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

Para implementar un servicio de bicicletas compartidas que tenga un impacto real sobre el transporte metropolitano en general, hay que construir primero un buen sistema de estaciones de anclaje. 

Hace falta “una red densa de estaciones en toda el área de cobertura”, aconseja la Guía de planificación de sistemas de bicicletas compartidas, publicada por el Instituto de Transporte y Política de Desarrollo. “La utilidad de los sistemas de bicicletas compartidas con anclaje depende de la presencia de una red de estaciones casi continua”, concuerda el Juego de Herramientas de Movilidad Compartida, creado por el Centro de Movilidad de Uso Compartido (Shared-Use Mobility Center), “y la construcción de la red es una tarea bastante intensiva en capital y mano de obra”. El proceso también requiere una planificación cuidadosa para colocar las estaciones en los lugares más efectivos y no generar efectos secundarios negativos sobre el entorno edificado. 

Pero, ¿si se pudiera construir un sistema de bicicletas compartidas sin necesidad de estaciones, como algunas empresas nuevas están tratando de hacer en algunas ciudades principales de China? Un ejemplo de alto perfil es mobike, que se lanzó el año pasado y ya tiene una flota de decenas de miles en Beijing. Su director ejecutivo es un veterano de las operaciones de Uber en Shanghái, y cuenta con más de $100 millones de dólares en inversiones de firmas financieras como Sequoia Capital y Warburg Pincus. 

El método de mobike depende en gran medida de su app original para teléfonos inteligentes y la tecnología incorporada al diseño patentado de la bicicleta. Lo más significativo es que las bicicletas no necesitan una estación de anclaje ni tampoco una base de estacionamiento. En vez de eso, están equipadas con un candado especial en la rueda trasera, o sea que los usuarios teóricamente las pueden dejar en cualquier lugar, salvo al interior y en otros pocos lugares. Para ubicar una bicicleta disponible, los usuarios consultan la app del servicio, que presenta un mapa que usa tecnología de GPS para ubicar la mobike más cercana disponible; pueden hacer la reserva con la app para asegurarse que nadie la saque primero. La app también genera un código de barras QR que se usa para abrir el candado. 

La compañía es demasiado nueva para poder juzgar su desempeño, y también tiene competencia, incluyendo otra empresa sin estaciones llamada ofo. Pero su modelo sin estaciones puede ser tan intrigante desde la perspectiva de planificación como desde el punto de vista del consumidor. 

Zhi Liu ha estado siguiendo el desarrollo de programas de bicicletas compartidas en China por muchos años. Trabajaba anteriormente en el Banco Mundial, donde se concentró en parte en temas de transporte urbano. Liu es ahora director del programa de China en el Instituto Lincoln de Políticas de Suelo y el Centro de Desarrollo Urbano y Políticas de Suelo de la Universidad de Pekín/Instituto Lincoln en Beijing. Señala que es importante comprender el contexto que dio lugar a estas nuevas empresas. 

China tiene un largo historial de ciclismo. Pero hasta para los dueños entusiastas de bicicletas, las calles mal cuidadas y el enorme tráfico dificultan el uso de bicicletas para recorrer grandes distancias a y desde el trabajo en las ciudades modernas de China. Por eso, cuando las alternativas de bicicletas compartidas emergieron en algunas ciudades en 2008, como complemento del metro y el autobús, la idea fue adoptada rápidamente. En 2011, el 12.^o Plan Quinquenal de Transporte Nacional alentó explícitamente a los centros urbanos que desarrollaran sistemas de bicicletas compartidas como suplemento útil para los sistemas de transporte público existentes. 

“Los planificadores y gobiernos municipales en la actualidad consideran que las bicicletas compartidas son un componente clave del transporte público”, explica Liu, “porque ayuda a resolver el problema de la asi llamada ‘última milla’”. Es decir: Uno usa el transporte público, llega a una estación, y todavía falta otra milla más para llegar al destino final. 

Los programas de gobierno en China no tienen el mismo problema de uso del suelo que puede ocurrir en una ciudad de los EE. UU., porque el suelo urbano es propiedad del estado. Pero tienen otros problemas persistentes. En 2011, cuando una conferencia del Banco Mundial se enfocó en las experiencias nacionales e internacionales de sistemas de bicicletas compartidas, las discusiones más importantes fueron sobre “gestión y sostenibilidad”, dice Liu. “¿Qué modelo de negocios es el más apropiado?” 

Lo que surgió fue una mezcla de soluciones. En Hangzhuo, un modelo impulsado por el gobierno creó una compañía estatal que en la actualidad es presuntamente el sistema de bicicletas compartidas más grande del mundo. Otras ciudades han experimentado con varios híbridos públicos/privados, buscando un equilibrio para que el servicio sea lo suficientemente barato para atraer a los usuarios pero suficientemente redituable como para cubrir los costos. 

