Nearly a decade since “smart cities” entered the vernacular — a term that early on captured the heady sense of what good could happen if we harnessed digital technologies to improve the way we live, work and connect – cities are beginning to grapple with the movement’s next phase: documenting its costs and benefits.
Billions of dollars have been invested in everything from smart parking meters to smart street lights and entire electrical grids. Spending around the globe on smart-city initiatives could total $41 trillion over the next 20 years, according to some estimates. With such sums at stake and urban sustainability a growing priority for cities, policymakers, planners and investors alike are keen to understand the impact of these projects to determine whether they are indeed smart, at least financially. But the task is not easy.
“Cities face enormous challenges when they try to assess the costs and benefits of smart city initiatives,” said Jarmo Eskelinen, Chief Innovation and Technology Officer of Future Cities Catapult, a London-based center focused on urban innovation. “The complexities and interdependencies of city systems combined with a lack of evidence of impact mean that cities are not always able to justify major smart city investment.”
With these challenges in mind, Future Cities Catapult commissioned researchers at NYU’s Center for Urban Science + Progress, led by faculty members Stanislav Sobolevsky and Constantine E. Kontokosta, to use data to analyze the impact of urban innovations. As part of the broader project, researchers are using a grant of anonymized and aggregated Mastercard transaction data to document the economic impact of two smart city initiatives: bike-sharing services and free Wi-Fi in New York City.
“Modern cities face increasing sustainability and operational challenges due to their increasing complexity,” said Sobolevsky, an associate professor of practice at NYU CUSP. “And while smart city innovations become a common way of addressing those challenges, it is of a critical importance for the urban stakeholders to justify such innovations by foreseeing their impact.”
The Mastercard data insights, he says, provide an “unprecedented opportunity to quantify such impact.”
Bike-sharing: bringing more business to food establishments
Bike-sharing systems that are blossoming in cities across the country are a boon to commuters. But as the research now shows, they can also provide a boost to a city’s food-related businesses.
NYU CUSP researchers used Mastercard data insights to understand overall spend behavior on the food industry near bike-sharing stations and estimate the growth of commercial activity near the bikes. The hunch was that people dropping off a bike might also drop in for a bite to eat.
Their hunch panned out. Neighborhoods in Brooklyn saw a bump of 0.2 to 0.5 percent improvement in food retail volumes in the years following the addition of bike-sharing stations, compared to a slight decrease or steady levels in nearby ZIP codes that didn’t have bike-sharing stations. A deeper analysis in Jersey City showed a bump in sales growth closer to 4 percent versus a control neighborhood. While it is impossible to say with certainty that the bike-sharing station deployments are the cause of the sales growth, the authors believe that it is likely playing a significant role.
The Mastercard Center for Inclusive Growth donated the data insights through the Mastercard Retail Location Insights (MRLI). MRLI is an interactive mapping and reporting tool designed to use sales and market data to measure the sales-based performance of retail locations.
“This is the first trustworthy source of data that can give you a real-time picture of how retail is changing over time,” said Sobolevsky.
Free Wi-Fi is linked to more retail sales
The team found similar results for the free Wi-Fi kiosks scattered throughout the city. As with bike-sharing, the researchers suspected that the Wi-Fi kiosks help people, especially visitors, find local businesses. Again, they measured any changes in commercial activity in seven Brooklyn neighborhoods by comparing a nearby “control” neighborhood to their target areas, as well as comparing sales the immediate year prior to the test. They found that within six months after a free Wi-Fi kiosk was installed, retailers in the immediate area saw an overall bump in sales of two to three percent. Again, causality is harder to claim for certain, given the fact that the kiosk locations are not random.
The results, while not definitive, are encouraging. The use of this source of big data is still in its infancy. But, Sobolevsky sees great potential for what can be done with data. More data analyses like this, he says, can become a “valuable asset for many types of urban stakeholders.”
Featured Photo Credit: STAN HONDA/AFP/Getty Images