First released just six years ago, the Chinese social-media app WeChat is one of the most popular in the world, with a reported 938 million active monthly users. It caught on as a messaging service, and has kept adding features. One has become wildly popular in ways that have attracted widespread attention: payments. Visit any Chinese city today and you’ll quickly discover that the option to pay for practically anything by using a smartphone is pretty much inescapable.

The upshot is that WeChat Pay has emerged as a powerful example of a digital-payments ecosystem taking hold through a unique intertwining of mobile technology and the built environment. Along with a rival service called Alipay (offered by e-commerce giant Alibaba), it’s at the center of a digital phenomenon shaped in part by the city context—and one that may, in turn, affect elements of that urban context in the future.

The general notion of digital payment is nothing new. PayPal has been around for years; your credit card details are likely on file at a slew of online retailers; a solid and growing base of users rely on Venmo to make person-to-person payments; Apple Pay has forged deals to enable smartphone payments at a number of major retailers in the United States and beyond. And so on. But while 2016 mobile payments in the United States totaled US$112 billion (RMB 742.7 billion), the figure in China was a reported US$5.5 trillion (RMB 36.47 trillion). Beyond the numbers, the sheer ubiquity of WeChat Pay and Alipay has made the smartphone-as-virtual-wallet idea more overtly visible, something woven into the fabric of city life.

Accepting payment via WeChat requires a vendor to do little more than print out a unique QR code—essentially a more advanced form of bar code—and link it to a digital account; to make a payment, a customer can scan that code with a smartphone. Pony Ma, the CEO of WeChat parent Tencent, has called the QR code “a label of abundant online information attached to the offline world.” For sellers, there’s no need for anything as complicated or expensive as the special devices a vendor typically needs to accept credit-card payments (or, for that matter, Apple Pay); anybody can print a QR code.

That’s one reason WeChat Pay caught on not just with larger established businesses, but also everything from small restaurants to street vendors. “It’s impossible not to use,” says Kate Austermiller, program manager for the China program of the PKU–Lincoln Center in Beijing. Skeptical at first, she now relies on WeChat Pay even for minor transactions like buying water or a piece of fruit from a vendor. “It’s almost faster than fishing through my purse for cash—my phone is always in a pocket,” she says. Even buskers use it to accept “tips” via a QR code, as easily as they might collect coins tossed into a hat.

The Better Than Cash Alliance—a United Nations–based organization focused on financial inclusion, with business, government, and other collaborating members—recently published an extensive case study focused on the rise of digital payments in China, and what that trend could mean globally. “Digital payments are very closely linked to financial inclusion,” observes Camilo Tellez, the head of research and innovation at the alliance. In China, Africa, and elsewhere, he explains, mobile payment systems have given millions of people their first direct link to the formal financial system.

“In China it’s become really obvious that SMEs—small- and medium-sized enterprises—can really reap the benefits,” Tellez says. “Leveraging digital payment systems can actually allow them to access new forms of credit” unavailable to a pure-cash operation, he continues, and that can have a major impact on managing or even growing a business. A payment system folded directly into a social network has other advantages; the Better Than Cash Alliance report tells the story, for instance, of a hair stylist who used WeChat both to expand his customer base, and to avoid carrying too much cash when traveling among client appointments.

Because WeChat made it easy for all sorts of online vendors to use its platform (and even subsidized third-party developers to help them), users can now do anything from book a flight to pay utilities to reimburse a friend for a shared meal, without ever leaving the app. “People responded to it rapidly—it really provided a lot of convenience,” says Zhi Liu, director of both the China program at the Lincoln Institute of Land Policy and the Peking University–Lincoln Institute Center for Urban Development and Land Policy in Beijing.

In fact, Liu confesses that while he’s not the type to jump onto the latest tech trend, this one made itself irresistible, even offline. He’d start looking around for an ATM to get the cash to, say, split a bill, and his colleagues “would just use a mobile phone and say ‘It’s done!,’” Liu laughs. Pretty soon, you just get on board with everyone else. And thus the flip side: any given urban business quickly figures out that if every rival on the block accepts this payment form, it’s time to do the same.

Some country-specific factors have likely contributed to the digital-payment explosion in China. Its Internet ecosystem is distinct in part because familiar entities like Google and Facebook, among others, are essentially locked out, and a kind of alternative universe of connected innovation has evolved. And in the case of these payment services, at least, Chinese regulators have so far allowed a fair amount of latitude for experimentation. (Current government planning around financial development through 2020 includes the specific encouragement of extending financial services to micro-businesses and low-income groups.)

And in China, digital payments arrived as an option in a fairly cash-based society—certainly compared to the deeply entrenched credit and debit card culture of the United States. (Some observers suggest that a Chinese aversion to debt makes digital payments preferable to the plastic alternative Americans in particular are so fond of.) The leapfrog from cash to digital seems to be happening elsewhere in the developing world, with the rapid rise of mobile as a driving factor. This has been amplified by population shifts toward urban centers, where job opportunities concentrate, that make the ability to stay connected with family or other contacts across physical distances more important.

WeChat isn’t the only digital payment player, or even the first, in China. Alibaba Group’s e-commerce platform dates back to the late 1990s, and evolved from a business-to-business marketplace into a variety of digital-payment products and services that made the company a global powerhouse. Its Alipay app was early to target brick-and-mortar merchants with an offline, QR code–based payment system. But it is widely acknowledged that when WeChat creator Tencent put its payment feature on the map with a major marketing push a couple of years ago, it was a game-changer.

Cleverly, the campaign played off a tradition of making monetary New Year’s gifts of cash in red envelopes. WeChat offered a digital Red Packets promotion, and an estimated 5 million users participated—learning in an instant to associate the social network with payments. For Tencent, the payment feature isn’t necessarily conceived of as a profit center, but as another attraction keeping WeChat users locked in to a service that profits from games and advertising. The company has subsidized third-party developers to help more businesses adopt WePay, and peer-to-peer transactions are free.

The more WeChat Pay took off, the more Alipay countered with its own competitive moves. Both systems are now widely available in China—and compete with various “cashless society” promotions involving discounts or rebates—and the companies are each diving into markets elsewhere, sometimes in partnership with local players.

As digital payments have become a routine part of city life, they’re already subtly shaping it. Tellez, of the Better Than Cash Alliance, points to the effect on utility cost-recovery and toll collection, particularly in developing-world contexts; and, for even small businesses, the ability to collect and leverage useful transaction data. And as Liu points out, the broader potential of higher-level data collection is tantalizing. Clearly there are privacy-related concerns about how such data is shared and utilized. But in an academic or planning context, it may offer a window on day-to-day economic behavior that can give us a whole new way to, as Liu puts it, “understand the city.”

Rob Walker (robwalker.net) is a columnist for the Sunday Business section of The New York Times.