Las últimas iniciativas son empresas como mobike y ofo, ambas operando en otras ciudades chinas. Sin duda tienen que encontrar el mismo equilibrio económico. Pero, quizás porque están muy bien financiadas, cada una parece concentrarse más por el momento en generar clientes y aceptación. 

Ofo se concentra abiertamente en los estudiantes, usando bicicletas más livianas con candados de combinación, un sistema de distribución centrado en la universidad y un depósito muy bajo (13 yuan, o alrededor de $2). Mobike se interesa más por los profesionales urbanos y/o entusiastas del ciclismo. El depósito es de 299 yuan (un poco menos de $50); el alquiler cuesta 1 yuan por cada media hora. Sus bicicletas son más pesadas pero también durables y distintivas. “Escucho a mucha gente hablar sobre el tema”, dice Hongye Fan, consultora del Banco de Desarrollo Asiático con asiento en Beijing, y gerente de inversión de China Metro Corporation, quien ha hecho un seguimiento de los programas de bicicletas compartidas. “Es un modelo innovador en China y se está difundiendo muy rápidamente”. 

Fan, que anteriormente era consultora de infraestructura financiera y gestión de activos en el Banco Mundial, señala algunos de los efectos secundarios más intrigantes de los modelos sin estaciones. La puesta en marcha de un sistema de bicicletas compartidas puede ser, necesariamente, un proceso muy estructurado que no deja mucho lugar para realizar modificaciones una vez que se hayan construido las estaciones; o, como dice Fan, “no permite pensar y analizar realmente: ¿Cuál es la verdadera demanda de los ciudadanos?” 

Un sistema de bicicletas compartidas es una respuesta útil al problema de la última milla, continúa, pero “no hay una última milla universal”. De hecho, una estación ubicada en un lugar fuera de la ruta acostumbrada de un usuario en particular puede convertir esa última milla en una milla y media. Un sistema parecido al de Uber o Zipcar, que se adapta más abiertamente a la demanda, podría evitarlo. 

Y hay por lo menos algunos experimentos similares en otros lugares. Un ejemplo contrastante es el sistema AirDonkey de Copenhague, esencialmente una plataforma basada en app que permite a los dueños de bicicletas (incluyendo, notablemente, tiendas de bicicletas), alquilar sus bicicletas a otros. Esta empresa naciente espera que su modelo funcione en otras ciudades, incluso aquellas con un sistema tradicional de bicicletas compartidas. 

Por supuesto, estos modelos crean otros problemas y barreras. Mobike ha tenido problemas con robos, lo cual probablemente sucedería en casi cualquier lugar del mundo, si bien la compañía ha dicho que es un problema que se puede contener. Además, el modelo gobernado por demanda podría crear un agrupamiento de bicicletas en lugares de destino muy populares en vez de puntos de origen, lo cual quiere decir que se tendrían que redistribuir físicamente. 

Y, como apunta Fan, la planificación sigue jugando un papel crucial en la resolución de problemas que estas empresas no pueden abordar, como el diseño y construcción de una infraestructura apropiada para que el viaje en bicicleta sea práctico y seguro, como carriles exclusivos para bicicletas. Pero esto es cierto en todos lados. Los programas de bicicletas compartidas han proliferado mucho en años recientes (África acaba de lanzar su primer sistema en Marrakech) y con aproximadamente 600 sistemas alrededor del mundo, las estrategias de financiación e implementación varían de un lugar a otro. “No hemos encontrado ningún modelo en particular que se adapte a todas las ciudades”, dice Liu. 

En realidad, probablemente nunca encontraremos una solución universal. Y eso es precisamente la razón por la que mobike y otros modelos novedosos que se están introduciendo en China, el país con más sistemas de bicicletas compartidas del mundo, son importantes. La explotación de innovaciones tecnológicas de manera astuta ofrece nuevas rutas potenciales de interés a seguir. Veremos si otros se aferran a estas ideas y las aplican para ver adónde llevan.

Rob Walker (robwalker.net) es colaborador de Design Observer y The New York Times.

Fotografía: ofo

Implementing a bike-sharing service that has a real impact on city transportation usually means, among other things, getting the underlying system of docking stations right.

You’ll need a “dense network of stations across the coverage area,” advises The Bike-share Planning Guide, published by the Institute for Transportation & Development Policy. “The utility of dock-based bike-sharing systems depends on the presence of a fairly continuous network of stations,” agrees the Shared Mobility Toolkit, from the Shared-Use Mobility Center, “and building the network is a relatively capital- and labor-intensive task.” The process also requires careful planning to make sure the stations are arranged in the most effective locations—and that they don’t have negative side effects on their built environs.

But what if you could build a bike-share system with no stations at all, as some new enterprises in China are trying to do in a handful of major cities? One high-profile example is mobike, which launched last year and already has a fleet in the tens of thousands in Beijing. Its chief executive is a veteran of Uber’s operations in Shanghai, and it is backed by more than $100 million in investments from financial firms such as Sequoia Capital and Warburg Pincus.

