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“Smart” starts with context

Imagine two marketing people sitting across a table from a data analyst. Marketing wants “simple numbers,” they say, and the analyst understands. The three of them know all the past conversations, the questions that arose and were answered, the points that got made, the conclusions or unresolved questions that remained.

Easy enough. But now with the rise of so-called “smart cities,” where the data spigots open and everyone with any point of view at all takes part, things can get into a mess. There, the starkly disparate points of view, which in business get talked through in relative calm, really show teeth.

Imagine the scene: From the city might come staff analysts, department heads, even elected officials. From the public come experts from interest-groups, grassroots politicians eager to make a name for themselves, owners of mom’n’pop stores, or even the even the grandmother who’s mad as hell. Each side — there may be several sides, and even divisions within each one — may have never set eyes on the other before, though some may know each other from decades of work together. Everyone’s got a different idea of past conversations, or no idea at all.

If only they had a nice, simple tool — as simple as a book, notepad, or whiteboard but also available for fast searches before meetings or quick refreshers on a tablet held just out of view from everyone else. Something as simple as — to use a metaphor by Alation vice president of marketing Stephanie McReynolds — a bicycle. You could walk across town, but it’s a lot easier on a bike. It has no hidden parts. Like any good machine, it makes more of whatever effort you exert.

Past conversations — rich with context, observations, and ideas — can be preserved and studied. Then those who like “simple numbers” might really talk.

“Smart” starts with context. Real decisions, no matter what data anyone has, still takes place among people — every one of whom has to know the context of any decision they’re part of.

A fun, lively path to smarter cities

San Francisco-area designer Steve Pepple wondered whether data could help him explore neighborhoods — on the way to helping city planners build better, perhaps “smarter” cities.

He describes in a fun post on Medium, a genuine “data story,” how he set out to help people find “pockets of activity, vibrance, and new things.”

First, he joined a do-it-yourself project called Sense Your City. Sensors around the world reaped “hyper-local data about environmental conditions, such as noise, dust, light, and pollutants.” That data led to “pockets of activity, vibrance, and new things.” In San Francisco, he writes, “you could see when the fog rolls in.”

That sounds like fun, but could he find a key insights about mobility, housing, or neighborhood change? He had frustrating moments. “I had a bunch of data to work with,” he writes. His many sources included DataSF, San Francisco Planning, SPUR, and the City of Oakland. But he didn’t know what he was doing. Perhaps worse, “I was stymied by trying to find a significant, cumulative insight by cobbling together and analyzing all the data.”

He had been inspired by one man’s discovery who famously found such an insight in San Francisco rental data. Eric Fischer had pored over newspaper ads back to 1948 and surprised urbanists with his conclusion: the usual levers for regulating demand wouldn’t solve the city’s housing crisis.

Smaller experiments

With no big discoveries in sight, Pepple turned to shorter experiments. Apparently as part of a fellowship with Stamen Design and Gray Area Foundation for the Arts, he looked at Instagram photos to track street movement, such as some taken during the 2015 Pride Parade. Data from those photos showed events like parade formation and, in the afternoon, a drift into block parties.

His Medium post includes a short series of visualizations that are more fun and vibrant than you might think any “smart city” has a right to be. With “color quantization,” he created a “spatial database of color” that showed parks and murals in the Mission District. In another map, colors expressed “density of people and establishments,” with brighter and more colorful buildings showing the most active.

Tangible, street-level meaning came in large, street-level screens…that showed passersby how they could “interact with data about their neighborhood and arrive at their own discoveries.”


It was his work with the urban designers and architects Perkins + Will that his approach had what I find the most interesting and possibly the most meaningful clue. Pepple and team could see and hear neighborhoods with the most activity. Could that be found with data? Indeed, data tracked activity and what people said about the places where it occurred; social media helped characterize those places; mapping tools connected hotspots.

A look at the maps Pepple and team produced shows that transit, living space, restaurants, and other amenities fail to anchor social hotspots. Fine, but why? If the data can reveal this, perhaps it can go the rest of the way.

Keep your eye on Pepple. His playful and often fun analysis puts a bright, even inspiring face on urban data.