Photograph: Tao Jin

Drones are revolutionizing data collection and mapping, ushering in major shifts and new opportunities in the domains of land management, policy, and advocacy.

Unmanned aerial vehicles (UAVs) came into wide use globally about a decade ago, as their cost fell rapidly in the consumer market. In the developing world and in rapidly urbanizing areas, drones are quickly becoming an essential tool for securing land rights, updating maps in virtual real-time, and understanding unplanned settlement patterns. From Latin America to South Asia, the drone is being deployed across the geospatial information and land management sectors, by surveyors defining specific urban parcels, appraisers determining land value over a peri-urban field, and corporate and government employees updating territorial information.

The technical capacity of drones—which can carry multispectral small-format aerial cameras and produce images of both the visible environment and the infrared spectrum—provides a substantial complement to traditional aerial photography and even high-resolution satellite imagery. Because UAVs can fly at very low altitude and execute tight, repeating patterns, they can produce fine-grained images of one centimeter resolution or better, enabling production of three-dimensional images.

Their democratic potential is also stirring excitement, as they empower citizens, nongovernment organizations, and other smaller, more informal networks. “Drones are going to make the difference for policy and decision-making processes, as citizens participate in data creation at critical moments,” notes Diego Alfonso Erba, a land surveyor engineer and expert in Latin American land management systems. “Citizens can fly them, take photos of a situation, and share the results with authorities. In rapidly evolving situations where informal settlement, unsanctioned resource extraction, or conflict is occurring, drones can furnish proof to legal systems.

Latin America’s pioneering use of drones to enrich and improve land policy and management is echoing across the globe. “We are doing the same thing in China,” says Zhi Liu, China program director at the Lincoln Institute and director of Peking University–Lincoln Institute Center for Urban Development and Land Policy (PLC) in Beijing. In East Asia, drones are aiding new high-tech research and experiments to modernize land registries for contemporary uses and to help address other large-scale challenges, including potential implementation of property tax.

Cadastres: Public Land Registries in Latin America

In Latin America and Asia, drones are proving especially useful in the evolution of territorial “cadastres”—public registries that manage information relating to land parcels and that play a critical role in land use decision-making throughout Latin America.

In most of the region, existing territorial cadastre systems derive from an “orthodox” model imported centuries ago from colonial Europe, says Erba, who coauthored Making Land Legible: Cadastres for Urban Planning and Development in Latin America, published by the Lincoln Institute in 2016. He is working at the forefront of an effort to upgrade these land registry systems to what are known as “multipurpose cadastres (MPCs),” and drones are playing a key role in this evolution.

Traditional, or “orthodox,” cadastres are maintained as public registries by governmental institutions. They’re inadequate for contemporary urban policy-making because they cover only private parcels and account for limited physical, legal, and economic attributes. Multipurpose cadastres, by contrast, are maintained by volunteer stakeholders in a jurisdiction who commit to providing richer, more inclusive information about a city. MPCs may include alphanumeric data and thematic or domain-specific cadastres pertaining to the environment, transportation systems, or utility networks, and they may be organized by government and private organizations. The benefits can include better urban planning as well as more equitable taxation, increasing revenues, and a broader tax base.

“The data integration provided by the MPC model is the most direct way to identify and monitor the economic, physical, legal, environmental, and social characteristics of parcels and their occupants,” observe Erba and coauthor Mario Piumetto, a land surveyor who specializes in geographic information systems. “Planners need this information to manage the growth of cities, define strategies for urban financing, reduce informality, and analyze the impact of government interventions” (Erba and Piumetto 2016). By democratizing the tools of geospatial monitoring, drone technology is helping to facilitate this movement toward multi-stakeholder MPCs.

Established Latin American cities with existing cadastres are using drones to tackle challenges associated with informal construction. For example, in Villa 31, one of the most valuable areas of Buenos Aires, some 40,000 people have built informal constructions up to five stories high within a 100-block area, note Erba and Piumetto. In 2016, the government launched a drone survey, in tandem with a street-level laser scanner, that created a 3-D model and generated statistics on the occupation of dwellings, streets, and public spaces. With this more accurate picture of residential development, agencies and stakeholders are in a better position to transition informal settlers toward formal property ownership and participation in planning processes.

Ecuador demonstrates how drone-enhanced cadastres can promote resiliency as well. The city of Portoviejo has been using drones to enforce rules against unpermitted occupation of public spaces. By comparing 2010 records with recent drone-produced monitoring imagery, authorities determined that more than 7,000 instances of new construction violated permitting rules. In April 2016, this more accurate real-time record of settlement proved vital when a 7.8-magnitude earthquake wreaked havoc on structures throughout Portoviejo, killing more than 200 people. Photos after the earthquake were compared to recent drone footage, aiding rescue and rebuilding efforts.

GIS-Assisted Mass Appraisal in China

In China, drones may prove most useful in current efforts underway to assess property value (see p. 8). Since 2003, China has been contemplating introduction of a municipal property tax on the private ownership of residential properties—a power that municipal governments have not had for several decades. However, most cities face a huge technical barrier: There is no system of property assessment or database. Researchers hope drones can help facilitate the underlying basis for assessment.

“The question is how we can help so many Chinese cities to quickly develop a cadastre system, which is the basis of a property tax system,” says Liu, noting that the PLC is funding a research project in China to provide innovations in this area. The next stage is for researchers in China to merge property rights data with 3-D representations of parcels produced by drone technologies. Crucially, researchers must get the full property rights data from the government—such as formal ownership information and the dimensions of units, parcels, and buildings—in order to match up the 3-D imagery in accurate fashion. It is unclear whether these records are fully digitized in many cities, Liu notes. Although drone-generated data cannot provide missing ownership documentation, better parcel data will ultimately accelerate the process of generating an accurate cadastre system.

Chun Zhang, a professor of city planning from the Beijing Jiaotong University and a leader of the project funded by PLC, says drones are currently using tilt-shift photography—which can make the features below a drone look like a miniature representation—and creating 3-D models through the imagery captured. The project will then provide basic spatial information. Currently, the drone techniques are being applied in small towns such as Jimingyi, Shexian, and Gubeikou. But as the researchers experiment with drones, they are bumping up against technical and regulatory limits. “The survey area cannot be too large,” notes Zhang, given the limits of the drone’s battery. “The biggest difficulty for researchers is flight control in certain [limited] areas—within the 6th ring of Beijing, for example.” But this should not be a problem if the municipal governments decide to use drones to develop a 3-D property database.

Property valuation is beyond the scope of Zhang’s current research project, but it will be a challenge of massive scale in China. Ultimately the work-intensive process might be solved by computational methods aided by drone-generated data. In the United States, Liu notes, local governments have long used computer-aided mass appraisal (CAMA) techniques to appraise all properties in a certain area. “In China, we work with a few cities that are refining the computer-aided mass appraisal model to incorporate big data, so they can assess property value more accurately,” Liu says. That sort of work might constitute the next phase of research. But the current phase remains focused on seeing how well existing property records can be matched with the drone data.

