From Smart to Savvy: The Transition to Cognitive Cities (Part 3 of 3)

January 23, 2018


In our previous post, How a smart city can benefit both consumers and citizens, we pointed out that residents in a smart city are not only consumers of technology-driven services, but also citizens with various legal protections (such as privacy), which can make the transition from safe city to smart city more challenging than similar revolutions in the private sector.
In this third and final installment in our series on the transition to cognitive cities, we’ll explore how cognitive cities can deliver the constant citizen interaction and engagement which bolsters the people’s legal power and equips them with the real-time influence normally wielded by consumers.

What’s a Cognitive City?

Before we delve too deeply into just how this can happen in cognitive cities, we first need to ground our discussion in a more solid definition of what a cognitive city really is. Wikipedia’s article on the subject provides a good starting point by defining a cognitive city as one which learns through the constant engagement of its citizens and advanced technologies. Together, these two features enable the cognitive city to become more efficient and resilient by enabling information exchange within the city.
Safe cities protect their residents. Smart cities collect data from their residents and technologies in order improve safety as well as the efficiency of public services. Cognitive cities can effectively use that data for improving the health, safety, well-being, and prosperity of its citizens, because the whole city is involved in the gathering, sharing, and use of the data. In cognitive cities, data now flows not only from the citizens to and from city management (as in smart cities), but also from citizen to citizen, and citizen to system. Not only does the whole city generate and consume information, but the whole city learns continuously and adapts as the city learns from it.
The cognition in a cognitive city happens not only in an administrative office or in a control center, but in and across the complex web of systems which comprise an urban area: social networks, local industries, transportation networks, utility systems, communications infrastructure and services, non-profit organizations, and political parties and movements. In a truly cognitive city, all members in this complex ecosystem are able to not only disperse data but also store memories, like how your brain records memories by connecting individual synapses.

Photo by Saketh Garuda on Unsplash
Explaining urban life via neuroscience may seem a bit esoteric. Thankfully, this scholarly paper succinctly explains the difference between smart cities and cognitive ones, “In more concrete terms, in the smart city, individual citizens predominantly receive information on urban infrastructures such as traffic conditions or service outages; in the cognitive city, they also deliver information to others (e.g., other devices and sensors, machines, operating platforms, humans) to allow these systems to learn from and adapt their behavior.”

With that adaptability comes the resiliency necessary to take on the list of the current urban challenges we cataloged in the first article of this series – rapid growth, limited space and resources, crime prevention – while at the same time surviving and managing rarer crises like earthquakes, floods, and other disasters.
So how do we build this cognitive city of the future?

Interaction and Analytics

The first step is to fully realize the potential of a smart city because cognitive cities build on their foundation. In practical terms, this includes constant citizen interaction via multiple touchpoints: in-person at government offices, over the phone with city personnel, and online through social media, city government websites, online chat, email, and mobile apps. Of those, perhaps social media is the technology which has the best potential to quickly foster the harvesting of information from citizens and the citizen to citizen interaction we mentioned above, and which is an indispensable part of a cognitive city. In addition, the rapidity of social media communications naturally lends itself to crowdsourced feedback.
Once that feedback comes in through social, other tech can step in. Predictive analytics, natural language processing, and cloud computing can not only help analyze citizen sentiment, cross reference citizens data with systems and sensors but to also proactively respond to behavioral patterns and deviations in them. For example, if there’s a sudden surge of tweets which favorably mention the hashtag of the local hockey team by city residents, the city administration may want to allocate more police to directing traffic on the streets around the rink during the next home game, in order to accommodate the throngs of fans who’ve come out to see their team. That city could then also put out advance warnings of traffic restrictions special parking changes on Twitter, tagging those tweets with the same hashtag, thus contributing relevant and useful information back to those same fans.
In that simple example, we see the essence of the smart city: seamlessly knitting together communication technology (Twitter) with how city personnel (the police) are deployed to make existing infrastructure (the roads around the rink) function more efficiently.

Photo by Andre Benz on Unsplash
In the less simple scenarios of the real world, there will be multiple data streams, many different data formats (e.g. unstructured text, video, databases), and high volumes of data. Thus, the analytics tools smart cities will have to rely on will need to be robust, scalable, and flexible.

Once those tools are in place, the cognitive city can emerge. Cognitive cities learn from the data over time, and that learning will require more sophisticated tools for finding correlations between different data streams, trends within each of them, and instantly detecting anomalies in them and in the complex city system. Machine learning and the cloud will be key enablers in this transition from smart to cognitive.
A cognitive city would be able to learn from our above example, and generalize from the individual data points to valuable lessons which can then be used to make predictions of road traffic conditions and the number of police personnel required for any major sporting event, in any neighborhood, given historical data and real-time social media trending. This fundamentally shifts all urban operations away from situation management and towards real-time adaptation.
In the first post in this series, we saw how advanced technology alone is unable to make a city safe or smart. In our second installment, we discussed why:  city residents are more than consumers, they’re citizens, and thus tech must incorporate and augment existing legal and civic mechanisms. In this final post, we demonstrated how cognitive cities use extensive data analytics to make the city smarter over time, and one of the ways this intelligence shows is in predictive, proactive adaptations (as opposed to reactive, rushed crisis management).
Even though smart cities are still maturing, cognitive cities will be the future of efficient, connected urban areas which employ technology and human intelligence to foster community, innovation, and prosperity. Both elements are necessary because tech alone can’t make a city safe, nor are city residents mere consumers of goods and services. Constant citizen engagement, ubiquitous data collection, and sophisticated analytics can combine to produce the best kind of cognitive city: the kind someone would actually want to live in.