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Peeling back the layers of the smarter city

James Kobielus | Dec. 8, 2014
What's the key to making cities smarter? Extend data-driven analytic infrastructure across every aspect of urban existence.

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.

 

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