In the context of land registries, the use of drones is proving crucial in the initial and provisional identification of physical property limits in cities and jurisdictions where there is still no formal land administration system and the land structure is unknown.

Pathways to the Drone Revolution

Drones are now functioning in a crucial capacity across a variety of land policy use cases, fulfilling cultural and legal needs, but their development and use obviously have a wider story. Their evolution toward wider commercial and recreational use—including sharper definition of land policy—is in some ways a classic story of second-order effects of technological innovation. The original development and prototyping of the flight technologies took place largely in the context of military research. But some of the key technical breakthroughs required to make flight-relevant instrumentation available at a reasonable price point resulted from the “smartphone wars,” wherein various communications technology companies raced to perfect efficient hardware and software for compasses, gyroscopes, altimeters, and more (Anderson 2017).

Still, even as the technology has been ready and the economics right for wide public use, the policy environment for drone use has needed to mature. In the United States, for example, the Federal Aviation Administration has tried to grapple with commercial and consumer demand while balancing concerns over conflicts with manned aircraft flight paths and potential invasion of privacy and land rights. These types of policy debates have been playing out across the globe, as noted above.

Yet many of the technologies under development are focused on agricultural lands, where competing interests and conflict are minimal. Farming is expected to be the primary zone for commercial use of drone technologies. Because drone instrumentation can be used to measure radiation signatures and the infrared spectrum, drones hold massive potential for improvements in crop yields and farming in general (Wihbey 2015). But the benefits have been unevenly distributed over the past decade, as countries such as Japan and Canada have opened up farming airspace, even as the United States is debating where to open up air space policies for agriculture (Lewis 2017). To scale the technology for farming, much greater latitude will be required for drones flying beyond the sight of ground operators. In any case, the idea of “precision farming” has caught on globally, with potential environmental benefits, such as reduced and more targeted use of pesticides and other chemicals. And surely the advancements achieved for rural farm settings will have applications for monitoring, for example, forest reserves and wildlife populations, and for global efforts to limit sprawling unplanned settlements and ensure ecological sustainability (Paneque-Gálvez et al. 2014).

Policies related to the training, licensing, and certification required for drone operators continue to evolve in many countries, and of course formal land surveying itself has its own professional standards that are integrating these new technologies. Citizen or recreational use and informal monitoring of land and urban space is bound to grow only more complicated, as new observational possibilities and challenges emerge from the use of multiple drones simultaneously and “swarming” techniques, as well as the potential for both greater autonomy, as drones become smarter through software, independent of human operators (The Economist 2017).

Challenges

Drones could prove a crucial tool for managing extensive land use problems expected to emerge over the coming decades as the world rapidly urbanizes, from housing inaffordability to shortages of land for open space (Wihbey 2016). Indeed, drones might facilitate a form of technological “leapfrogging,” similar to that of mobile phone Internet connectivity, which has allowed many individuals and societies across the developing world to connect to the Web without dedicated broadband lines to households.

PLC Director Zhi Liu thinks that multipurpose cadastres would enable solutions, but many Asian cities would need technical advancement, as well as political willingness and public support, to improve and update the cadastres of their rapidly growing cities. Experiments in small cities and towns in China might prove useful to other bigger cities in the region, if not countries around the world.

Regulations throughout South and Central America are evolving to keep up with the proliferating use of drones as tools to upgrade land policy in the region. In Brazil alone, officials estimate that 20,000 drones were in operation in 2015, with applications mainly in agriculture, mining, infrastructure inspections, security and border control, and the mapping of environmental areas and cities, according to Erba and Piumetto. In May 2017, this growth prompted the Brazilian National Civil Aviation Agency (Agência Nacional de Aviação Civil or ANAC) to issue new safety and operating rules, which cite and specifically follow definitions of other civil aviation authorities such as those found in the United States and the European Union (ANAC 2017).

In Mexico, the Civil Aeronautics General Directorate in Mexico has issued a similar set of rules that aims to prevent accidents and protect third persons and property on land and in flight. In Argentina, flights above 400 feet (122 meters) require authorization, and there are also limitations depending on the weight of the equipment, the areas overflown, and the information collected.

New Frontiers for Drones

Many institutions across the world have become interested in leveraging drone technologies to help solve age-old administrative problems, particularly in areas of the world that have suffered under adverse conditions caused by conflict or difficult economic conditions. The World Bank, for example, has highlighted efforts in the post-conflict Balkans, where areas in Kosovo have been left with lingering problems after property owners, mostly male, were killed in the 1990s regional war. The women left in these areas have struggled to reestablish order with regard to property and land policy, given the lack of formal records. The World Bank has noted: “The time, cost, and complexity of conventional land surveying and registration . . . is an obstacle for these women. It often takes years and is too expensive to complete, leaving these women with no information or legal protection of their rights” (World Bank 2016). In partnership with the Kosovo Mapping Authority, drones are therefore being used to execute cadastral mapping activities.

World Bank experts have also noted that drones are proving to be effective weapons in the fight for land rights in underdeveloped areas on the African continent (Totaro 2017). Although nearly 90 percent of Europe is mapped at a local level, only 3 percent of the African continent has maps at such resolution. As coastal zones are rapidly developed for hotels and commercial/residential use, drones could help communities keep up with development and garner appropriate tax revenue.

Overall, the strength of drones comes from the richly detailed information they can collect at relatively low cost; they can even produce quality 3-D models of streets and properties and expedite data collection. But certain weaknesses must be taken into account. UAVs can only provide limited territorial coverage, given limited speed and autonomy of flight. Adverse weather conditions are also a significant issue.

Drones have so far proven most effective in urban operations, which often require great detail and richness of data. Any decision to deploy drones has to weigh costs and benefits for a given task. High-resolution satellite images (currently down to 30 centimeters, or 1.8 inches, in resolution) may suffice; if the area to be surveyed extends beyond 25 kilometers (about 15.5 miles), satellite image files may be more appropriate and efficient.

Yet drones furnish possibilities that no other aerial surveying technology provides given their mass market deployment. “Drones will democratize geospatial information gathering and analysis,” Erba says. “Everybody will soon have access to the tools that only satellite owners had just a few years ago. Photos could be sent all the time to the cloud.” And this new capability, he notes, could strengthen transparency and accountability of many kinds, and bring efficiencies to government: “Aerial photos of areas being invaded or deforested in real-time could be sent directly to the officer responsible for urban monitoring. This extremely relevant information can be provided at no cost to the state, and it can be used immediately for action.”

Whether such action involves more uniform regulatory enforcement, better tax collection, or richer, more dynamic data for land registries, these new technologies are poised to bring major shifts across numerous aspects of land policy worldwide.

John Wihbey is an assistant professor of journalism and new media at Northeastern University. His writing and research focus on issues of technology, climate change, and sustainability.

