Last year I penned a two-part series entitled Numbers in the City, Part 1 and Part 2. Those posts discussed quantitative urbanism and how mathematics can be used to better understand city life and provide insights about how it can be improved. The “smart cities” movement really began when planners understood that connected networks and systems could be analyzed and manipulated in ways previously impossible. Connected systems provide vast amounts of data that help system operators understand what is really going on within the system. There are, however, at uses of data that can help planners understand urban activities better. At the macro-level, Meghan Neal discusses how Big Data analytics can be used to predict the growth of cities. “Urban growth isn’t determined by millions of random decisions by individual citizens,” she writes, “but can be explained by regular correlations of time and space.” [“Math Can Predict the Future of Cities and Urban Sprawl,” Motherboard, 20 June 2013] At least, she reports, that is the conclusion of a “study published on the arXiv preprint server by Cornell University. And researchers are confident they can predict the future of this rapid urbanization with simple math.” She continues:
“Demographers at a Swiss university analyzed a large set of urban data and were able to quantify the social patterns that have made cities ebb and flow over the last century. After parsing data from the Spanish Government’s Institute INE — one of the largest collection of city information, with records from 45 million people living in 8,100 cities from 1900 to 2011 — researchers found city growth can be predicted based on two space-time correlations: the past growth, or inertia, of the city, and the growth of nearby cities. Past growth patterns proved an accurate indicator of future growth for up to 15 years, and then the correlation weakened. So the researchers concluded the inertia of a city has a window of about 15 years. The growth correlation to other cities was a strong indicator of future growth up to 50 miles, at which point influence dropped. That what happened before and what’s happening nearby can give clues about the growth of a city is not exactly a shocker. But what makes the study important is that the researchers developed a mathematical formula that can explain these patterns, and can apply the model to the future in order to make more accurate predictions about the evolution of global cities. Lead researcher Alberto Hernandez wrote that these space-time patterns are ‘an important step toward understanding collective, human dynamics at the macro scale.’”
Although such analysis is probably of general interest to city planners, they are even more interested in what Big Data analytics can tell them about specific urban areas. The Economist reports, “Cities are finding useful ways of handling a torrent of data.” [“By the numbers,” 27 April 2013] The article focuses on Chicago and discusses how Brett Goldstein, Chicago’s chief information officer, plans on using Big Data analytics to inform policymaking. The late Carl Sandburg wrote a famous poem about Chicago in which he described the city this way:
Hog Butcher for the World,Tool Maker, Stacker of Wheat,Player with Railroads and the Nation’s Freight Handler;Stormy, husky, brawling,City of the Big Shoulders
Today, Sandburg might have added City of the Big Data. Goldstein told The Economist that he wants “to make Chicago’s data openly accessible and useful to the millions of people who live and work there.” The article indicates that a number of cities share this ambition. It continues:
“As cities also start to look back at historical data, fascinating discoveries are being made. Mike Flowers, the chief analytics officer in New York, says that if a property has a tax lien on it there is a ninefold increase in the chance of a catastrophic fire there. And businesses that have broken licensing rules are far more likely to be selling cigarettes smuggled into the city in order to avoid paying local taxes. Over in Chicago, the city knows with mathematical precision that when it gets calls complaining about rubbish bins in certain areas, a rat problem will follow a week later.”
David Sasaki believes that cities should follow the lead of the “quantified self” movement and look to become “quantified cities” that can monitor their health using Big Data analytics. [“Quantifying Our Cities, Ourselves,” Next City, 25 June 2013] He reports that a number of companies are partnering with cities to provide citizens with access to pertinent data. These kinds of software programs, Sasaki insists, empower citizens in two ways. First, they help people make better decisions; and, second, they hold public servants up to greater scrutiny and accountability. He thinks this is a good direction to be heading. He explains:
“The so-called ‘smart city’ has until now been defined by companies like Cisco, Siemens and IBM, which sell public officials a vision of future cities that are fully automated by a network of intelligent sensors. However, these companies are less interested in empowering citizens to make better decisions and participate more meaningfully in the governance of their communities. Now, a growing number of social critics and urban planners are speaking out against the smart city. ‘No one likes a city that’s too smart,’ notes the sociologist Richard Sennett in an that lambasts the smart city prototypes of Masdar City and Songdo. Writing in the Boston Globe, author Courtney Humphries collected the various arguments of critics of the ‘too-smart city.’ These critics remind us that we have already tried to build smart cities from scratch using the latest technologies of the past, and that those failures should serve as warning signs as cities invest billions of dollars in top-down projects based on the technologies of today.”
For many critics, the “too smart city” creates a sterile cultural environment that can destroy the vibrancy that makes cities exciting places to live. On the other hand, one of the real values created by cities is more efficient use of resources. Technologies, like those offered by Cisco, Siemens, and IBM, have a lot to offer when it comes to using resources more efficiently. Just because electricity, water, sanitation, and transportation systems can be run more effectively and efficiently doesn’t mean that the character of a city need suffer. For further discussion on that topic, read my post entitled “Smart Cities, Like It or Not, Will Require Technology in the Future.” Ultimately, smart cities require smart people, smart policies, and smart systems. The goal of Big Data analytics is to help foster better decision making by citizens, policymakers, and systems. As Sasaki states, “There is an important difference between data and knowledge. Data must be interpreted within specific contexts and compared to relevant points of reference. Knowledge informs our decisions and behavior. … The quantified city is built on data, but ultimately comes down to communication. Numbers must be recognized as just another part of the ongoing conversation of our cities. Imagine a government that doesn’t only share its performance indicators and goals with the public, but invites the public to weigh in on how the data reflects reality and what still needs analysis or improvement.”
In a very interesting article, Anya Kamenetz discusses how “cold, hard, data” has helped cities like Baltimore and New York make better decisions. [“How Cities Are Using Data To Save Lives,” Fast Company, 12 November 2013] She concludes her article, however, with a warning drawn from a report published by “Bridgespan, a nonprofit consultancy for philanthropists and mission-driven organizations, and the education organization America Achieves. The report discusses “how data-driven decision making can lead to the most effective use of a city’s limited financial resources.” Here’s the cautionary note:
“There is a big caveat to this trend of data-driven policymaking, and that involves deciding the standard of proof used to drive decisions. ‘City leaders should prioritize outcomes instead of just outputs,’ the report cautions — that means multiple, meaningful, independent measures of success, not more easily trackable and manipulable single stats. Otherwise cities that rush into the ‘Geek City’ category risk falling afoul of ‘When a measure becomes a target, it ceases to be a good measure.'”
There are a lot of considerations that must be taken into account as cities try to make themselves more livable and more sustainable. In fact, there are so many factors and so much data to be analyzed that without the help of cognitive computing systems, policymakers, citizens, and systems are likely to overlook insights that could prove valuable.
