Home » smart cities

Category: smart cities

“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.

Public sector CIOs have a tough job

Public sector CIOs have a tough job / San Jose’s winding road toward “smart” / Bill Schmarzo

It’s hard to get things done in the public sector, says the data industry’s “Dean of Big Data,” Bill Schmarzo. That’s one big lesson he’s learned in his first six months on a Silicon Valley tech advisory board. What do you do when your city wants to be “smart” but your budget doesn’t add up?

Smart strategy

San Jose, California is on the long road to becoming a “smart city.” The city is already pretty smart in the old sense of “smart.” For one thing, it’s got within it a bounty of tech-industry pros, such as Schmarzo. Early this year, the city recruited him to serve on its seven-member Innovation and Technology Advisory Board. San Jose also got some of the best educational institutions in the world within a few minutes’ drive. The San Jose State University computer engineering department, for example, is a seven minute walk from the San Jose city hall.

But getting to the new kind of smart, where sensors inform decisions, from the mundane to the sweeping, is tough. “You don’t have the kind of budget you need for these projects,” says Schmarzo.

He comes from the private sector. There, he explains, you can usually get money for worthy projects much more easily. He’s spent 36 years in senior-level tech-industry positions and is now CTO of Dell EMC Services.

The creative CIO

In the public-sector, the CIO has to be creative. Where a private sector CIO just goes out and hires a team, the public sector CIO has to think hard how a paltry budget will meet obligations. One popular strategy among U.S. cities is to enlist a computer science department at a nearby college. Many up-and-coming data scientists like to dive into public-sector projects.

That hasn’t always worked out so well, Schmarzo says. The work tends to be undirected, and worse, those clever students move on. Public sector CIOs realize quickly that it’s not data science they need first, it’s project management and a collaborative framework. Only with that can one group of students hand off their work to the next.

One especially creative public sector CIO, says Schmarzo, is San Jose’s, Rob Lloyd. As tight as Lloyd’s budget started out in 2014, his progress on an open-data platform soon faced an extraordinary challenge when a major flood hit the city. The crisis was expensive and not budgeted. Every one of the city’s departments had to contribute much of their budgets.

Datadoodle hopes to hear much more about that from Lloyd.

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.”

Clues

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.

A horse race in town: Chief Data Officer vs Chief Reliance Officer

Smart City, the movement, has a horse race underway. At stake: Who will sit closer to the mayor or city manager?

Chief Data Officer, the darling of the data crowd, was out of the gate first. Four lengths behind and gaining, though, is a dark horse. It’s Chief Resilience Officer, favorite of the humanists. He’s breathing hard and coming up fast. Whoever wins will subsume the other.

If Chief Data Officer wins, the city’s chief executive will feel the sway of CDO’s data-driven whispers. All other things being equal, decisions will rely on data analysis.

But if Chief Resilience Officer pulls off a surprise win, the chief executive will hear her slightly more humanist whispers. Data will be a factor, but so will empathy. Here’s how the group 100 Resilient Cities begins to explain resilience:

[Resilience is] the capacity of individuals, communities, institutions, businesses, and systems within a city to survive, adapt, and grow no matter what kinds of chronic stresses and acute shocks they experience.

I don’t see how that’s done without a big dose of data analysis. What modern process goes without it? The difference is scope: tending toward cold and narrow or toward broad and warm?

Each horse has a cheering section in the stands. Naturally, the data people — data analysts, data stewards, “data scientists,” data people of all kinds — root for the chief data officer. “Run, CDO, run!” To many of them, data is not merely a reflection of the world, it is the world itself.

Nearby them is a small crowd cheering for the chief resilience officer. “Run, CRO, run!” Data’s good, they agree, but there’s more to running a city or living in it.

Within business, the CDO, the data guy, would be the obvious winner. Data is tangible. It’s new and shiny, and it’s got that science-and-research allure of cold certainty.

But this is a city. Warmth and comfort matters, not just “truth” and cold facts. People — the voting populace — have lives to live there in that city.

Which crowd do I stand with? I’m always for the dark horse.

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.