Photograph: iStock.com/dabidy

References

ANAC (Agência Nacional de Aviação Civil). 2017. “Orientações Para Usuários de Drones.” Brasília, Brazil: ANAC.

Anderson, Chris. 2017. “Drones Go to Work.” Harvard Business Review, June 7. https://hbr.org/cover-story/2017/05/drones-go-to-work.

The Economist. 2017. “Drone Technology Has Made Huge Strides.” June 10. www.economist.com/news/technology-quarterly/21723001-originally-military-technology-drones-are-now-benefiting-rapid-advances.

Erba, Diego Alfonso, and Mario Andrés Piumetto. 2016. Making Land Legible: Cadastres for Urban Planning and Development in Latin America. Policy Focus Report. Cambridge, MA: Lincoln Institute of Land Policy.

Lewis, Jason. 2017. “Striking a Balance on Drone Regulation.” The Hill, July 10. http://origin-nyi.thehill.com/blogs/congress-blog/technology/341300-striking-a-balance-on-drone-regulation.

Man, Joyce Yanyun. 2012. “China’s Property Tax Reform: Progress and Challenges.” Land Lines 24 (April): 15–19.

Paneque-Gálvez, Jaime, Michael K. McCall, Brian M. Napoletano, Serge A. Wich, and Lian Pin Koh. 2014. “Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas.” Forests 5 (6): 1481–1507.

Totaro, Paola. 2017. “Newest Technologies Becoming Weapons in Fight for Land Rights.” Reuters, March 20. www.reuters.com/article/us-global-landrights-technology/newest-technologies-becoming-weapons-in-fight-for-land-rights-idUSKBN16R2IE.

Wihbey, John. 2016. “Boundary Issues: The 2016 Atlas of Urban Expansion Indicates Global De-Densification.” Land Lines 28 (October): 18–25.

Wihbey, John. 2015. “Agricultural Drones May Change the Way We Farm.” The Boston Globe, August 22. www.bostonglobe.com/ideas/2015/08/22/agricultural-drones-change-way-farm/WTpOWMV9j4C7kchvbmPr4J/story.html.

World Bank. 2016. “Drones Offer Innovative Solution for Local Mapping.” Washington, DC: World Bank, January 7. www.worldbank.org/en/news/feature/2016/01/07/drones-offer-innovative-solution-for-local-mapping.

China is one of a small number of countries around the world that does not levy property tax on privately owned residential properties. After the Communist Party established a socialist regime in 1949, China adopted a public land ownership system and thereby lacked a real estate market until the reform era.

Since the reform, property sales, along with the economy as whole, have boomed. First-tier cities such as Shanghai and Beijing are now home to some of the world’s most expensive real estate. But taxes are imposed only at the point of property sales and transactions, not annually on ownership.

It may come as a surprise, then, that China is driving the evolution of valuation technology, particularly in Shenzhen—the brand-new southern city that has grown from a small town of 50,000 residents to a major metropolis of 12 million since 1982. The Shenzhen Assessment Center—a municipal statutory agency that was established to assist the collection of taxes on real estate sales and transactions—has developed what is arguably the most advanced property valuation system in the world. It is a logical extension of the computer-assisted mass appraisal (CAMA) system that the Lincoln Institute was instrumental in developing for desktop computers decades ago. The Peking University–Lincoln Institute Center for Urban Development and Land Policy (PLC) has helped several Chinese cities implement CAMA in anticipation of a future property tax. What makes Shenzhen’s system different is that it uses GIS technology and new techniques that elevate CAMA to the next level.

Today, CAMA is an international standard that has made it possible to assess entire metro areas from a desktop computer. But CAMA by nature is mainly a two-dimensional system, whereas modern geographical information system (GIS) software is capable of efficiently rendering three-dimensional (3-D) maps. The future of property assessment lies in marrying CAMA techniques with GIS tools in a system known, naturally, as “GAMA.”

CAMA systems are, broadly speaking, not overly exciting to look at, with lots of data tables and highly detailed two-dimensional maps. GAMA by contrast is dazzling. Using GIS tools, the system constructs 3-D models of entire cities, with streets, buildings, the individual properties within them, landscape features, and so on. Imagine the feel of an open-world video game. The aim is to be able to appraise every property from computers in the assessment office.

“In my view, Shenzhen is dragging CAMA into the next generation, doing things in their valuation that nobody else can do,” says George W. McCarthy, president and CEO of the Lincoln Institute.

Shenzhen: Center of Progress

In many ways, the development of Shenzhen’s property assessment system is the classic story of modern China: starting from far behind, absorbing knowledge from more advanced economies, adapting to local needs, and ultimately coming to rival the best in the world. The fact that it happened in Shenzhen—the Special Economic Zone that launched the experimentation that transformed China from a largely rural economy to a global power—is unsurprising. In 1979, as China was charting the course of its new reform, four cities were declared “Special Economic Zones (SEZs),” pilot projects where the government would experiment with market mechanisms. Shenzhen, a fishing town of just 30,000 people, was one of them. Adjacent to Hong Kong, which was administered at that time by the British and highly internationalized, Shenzhen was in a perfect position to perform the mission of SEZs—attract global companies to trade, bring in foreign direct investment, and obtain for China the tools necessary to forge a modern developed nation.

As investment poured in and factories sprang up, Shenzhen became the beating heart of China’s new economy, and one of the world’s most advanced cities. In just 30-odd years, it grew into a bustling metropolis of nearly 12 million. Its official GDP in 2016 was US$284 billion (RMB 1.88 trillion), with a per capita GDP of US$25,790 (RMB 171,013), more than triple China’s average. Sometimes called China’s Silicon Valley, it is home to some of the world’s most powerful tech companies, including Internet giant Tencent.

As early as 2003, the central government started to consider introducing a property tax. Six cities were selected as pilot experiment cities for mass appraisal of properties. Shenzhen was one of them. Shenzhen’s Center for Assessment and Development of Real Estate was founded that same year to commence the enormous task of citywide valuation. At first, they were more or less on their own and progress was slow. It took three years to designate basic prices in 56 neighborhoods, in order to assign a single price for the whole area.

The initiative coincided with the Lincoln Institute’s foray into China in 2003, when it began developing relationships with government agencies and conducting research projects on topics ranging from property tax and municipal finance to public land management and land expropriation. “We saw the changes as the economy was being opened up, and we figured there would be all sorts of land policy challenges to grapple with,” McCarthy says.

In 2007, the Lincoln Institute and Peking University, China’s oldest and most prestigious university, endeavored to open the PLC, a research institute staffed by both organizations. One of the PLC’s early tasks was to help the Chinese government understand how to create a property tax that works as a system of revenue. The PLC organized training events to disseminate international knowledge of property taxation and computer-assisted mass appraisal to China. The PLC invited experts from the International Association of Assessing Officers, International Property Tax Institute (IPTI), Rating and Valuation Department of Hong Kong, ESRI Canada, and others. To better demonstrate how CAMA worked, the PLC launched a pilot demonstration project that established a CAMA system for the financial district of Beijing. The PLC also mobilized international experts to assist Shenzhen and Hangzhou, and funded study tours for technical personnel, in the United States, Canada, and Hong Kong. The impact was enormous.