Mobike’s approach relies heavily on its unique smartphone app and technology built into the bike’s patented design. Most significantly, the bikes don’t need a docking station or even a parking dock. Instead they are equipped with a special locking mechanism on the back wheel, meaning users can theoretically leave them almost anywhere except indoors and a few other locations. To locate an available bike, users consult the service’s app, which presents a map that uses GPS technology to point out the nearest available mobikes; you can reserve one through the app to make sure nobody else snags it first. The app also generates a QR code that’s used to unlock the cycle.

The company is still too new to be fully proven, and it faces competition—including another dock-free enterprise called ofo. But its stationless model may be as intriguing from a planning perspective as from a consumer’s point of view.

Zhi Liu has tracked the development of bike-share programs in China for years. Formerly with the World Bank, where he focused in part on urban transportation issues, Liu is now director of 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. He notes that it’s important to understand the context in which these new businesses evolved.

China has a long history with cycling. But even for enthusiastic bike owners, rough and heavily trafficked roads make for a challenging long-distance commute in modern Chinese cities. So when bike-sharing schemes emerged in a few cities around 2008, as a complement to metro and bus options, the idea was quickly embraced. In 2011, the National Transport 12th Five Year Plan explicitly encouraged urban centers to develop bike-sharing as a useful addition to existing mass-transit systems.

“Planners and municipal governments now consider shared bikes a key component of public transport,” Liu explains, “because it helps solve the problem of the so-called ‘last mile.’” That is: You use public transport, and arrive at a station—and you still have another mile to reach your real destination.

Government programs in China didn’t face the same land-use challenges that might arise in a U.S. city, because urban land is state-owned. But other challenges persisted. By 2011, when a World Bank conference focused on domestic and international experiences with shared bikes, the major discussion was around “management and sustainability,” Liu says. “What business model makes sense?”

A mix of solutions emerged. In Hangzhuo, a government-led model involved setting up a state-owned company; today this is reportedly the largest bike-sharing system in the world. Other cities have experimented with various public/private hybrids, searching for a balance that would make bike-sharing cheap enough to attract users but profitable enough to cover costs. 

The latest wrinkle is businesses such as mobike and ofo, both of which also operate in other Chinese cities. These will clearly need to find that same economic equilibrium. But, perhaps because they’re both lavishly funded, each seems more focused for the moment on building ridership and acceptance.

Ofo overtly targets students, using lighter bikes with combination locks, university-centric distribution, and a very low deposit (13 yuan, or about $2). Mobike’s target is more likely to be an urban professional and/or cycling enthusiast. The deposit is 299 yuan (a little less than $50); rental is 1 yuan per half-hour. Its cycles are heavier but also more durable and distinct. “I do hear a lot of people talking about it,” says Hongye Fan, a Beijing-based consultant for the Asian Development Bank and investment manager for China Metro Corporation who has tracked bike-share programs. “It’s an innovative model in China and spreading very fast.”

Fan, previously an infrastructure finance and asset management consultant at The World Bank, points out some of the more intriguing side effects of the stationless models. Rolling out a major bike-sharing system can be, by necessity, a top-down process that doesn’t leave much room for flexibility once dock locations are built out—or, she notes, for “really thinking about and analyzing: What is the real demand from the citizens?”

Bike-sharing is a useful response to the last-mile problem, she continues, but “there is no universal last mile.” In fact, a station fixed in a spot that’s out of a particular user’s way could turn the last mile into the last mile and a half. An almost Uber- or Zipcar-like system that’s more overtly shaped by demand could avoid that.

And there are at least some experiments along similar lines elsewhere. A striking example is Copenhagen-based AirDonkey, essentially an app-based sharing platform that allows bike owners (including, notably, bike shops) to rent out their cycles to others. The startup hopes its model can work in other cities, even those where traditional share systems are in place.

Of course, such approaches involve other challenges and hurdles. Theft has been an issue for mobike, as it would surely be in almost any city in the world, although the company has said it’s a containable problem. Also, the demand-driven model could mean lots of bikes end up clustered in spots that are more popular as destinations than as starting points—meaning they’d have to be physically redistributed.

And, as Fan points out, planning would still play a crucial role in addressing problems that startups can’t—like designing and ensuring proper infrastructure, such as bike lanes, that makes bike riding safe and practical. But that’s true everywhere. Bike-share programs have proliferated wildly in recent years—Africa just launched its first, in Marrakech—and with an estimated 600 systems in place around the world, funding and implementation strategies vary. “We have not found any particular model that fits all cities,” Liu says.

Truth is, we probably never will find a universal solution. And that’s precisely why mobike and other new models—taking shape in China, the country with the most extensive bike-sharing systems anywhere—matter. Exploiting tech innovations in clever ways offers some compelling new potential routes to follow. Let’s see whether others take these ideas for a spin and where that leads. 

Rob Walker (robwalker.net) is a contributor to Design Observer and The New York Times.

Photograph: ofo