Cities seem to have minds of their own, though it may appear otherwise.
Some cities feel mindless, as if no one and nothing is in control. Urban areas have an occasional tendency to become unlivable as issues such as congestion, pollution, crime, and blight spin out of control. They also tend to feel ungovernable as they grow larger, denser, and more complex.
You can't directly administer an intelligence test to an urban conglomeration, so the notion of "mind" is metaphorical -- although the best-run cities manage themselves as cohesive entities, as if by a single mind down to the tiniest detail. At the most basic level, this mind is the collective will of the local population, as interpreted and executed by its public-sector institutions.
Another layer of this collective mind is expressed in the operations of its business sector, civic organizations, and other institutions. Yet another layer is in the procedures, practices, and routines of the infrastructure upon which the city runs.
One might say that a "mindful city" is any community in which all of these levels mesh like clockwork. The happy result is when city services are delivered nonstop, predictably, effectively, efficiently, and equitably. Commonly, we speak of this ideal as the "smart city."
This latter term usually implies that there is an essential layer of IT infrastructure enabling the community to hum like an intelligent machine. More than that, it implies this IT infrastructure incorporates a mind-boosting dose of big data analytics.
Indeed, that's essentially how the "smart cities" topic is framed in this recent article. According to author David Barton, "The truth about ‘Smart Cities’ is that there is only going to be one way that they can become truly ‘smart’: through data and analytics." He goes on to sketch out important roles for data analytics in diverse urban-governance functions, including zoning, policing, traffic management, and service delivery. The tools he alludes to include predictive analytics, geospatial analytics, event monitoring, performance analytics, interactive modeling, segmentation analysis, and data-driven alerting.
Essentially, Barton's discussion covers the key data-driven components of what you might call the urban nervous system. According to this perspective, it's through big data, decision automation, and analytic-optimized infrastructure-process feedback loops that the smart city "mind" monitors and optimizes its own operations continuously. In this post from two years ago I refer to this as a vision of "next best action everywhere."
Essentially, Barton presents a top-down view of how government agencies can administer urban services. Consistent with this perspective is this discussion of "smarter cities," which presents three principal levels of data-driven enablement for government service delivery:
- Planning and management: Long-term insights based on comprehensive data analysis, followed up through efficient daily management, help a city stay vital and safe for its citizens and businesses.
- Infrastructure: Fundamental services — such as roadways, mass transit and utilities — make a city desirable and livable, but the key to keeping them viable is readiness for constant change.
- People: Smarter cities use the system of systems to their advantage when supporting the needs of each citizen through social programs, health care, and education.
Perhaps you're agreeing with all of that, but wondering whether it's meaningful to speak of not only "smart" but of "smarter" cities? How can urban governance structures be more mindful – hence smarter – going about their duties?
One way to address this is to emphasize it's always smarter to extend data-driven analytic infrastructure across every aspect of urban existence, including public safety, transportation, roads, communications, utilities, health care, education, social services, economic development, and so on. You should check out this recent article for a discussion of how shared cloud services, spanning diverse government agencies, can drive consistent adoption of "smarter city" technologies across cities and regions.
Another approach is to point to the increasingly layered data analytic services that drive "smarter" urban governance. In the above-cited post on "next best action everywhere," I alluded to one such service-layering model:
The platform components often include big data clusters, decision engines, business rules management systems, and stream computing platforms. Just as fundamental is the need for reusable 'business logic' artifacts – data, metadata, predictive models, business rules, process orchestrations, service definitions, and the like – that developers can access from within integrated toolsets. Teams of next-best-action developers should be able to access these artifacts from a unified component repository, with their interactions shaped by shared governance, collaboration and deployment infrastructure.
Yet another approach is to highlight the value-added layers of "cognitive computing" infrastructure that enable "smarter city" infrastructure.
A "smarter" layer often gets overlooked in these sorts of discussions: crowdsourcing. As I discussed in this two-part post from earlier this year, continuous crowdsourcing of pertinent data can improve quality of life in sprawling mega-cities. It can tap into the collective mind of the community via social, mobile, and Internet of things (IoT) tools. As noted in the post, urban planning visionaries are beginning to envision a future where "Smarter Cities will leverage Sensor Data with the goal of making cities more desirable, livable, sustainable, and green and therefore attract more citizens to live or to play or to holiday or to play."
Smartphone-originated crowdsourced data is a key tool for building a smarter urban fabric. In this post from earlier this year, I report on a study in which European social scientists use mobile phone data to plot the daily density cycles of various urban areas. The researchers use that data to classify cities by their various and shifting density structures. As illustrated in the study, mobile-phone usage densities clearly show how urban area density structures evolve over the course of a day as people commute, shop, entertain themselves, and so forth.
For a more mindful urban fabric, it's necessary to have open data and a cadre of policy analysts who advise all stakeholders on the state of the city. In that regard, check out this recent post in which I discuss the emergence of rich open-data sets on urban quality-of-life, governance, and other metrics. A new type of urban data scientist is emerging whose job is to use big data analysis tools to assist the governments and other organizations who plan, monitor, and improve smarter cities everywhere.
Clearly, cities don’t grow smarter through some natural, organic process. Stakeholders at every level of civic life must engage in the effort on an ongoing basis. Data-driven analytics must be a fundamental open resource that empowers one and all.