“The PLC was translating the professional literature on property valuation, and it was the first time we were encountering some of this stuff,” says Dr. Wang Youjie, head of the Shenzhen center’s mass appraisal department. “They also introduced us to CAMA.”

Aided by access to a developed body of knowledge, progress in Shenzhen rapidly accelerated. By 2010, the center had evaluated prices on a per-building basis for 170,000 buildings, and by 2011 had done basic evaluations for 1.5 million residential properties. “After understanding the theory better, 2010 to 2011 was a breakthrough point for us,” says Xia Lei, director of the Shenzhen Assessment Center.

Also important was the Lincoln Institute’s role as a connector, enlisting top foreign experts to host seminars and perform hands-on training and development work. To date, the Lincoln Institute has mobilized more than 20 property tax experts to China. For the assessment center in Shenzhen, no one was more familiar than Michael Lomax.

For 22 years, Lomax worked as property assessor for British Columbia Assessment, a province-wide assessment office in Canada. He was among the first people the Lincoln Institute brought to China in 2007, when he joined a government delegation. He has continued making trips to China even after leaving British Columbia Assessment in 2012 to take a position with ESRI, which specializes in GIS solutions.

“A lot of my work in China was to illustrate, convey, and help them install worldwide best practices,” says Lomax, who also teaches mass appraisal at the University of British Columbia. Around 2011, he began working more directly with the Shenzhen center and an appraisal firm hired by the city of Hangzhou, Zhejiang, a city not far from Shanghai. Like Shenzhen, Hangzhou is also known for its tech industry, including the headquarters of e-commerce titan Alibaba.

The speed at which these two cities were working was sometimes astonishing. During one trip to Hangzhou, Lomax spent an entire day critiquing the assessment system built by the local department. The next morning, they asked him to look again. “They had their programmers stay up all night at the hotel to fix all the problems I pointed out,” says Lomax, still a bit in awe. “This might take you six months to do in the West, and they did it in hours.”

The team in Shenzhen was equally impressive. According to Lomax, they took the computerized evaluation methods to the next level. “They are really advanced in fine-tuning the mathematics,” he says. “Shenzhen is far better at valuing properties dynamically, on the fly, than British Columbia.”

In other words, there was a clear opportunity in Shenzhen to advance the GAMA evolution. “It was Michael that gave us the idea of doing GAMA,” says Wang.

From Follower to Leader

ESRI, a global consulting firm specializing in GIS solutions, is helping to build GAMA models in several municipalities. There is Vancouver, where Lomax works; Maricopa County, Arizona, which encompasses Phoenix; and also Shenzhen. These projects are in varying stages of development, but the Shenzhen system is impressive nonetheless. Sitting in on a demonstration of the system is like inhabiting a painting inside a painting, as if you might spot your virtual self if you peeked in the right window. But what it can do in terms of assessment is even more impressive.

Of course, it factors in all the indicators accounted for by a traditional CAMA system: location, number of rooms, floor space, recent market prices, and so on. It can also estimate the value of being near a subway station or close to a school. The three-dimensional nature of the system boosts the functionality. Using vectors, it is possible to model the window vantage point of every single unit in a given building. From the desktop, the appraiser can determine if a resident has a sweeping view of beautiful Lianhuashan Park in central Shenzhen (think New York’s Central Park, except with palm and banyan trees), or just the boring façade of a neighboring high-rise. The system can also track a virtual sun across the sky, estimating how much daylight an apartment gets. In addition to modeling light, it can also model sound—a lower-floor unit facing a busy traffic intersection, for instance, is disadvantaged compared to a unit facing a peaceful courtyard.

The system weights all those factors and synthesizes the final valuation of a property. All told, these factors can amount to a 20 percent difference in value between two units in the same building.

The system is also being used to better execute property transaction taxes. Through this smaller trial, the efficacy of the tool is apparent: of the millions of properties valued so far, only 27,106 challenges have been made as of January this year, and of those only 282 assessments had to be readjusted.

The Shenzhen assessment project is not without challenges. First, the market is young, so there is a relative dearth of transaction data. On top of that, transactions are sometimes reported at artificially low prices, to avert transaction taxes. Finally, the housing market is highly heterogeneous, with fairly distinct groups of housing types.

Limited property transaction data can be among the biggest challenges to implementing a system such as this. In this regard, Shenzhen has a distinct advantage over just about any other city in the world in terms of the knowledge of its properties. The whole place is brand new, and this is especially true for the city center where the slick 3-D model is most impressive. That means the data on all the buildings and floor plans is existing, complete, and rendered in digital formats that are, relatively speaking, easy to adapt to the model.

The team in Shenzhen cleverly innovated around this with a system they call the “holistic” approach. Briefly, it treats those distinct groups of housing first as separate “sub-markets.” Then by establishing relationships among those sub-markets, they are better able to estimate prices across the entire market with fewer data points overall.

The system alone is marvelous from a technical standpoint, but it is also a testament to the advanced nature of the city as whole. In numerous ways, it is an “only in Shenzhen” achievement.

Shenzhen is unique in a purely Chinese context as well. Conjured by the pure political willpower that gave life to the Special Economic Zones, Shenzhen is not directly administered by the central government. However, as a prefecture-level municipality, Shenzhen enjoys closer relationship with the central government than other prefecture-level municipalities. The central government grants more freedom to Shenzhen to try new things.

“In Shenzhen, government agencies, such as the municipal commissions of planning and land, and finance and taxation, are cooperating to share data,” says Director Xia. In a country where interdepartmental data sharing is rare, it is difficult to understate how important this is. “The point is to be creative.”

Geng Jijin, who directed the assessment center before Xia, when development of the model was most intense, puts a more personal spin on it: “Everybody here is from different places in China. We have no choice but to figure out how to get along.”

The Road Ahead

The job of creating the GAMA system in Shenzhen is not yet finished. Partly because Shenzhen grew at such a breakneck pace, a significant portion of buildings from the newly annexed localities are rather poorly documented. According to Director Xia, bringing these properties into the system is a top priority going forward. Given the scale of Shenzhen, it will likely take a few years to work through the challenge.

The implementation of a property tax goes beyond the purview of the Shenzhen Assessment Center. It is a policy problem and the center does not make policy, Wang says, adding “If the policy is put forward, Shenzhen is ready for it.”

