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.
Sign up for CIO Asia eNewsletters.