Thanks to Stephanie Langenfeld McReynolds, vice president of marketing at Alation, for alerting me to Pepple’s work.

improved urban decision making

…the smart city movement is less about technology and more about improving the way decisions are made in large urban areas, where the demand for services is increasing and the availability of resources is diminishing.”

Michael Flowers, as interpreted by Mike Barlow in a 2015 O’Reilly report, Smart Cities, Smarter Citizens. Flowers is now chief analytics officer at Enigma.io.

Dumbstruck city: big, brittle, not so smart

A city that seems smart one moment can look like the dumbest thing alive the next moment. All it takes is one little cloud outage.

We’ve taken cloud outages in stride. After all, how often does it really matter if you place an Amazon order now or in an hour or two? But what if the cloud locks or unlocks your front door?

Fainting ladies

I’ve been reading up on “smart cities.” Anthony Townsend, in his article “Smart Cities: Buggy and Brittle” — from 2013 and still fresh — says yes, worry. Start with the cellular networks.

Cellular networks … are the fainting ladies of the network world — when the heat is on, they’re the first to go down and make the biggest fuss as they do so.

All networks are vulnerable, and outages have ever higher stakes.

Cloud-computing outages could turn smart cities into zombies. Biometric authentication, for instance, which senses our unique physical characteristics to identify individuals, will increasingly determine our rights and privileges as we move through the city — granting physical access to buildings and rooms, personalizing environments, and enabling digital services and content.

But biometric authentication is a complex task that will demand access to remote data and computation. The keyless entry system at your office might send a scan of your retina to a remote data center to match against your personnel record before admitting you. Continuous authentication, a technique that uses always-on biometrics — your appearance, gestures, or typing style — will constantly verify your identity, potentially eliminating the need for passwords. Such systems will rely heavily on cloud computing, and will break down when it does.

Why “smart cities” is interesting, first look

At first, the term “smart cities” may sound like just another bit of fluff — another one of the data industry’s fascinations. It did to me. But I’ve been reading about it, and I’ve come to think that the reality may hold real benefits for the data industry — more than just a new market for bright, shiny products.

Definitions vary, of course, but the term seems to boil down to this: It’s the use of data from the Internet of Things and other sources to squeeze more use, more security, and even more pleasure from city facilities, utilities, roads, transit, and other public resources. City administrators can spend less, for example, to make data guide drivers around congestion than to build a new lane.

You might say, well, that’s just the old dream: data analysis fixes everything! Yeah, we’ve heard that one before. But I think this is different when you try this in cities instead of businesses.

Cities and businesses both know how to hide things. But they’re different. Business executives can pretend everything’s humming along like air conditioning blowing cool air on cool heads. City officials, meanwhile, fear the hot heads, the activists, the ever growing and ever-smarter legions of data crunchers.

In cities, smell and dysfunction is in everyone’s face and nose. If it’s impossible to drive across San Francisco at four on a weekday afternoon, you know it— and people learn to assume there’s data on it somewhere. They look in the data mirror to examine it. Is it as bad as it felt? How long did it last? What caused it, and what’s being done about it? The data mirror becomes part of life. You just step in it.

That’s a fine dream, of course. But users of restaurant ratings and other attempts at quasi-public data know that such visions don’t always come true. Who hasn’t relied on public raves for restaurants or movies to find they were fooled?

Even so, I suspect that the public nature of smart-city data will give a nudge to common data pathologies. If dysfunction is in your ears and nose, smart community organizers have a strong lever on reluctant officials. Hey guys, break down the silos. Where’s the data you promised? Hey, your data stinks.

Smart cities in full flower can do even more than offer efficiency and safety. They can also make people feel good. That might be the most interesting benefit of all.

Daniele Quercia, for example, offers an “alternate agenda.” He advocates, among other things, letting data point to slightly-less-than-efficient paths between A and B that are more fun, more beautiful, or more interesting. (See his crowdsourced “happy maps” and “smelly maps.”) Who wants to live in bare, cold efficiency? Not successful people, many of whom have a good pick of places to live and work.

Imagine: data that makes you feel good.

I’ve just started looking at smart cities. I’m probably naive about some things. But so far, I think smart cities is worth attention.