It is anyone’s guess when that might happen, given the politically sensitive nature of property tax in China. While there have been two pilot taxes in Shanghai and the southwestern city of Chongqing, they have been very limited and undertaken mainly as a signal that property taxes are coming. Pressure is, however, building. In the absence of a property tax, and as the net revenues from land lease sales that local governments rely on have declined, local budgets have become increasingly strained.

In the meantime, the assessment center is already helping to spread knowledge beyond its very special borders. Delegations have been sent from all around China to view the system, including from across the river in Hong Kong and all the way from Taiwan.

Lincoln Institute President McCarthy, for his part, is ready to see knowledge and experience flow west. Places such as Boston, where there has long been controversy over building near Boston Common due to the shadows it would cause, could use a system that models the sun.

Actually spreading the new GAMA system will likely be difficult, and there is no telling how long it might take. But nobody would have predicted that a fishing village could become a metropolis in three decades flat.

Tom Nunlist is editorial director at Sinomedia and managing editor of CKGSB Knowledge, on behalf of Cheung Kong Graduate School of Business in Beijing.

The author extends special thanks to Carolyn Wang, a mass appraiser at the Shenzhen Assessment Center, who helped arrange reporting in Shenzhen. This piece would not have been possible without her expert help and remarkable patience. 

Image Credit: Shenzhen Assessment Center

References

Chen, Xiangming, and Tomas de’Medici. 2009. “The ‘Instant City’ Coming of Age: China’s Shenzhen Special Economic Zone in Thirty Years.” Inaugural Working Paper Series, No. 2, Spring 2009. Hartford, CT: Center for Urban and Global Studies at Trinity College.

The Economist. 2012. “Time for a Property Tax: A Way to Stabilise Both China’s Wild Property Market and Its Weak Local Finances.” February 4. www.economist.com/node/21546014.

Shenzhen Municipal E-Government Resources Center. 2017. “Shenzhen Government Online.” http://english.sz.gov.cn/.

Wang, Da Wei David. 2016. Urban Villages in the New China: Case of Shenzhen. New York, NY: Palgrave Macmillan.

Xiao, Cai, Wang Yu, and Hu Yuanyuan. 2017. “Overall Govt Debt Risks ‘Under Control.’” China Daily USA, July 13. http://usa.chinadaily.com.cn/epaper/2017-07/13/content_30102302.htm.

La empresa Strava, con sede en San Francisco y fundada en 2009 como una “red social para deportistas”, hoy en día es más conocida por su famosa aplicación para teléfonos inteligentes, utilizada por millones de personas en todo el mundo para medir y compartir con otras actividades tales como andar en bicicleta, correr y caminar. Algunos de los usuarios son deportistas serios, pero muchos otros simplemente usan la aplicación para medir, por ejemplo, los viajes al trabajo ida y vuelta o las salidas de ejercicios de rutina que realizan como parte de un plan básico de entrenamiento. Como resultado, Strava ha recopilado un conjunto enorme de datos, que muestran cómo se mueven los ciclistas y los peatones en las ciudades. Por lo tanto, hace unos años la empresa decidió hacer algo con esta información: “retribuir a la gente de Strava”, en palabras de Brian Devaney, líder de comercialización de Strava Metro.

En su sitio de Internet, la empresa publicó un “mapa térmico” mundial, es decir, una presentación visual e interactiva de sus datos (anónimos). Así, se podía hacer un acercamiento a un barrio de San Francisco, por ejemplo, para ver cuáles son las rutas que los usuarios de Strava utilizan con mayor frecuencia. Los clientes parecían estar contentos con esta función. Sin embargo, la empresa luego se enteró de que otro tipo de público que no había tenido en cuenta hasta entonces estaba también interesado en estos datos. “Comenzamos a recibir muchos correos electrónicos de grupos de urbanistas y departamentos de transporte”, explica Devaney. Estos grupos deseaban tener acceso a los datos de Strava, ya que entendían que estos datos podrían ser útiles para planificar proyectos de transporte e infraestructura, tanto a corto como a largo plazo, o para hacer un seguimiento y demostrar el uso y el comportamiento reales de los proyectos terminados.

Lo que ocurrió fue, en palabras de Devaney, “totalmente inesperado”, pero la empresa ha abrazado esta tarea. Para ello, crearon una nueva división, denominada Strava Metro, con el fin específico de ayudar a los municipios a aprovechar al máximo los datos generados por Strava. “Esto nunca lo habíamos previsto para ninguno de nuestros productos ni era parte del plan estratégico a largo plazo de Strava”, continúa Devaney. “Simplemente ocurrió”.

El caso de Strava es sólo un ejemplo de una prometedora convergencia del apetito de los urbanistas por una novedosa categoría de datos y la disposición (tal vez sorprendente) de una empresa comercial a saciar dicho apetito. Otro ejemplo es Waze, la aplicación de mapas y direcciones que funciona, en parte, gracias a la información que proporcionan los usuarios acerca de las condiciones del tránsito con el fin de sugerir la mejor ruta para ir manejando entre dos puntos en tiempo real (actualmente Google es propietaria de Waze, que ha incorporado algunos de sus propios datos a Google Maps pero también se mantiene como una aplicación independiente).

Hace un par de años, Waze lanzó el Programa de Ciudadanos Conectados para facilitar la transmisión bidireccional de datos entre sus usuarios y varias instituciones municipales. Además de permitir a los municipios informar efectivamente a los usuarios en tiempo real todo cierre de carreteras u otros proyectos, este programa también recaba información para las futuras decisiones de planificación que se deban tomar, ya que revela cuáles son los lugares con mayor congestión de tráfico u otros problemas. El año pasado, Waze se asoció con Esri, una empresa que elabora software de mapeos digitales para los municipios. El objetivo es utilizar los datos generados por Waze en cuanto a los patrones de tránsito con el fin de orientar la planificación de los sistemas de transporte, además de no depender tanto de los métodos de recolección de datos más costosos que conllevan sensores conectados a Internet y otros aparatos similares.

Hace poco, Uber, la empresa de transporte compartido, lanzó Uber Movement, un servicio que pone a disposición de los urbanistas e investigadores información sobre tiempos de viaje, condiciones de las carreteras y otros datos provenientes de los miles de millones de viajes que han realizado los conductores que trabajan con la empresa. Según explicó Andrew Salzberg, jefe de políticas de transporte de Uber, a la revista Wired a principios de este año: “No administramos las calles. No planificamos la infraestructura. Así que, ¿por qué íbamos a tener escondida toda esta información si podría ser muy valiosa para los municipios en los que trabajamos?”

Tomados en conjunto, estos esfuerzos suponen nuevas oportunidades —a la vez que nuevos e interesantes desafíos— para la planificación de los sistemas de transporte. “Es un gran salto en cuanto a la cantidad de los datos disponibles”, sostiene Julie Campoli, fundadora de la consultora Terra Firma Urban Design con sede en Burlington y autora del libro Made for Walking: Density and Neighborhood Form (Hecho para caminar: Densidad y forma del barrio) (2012), publicado por el Instituto Lincoln. Por un lado, estos datos pueden ser más informativos que los datos obtenidos de una encuesta sobre formas de viaje, ya que estos últimos se recaban mediante un proceso costoso, que lleva mucho tiempo y que implica preguntas detalladas sobre el comportamiento del tránsito.

