Slow Data – to solve urban challenges. There’s the OrganiCity Playbook which uses the “experimentation as a service” model to provide citizens, small businesses, large corporations and city authorities with the resources necessary to test new ideas with urban data at a small scale. Infrastructure Canada’s Smart Cities Challenge focuses heavily on citizen engagement and non-traditional partnerships in order to enable truly connected communities. And Boston’s Smart City Playbook aims to “create a City-wide strategy for the use of sensor technologies that is people-centered, problem-driven, and responsible.”

What does this all demonstrate? That in our pursuit of more digitally-connected communities and smarter cities, we need to involve the very people that these technologies are meant to help in the first place. An abundance of new insights can be gained from the deployment of sensors, apps, and devices that form part of this intelligent infrastructure. But to realize its full potential, a combination of both Big Data and Slow Data is critical. Infrastructure can only be called intelligent if it is designed for, and with, the people it serves.


Originally posted on WSP Insights