Sin embargo, independientemente de su calidad, estos datos nuevos pueden ser parciales: un usuario cualquiera de la aplicación puede tener un sesgo demográfico en particular. Y, tal como lo señala Campoli, no todos tienen un teléfono inteligente. “Es muy bueno tener esa información”, dice Campoli, “pero debe recordarse que no representa a todos”.

Al observar más de cerca la forma en que se han utilizado los datos de Strava Metro en la vida real, podemos ver cómo estos enormes almacenamientos de nueva información pueden integrarse concienzudamente en los procesos existentes. Los analistas de datos del Departamento de Transporte y Carreteras Principales (TMR, por su sigla en inglés) de Queensland, Australia, se interesaron en los datos de Strava desde un principio. Michael Langdon, asesor senior del TMR especializado en ciclistas y peatones, explica que el departamento ya había estado recabando y utilizando datos del sistema de posicionamiento global (GPS) desde hace años, pero que resultaba un proceso engorroso, ya que implicaba muchísimas unidades dedicadas específicamente al GPS y dependía de que hubiera personas que los utilizaran en forma regular y adecuada. “Cuando nos enteramos de Strava, lo que nos sorprendió fue que esta aplicación verdaderamente automatizaba muchos de los procesos que debíamos realizar en forma manual”, indica Langdon.

Devaney explica que Strava, como entidad privada que tenía el objetivo de desarrollar su base de usuarios y sus negocios, nunca tuvo como meta recabar, almacenar o empaquetar sus datos con fines de planificación municipal. Por lo tanto, la empresa tuvo que abocarse a realizar tareas de investigación y desarrollo para que el material pudiera ser fácilmente utilizado por los municipios (aprendiendo a extraer los detalles que fueran relevantes y haciéndolos compatibles con el software y los sistemas ampliamente utilizados) y para formar un equipo que trabajara específicamente con los urbanistas profesionales. Para perfeccionar el proceso, la empresa se asoció a modo de prueba con la ciudad de Portland, en Oregón, y la ciudad de Orlando, en Florida. Para fines de 2016, Strava Metro estaba trabajando con más de 100 municipios. La empresa cobra una cuota anual de uso para cubrir los costos, la cual varía dependiendo de los detalles que se utilicen.

El estado de Queensland fue otro de los primeros socios. Conscientes precisamente de la existencia de los sesgos y las limitaciones mencionadas por Campoli, además de otros posibles defectos, el TMR se dedicó a “analizar y calibrar” los datos de Strava y finalmente publicó un estudio detallado acerca de sus evaluaciones. En resumen, la conclusión de esta investigación fue que los datos de GPS de los teléfonos inteligentes funcionan mejor en conjunto con otras fuentes de datos, pero pueden ser particularmente útiles a la hora de evaluar el impacto de un proyecto de infraestructura específico.

De hecho, el departamento ha utilizado con éxito los datos de Strava precisamente de esa manera. Por ejemplo: el reemplazo de un sendero flotante para ciclistas y peatones que había sido destruido en el año 2011 por una inundación provocada por el río Brisbane. A los funcionarios les llevó varios años comprometerse a reconstruir el paseo denominado New Farm Riverwalk, y el TMR deseaba demostrar que la nueva estructura realmente estaba produciendo un impacto. “La gente pregunta: ‘¿Por qué estamos construyendo esto? ¿Realmente alguien va a utilizarlo? Nunca he visto a un ciclista en ese camino o ese puente’”, comenta Langdon al referirse a los proyectos de infraestructura del transporte en general. Las encuestas tradicionales no siempre responden a dichas preguntas de manera empírica: que los ciudadanos digan que les gustaría tener un nuevo sendero para ciclistas no significa que efectivamente lo utilizarán.

Esta vez, el TMR tuvo información fehaciente para demostrar niveles de uso impresionantes y para presentar un detalle del impacto que esto tendría en el comportamiento de los ciclistas en las carreteras y rutas aledañas. “Los datos de Strava nos permiten demostrar lo que verdaderamente ocurrió”, sostiene Langdon.

A su vez, esto fomenta nuevas iniciativas de planificación. Langdon menciona otro ejemplo: la creación de senderos para ciclistas a lo largo de una carretera principal. Tal como ocurre con muchas grandes inversiones, este proyecto se ha desarrollado en diferentes etapas. Según el análisis que se llevó a cabo en la primera etapa, mediante el cual se realizó una referencia cruzada entre los datos de Strava y los datos oficiales sobre colisiones de automóviles y otras fuentes, hubo un aumento del 12 por ciento en la utilización de bicicletas comparado al uso que se le daba al sendero para ciclistas anterior, así como también una notable desviación de los ciclistas de un camino cercano muy transitado donde eran muy comunes los accidentes. “Esto nos ayudó a defender nuestro argumento de por qué debíamos completar el resto de las secciones, debido a que ya estábamos viendo este beneficio”, explica Langdon.

Como resultado, concluye Langdon, la tarea de calibrar y aprender a utilizar lo que ofrece Strava Metro se ha convertido en una parte habitual del conjunto de herramientas de planificación del departamento: “Ya se ha vuelto algo común para nosotros”.

Strava Metro presenta otros ejemplos en Seattle, Glasgow, Londres y otros lugares. Según Devaney, la recompensa que recibe la empresa es que una infraestructura mejorada para ciclistas y peatones ayuda indirectamente a fomentar los comportamientos que constituyen el centro de su base de usuarios actual y futuro. Para otras firmas, los motivos pueden ser distintos: por ejemplo, la experiencia del usuario final de Waze mejora directamente debido a la comunicación bidireccional con los municipios; Uber desea posicionarse más como un socio de los municipios; etc.

De más está decir que incorporar estos flujos de datos en las prácticas de planificación implica un esfuerzo de ambas partes. Sin embargo, aun cuando los creadores de aplicaciones populares (que, en parte, dependen de la recolección masiva de datos de comportamiento) nunca consideraron que los municipios y los urbanistas pudieran utilizar dicha información, resulta alentador que algunos de ellos están pensando seriamente en dicha posibilidad. Y lo mismo se aplica a los municipios que están buscando nuevas ideas que orienten sus decisiones. Tal como observa Campoli: “Es otra pieza del rompecabezas”.

Rob Walker (robwalker.net) es columnista del suplemento de negocios dominical de The New York Times.

Fotografía: RyanJLane/flickr

Founded in 2009 as a “social network for athletes,” San Francisco–based Strava is today best known for its popular smartphone app, used by millions of people all over the world to track and  share their biking, running, and walking activity. Some users are serious athletes, but plenty simply track commutes or routine exercise excursions as part of a basic fitness regimen. As a result, Strava has built up a massive data set showing how bikers and pedestrians move through cities. And a couple of years ago, the company decided to do something with this information—“to give back to the people on Strava,” says Brian Devaney, the marketing lead for Strava Metro. 

On its site, the company released a global “heat map”: a visual and interactive presentation of its (anonymized) data. You could zoom in on, say, a San Francisco neighborhood, to see which routes Strava users travel most frequently. Customers seemed to enjoy this. But the company also heard from another audience that it hadn’t counted on. “We started to get all these emails from city planning groups and departments of transportation,” Devaney explains. They wanted access to Strava’s data, which many recognized as potentially useful for planning both short- and long-range transportation and infrastructure projects, or for tracking and demonstrating actual usage and behavior of completed projects.

This was “completely unexpected,” Devaney continues, but the company has embraced the development. It formed its new Strava Metro division specifically to help municipalities get the most out of its data. “That was never on a product roadmap or any Strava long-term strategic plan,” Devaney says. “It just sort of happened.”

It’s also one example of a promising convergence of planners’ appetite for an emerging category of data—and a perhaps-surprising willingness of for-profit businesses to feed that appetite. Another example is Waze, the map and directions app that relies in part on user-submitted information about traffic conditions to suggest the best driving route between two points in real time. (Waze is now owned by Google, which incorporates some of its data into Google Maps, but also remains a stand-alone app.)

A couple of years ago, Waze launched its Connected Citizens Program, easing two-way data sharing between its users and various municipal entities. Apart from allowing cities to in effect communicate road closures and other projects to users in real time, the program also helps inform potential planning decisions by revealing locations with frequent traffic congestion or other problems. Last year, Waze partnered with Esri, which makes digital-mapping software for cities. The goal is to use data that Waze generates about traffic patterns to help guide transportation planning—and to reduce reliance on much more expensive data-collection methods involving Internet-connected sensors and the like. 

Most recently, the ride-sharing company Uber has launched Uber Movement, a service that makes available to planners and researchers information about travel times, road conditions, and other data, culled from the billions of rides the company’s drivers have made. “We don’t manage streets. We don’t plan infrastructure,” Andrew Salzberg, Uber’s chief of transportation policy, told Wired earlier this year. “So why have this stuff bottled up when it can provide immense value to the cities we’re working in?”

Taken together, such efforts present some fresh opportunities—and some interesting new challenges—for transportation planning. “It’s a big leap in terms of quantity of data,” says Julie Campoli, founder of the Burlington-based practice Terra Firma Urban Design and author of Made for Walking: Density and Neighborhood Form (2012), published by the Lincoln Institute. And on one level, this can be more informative than travel survey data, gathered in an expensive and time-consuming process involving detailed questions about transit behavior. 

But as rich as the newer data may be, it can carry biases: any given app’s user base may have particular demographic skews. And, as Campoli points out, not everyone has a smartphone. “It’s great to have that information,” she says. “But it’s important to remember that it doesn’t represent everyone.” 

A closer look at how Strava Metro data has been put to real-world use shows how these massive new caches of information can be thoughtfully integrated into existing processes. Data analysts in the Department of Transport and Main Roads (TMR) in Queensland, Australia, took an early interest in Strava’s data. Michael Langdon, a senior advisor in the TMR with a focus on cycling and walking, explains that the department had already been gathering and making use of global positioning system (GPS) data for years, but it was a cumbersome process, involving lots of dedicated GPS units and relying on subjects to use them regularly and properly. “When we saw Strava, what hit us was: this actually automates a lot of the processes that we had to do manually,” Langdon says.

Devaney of Strava explains that, as a private entity focused on building its user base and business, the company hadn’t been collecting, storing, or packaging its data with municipal-planning uses in mind. So it had to devote research and development efforts into making the material easily usable by cities (learning to extract the relevant details, and making them compatible with widely used software and systems), and building out a team to work specifically with planning professionals. Beta partnerships with Portland, Oregon, and Orlando, Florida, honed the process, and by the end of 2016 Strava Metro was working with more than 100 municipalities. It charges annual usage fees to cover costs; these vary depending on details. 

Queensland was another early partner. Mindful of precisely the sorts of biases and limitations Campoli cites, and other potential flaws, its TMR set about “analyzing and calibrating” Strava’s data, ultimately publishing a detailed study of its assessment. In short, the research concluded that smartphone GPS data is best in conjunction with other data sources but can be particularly useful in evaluating the impact of a specific infrastructure project.

In fact, the department has successfully used Strava data in precisely that manner. One example involved the replacement of a floating bike-and-walk pathway destroyed in a 2011 Brisbane River flood. It took several years for officials to commit to rebuilding the New Farm Riverwalk, and the TMR sought to demonstrate that the new structure was really having an impact. “People question: ‘Why are we building this? Are people even going to use this? I’ve never seen a cyclist on that road or bridge’,” Langdon says, referring to transportation infrastructure projects in general. Traditional surveys don’t necessarily answer those questions in an empirical way: just because citizens say they’d like a new bike pathway doesn’t mean they’ll use it.

This time, TMR had hard information to demonstrate impressive usage levels and to detail the impact on cycling behavior on surrounding roads and routes. “The Strava data does allow us to prove what actually happened,” Langford says.

And that, in turn, helps new planning initiatives. Langford points to another example involving the creation of new bikeways along a major motorway. Like many big investments, it has rolled out in stages. Analysis of an early phase, using Strava data cross-referenced with official crash data and other sources, showed a 12 percent increase in bike usage over the prior bikeway—as well as a notable deflection of cyclists away from a nearby, car-trafficked road where accidents were common. “That helped us argue: ‘this is why we need to complete the other sections,’ because we were already seeing this benefit,” he says.

The upshot, Lanford concludes, is that having calibrated and learned to use what Strava Metro offers, it’s evolved into a regular part of the department’s planning toolkit:  “it’s become pretty stock-standard for us now.”

Strava Metro points to other examples in Seattle, Glasgow, London, and elsewhere. The payoff for the company, Devaney says, is that enhanced cycling and pedestrian infrastructure indirectly help encourage the behaviors at the core of its current and potential future user base. For other firms, motives may differ. For example, Waze’s end-user experience is directly improved by two-way communication with cities; Uber wants to position itself as more of a partner to municipalities; and so on.

Clearly incorporating such data streams into planning practices takes effort, on both sides. But even if makers of popular apps that rely in part on corralling behavioral data never considered how cities and planners could use that information, it’s encouraging that some are taking thoughtful approaches to that possibility. And the same goes for cities looking for fresh insights to guide decisions. As Campoli observes: “it’s another piece of a puzzle.”

Rob Walker (robwalker.net) is a columnist for the Sunday Business Section of The New York Times.

Photograph: RyanJLane/flickr