“What gets measured, gets managed,” goes the business truism. The idea applies to the design of cities and infrastructure, too. And the emergence of big data—massive sets of raw information made possible by new collection and storage technologies—is making possible new measurements that can inform how state transportation agencies plan and manage their projects.

Consider the work being done by the State Smart Transportation Initiative. Founded in 2010 at the University of Wisconsin, SSTI uses new data troves to guide real-world land use and planning decisions. By combining and analyzing data on questions ranging from how people access transit stations to how easy it is for them to get to work or the grocery store, SSTI is shedding light on patterns that can inform future decision making.

In 2018, SSTI began operating in partnership with the nonprofit Smart Growth America (SGA), whose programs include serving as a resource for state departments of transportation. SGA had collaborated with SSTI on multiple editions of The Innovative DOT: A Handbook of Policy and Practice, a guide for “for DOTs committed to innovative excellence.” The partnership now works with more than a dozen transportation agencies, functioning as a kind of policy knowledge base and providing direct technical assistance.

One key to making the most of big data is finding the right framing. “Accessibility means looking at ‘how accessible is this place?’ as opposed to ‘how fast are the cars going on a certain part of road?,’” explains SSTI Director Eric Sundquist. This more holistic approach is not a new idea, but it’s one that’s gaining momentum, partly because of richer data and more sophisticated tools for sorting it. In recent research, SSTI defined accessibility as “the ease with which people may reach opportunities such as jobs, stores, parks, schools, and other destinations. ‘Ease’ is measured in terms of travel time, with some adjustments to account for how travelers use the system.”

Among other projects, SSTI has been working with the Virginia Department of Transportation, whose Smart Scale program draws on a range of big data to “score” transportation proposals submitted by counties and municipalities on their likely ability to improve accessibility to jobs. The most recent round also incorporates access to non-work destinations such as shopping and parks.

As an example, an SSTI planning exercise focused on improving non-work-destination access in Vienna, Virginia. One track of analysis explored how beefing up a walking network and bike path could better connect the town’s main street to other neighborhoods. But another track considered a scenario that involved a shift in land use: encouraging the commercial development of an underused area on the southern edge of town. The latter actually led to higher-scoring accessibility improvements than the hypothetical transportation projects.

This scoring scheme draws on population, employment, and land use data; auto data; transit service data that’s now largely reported in a consistent format thanks to Google Maps; and bike and pedestrian data. Depending on the project, more data can be added, like job categories and neighborhood income. This opens up broader thinking about how “accessibility” can be improved, measuring whether the best option is building new pedestrian infrastructure or working to place a grocery store in a food desert.

“We’ve made people aware of this in our community of practice,” Sundquist adds, so that other DOTs can build on the same ideas. And indeed, transportation officials from Hawaii recently worked with SSTI to try to take the scoring process “a step further,” he continues. “We scored all their projects on a weighted accessibility basis. So if a project provides more access by transit in relation to auto, it will suggest how modes might shift.” The state is evaluating SSTI’s results now.

Such data represent both improvements on existing information-gathering methods and measurements that are altogether new, observes Amy Cotter, associate director of Urban Programs at the Lincoln Institute.

For example, she says, planning decisions have often relied heavily on transit survey results, which are “expensive to collect and sometimes questionable.” So the emergent technologies SSTI is harnessing—including “trip-making data” culled from services that aggregate information from GPS-enabled vehicles, navigation devices, and even smartphone apps—are an enticing alternative. “These new data are providing better information at lower cost to prepare agencies, planners, and state DOTs to make better decisions,” Cotter says.

The Lincoln Institute partnered with SSTI in a 2017 project, “Connecting Sacramento,” along with a variety of public and private entities and stakeholders. The resulting study, which catalyzed much of SSTI’s more recent work, sought to assess how these new data sources, and new tools for understanding data, could help improve transportation policy.

The Sacramento research included a case study on walking trips to and from a particular transit station. SSTI worked with traffic analytics startup StreetLight Data, which has devised methods for assessing GPS signals with machine learning to distinguish walking and biking behaviors. Walking and biking have at times “gotten short shrift” in planning efforts, says Sundquist, precisely “because they’re so hard to measure.” So adding this new information to other transportation and land use data sets can lead to new discoveries. In this case, the data pointed out an unexpectedly high percentage of foot trips between the transit station and a particular cluster of office buildings. This was surprising, given that the buildings not only had ample parking, but also were accessible on foot only by way of a single route—across a freeway. The study argued that, in light of this finding, improved or additional access points would improve conditions for current commuters and encourage more to join in.

Such analysis, of course, can often be miles ahead of the realities facing a state department of transportation. But programs like Virginia’s Smart Scale rating system suggest what big data analysis might lead to. Continuing advances in data collection and analysis should mean we will be better able to evaluate the impact of any given project, and better able to compare that to what was predicted—and adjust for the future.

The “what gets measured gets managed” cliché is sometimes used, inappropriately, to argue that what isn’t (or can’t be) measured also can’t—or even needn’t—be managed. But as Sundquist argues, these new forms of transportation data and analysis can be considered as an opportunity. They can reveal practical, actionable information. And they can also help planners, transportation managers, and others think creatively about what they wish they could measure next.

Rob Walker is a journalist covering design, technology, and other subjects. His book The Art of Noticing will be published in May 2019. 

Image: Mapping and data analysis by the State Smart Transportation Initiative can help transportation officials make more informed decisions. This map shows transit patterns in the Sacramento area. Credit: State Smart Transportation Initiative

En la última década, la impresión tridimensional fue una de las ideas con mayor resonancia en el ámbito de la tecnología. En vez de agregar tinta a un papel, una impresora 3D traduce un diseño digital en un objeto al agregar capas y capas de material (plástico, metal o concreto) mediante una extrusora guiada por computadora. Casi como un tubo motorizado de dentífrico. El proceso posee una descripción más correcta, pero menos chistosa: “fabricación aditiva”, y ha evolucionado desde los usos para prototipos veloces de las corporaciones tecnológicas y empresas de diseño a experimentos generalizados de aficionados, hackers y emprendedores, que realizan distintos objetos, desde productos de consumo hasta juguetes.

Pero ¿y si pensamos en algo más grande, como una casa?

En realidad, desde hace varios años, investigadores y emprendedores de todo el mundo han estado aplicando variaciones de la técnica para llevar a cabo proyectos del tamaño de edificios cada vez más ambiciosos. El último ejemplo corresponde a un desarrollo de cinco viviendas en Eindhoven, Países Bajos. Es un experimento ambicioso que involucra a múltiples socios, quienes lucharán no solo con las practicidades del diseño y la construcción, sino también con las regulaciones y el mercado del mundo real, dado que las propiedades se alquilarán. “Necesitamos una gran revolución en la industria de la construcción”, dijo Rudy van Gurp, gerente de proyecto de la constructora Van Wijnen Rosmalen; la aplicación de técnicas en impresión 3D podría ser parte de ella.

Algunas ventajas de esta forma de construcción, que sigue evolucionando, son el uso más eficiente de materiales, que reduce costos y minimiza el desperdicio, y el potencial de personalización. O, al menos, eso es lo que se promete, si la tecnología sigue mejorando al ritmo actual. Ese es un motivo por el cual el experimento de Eindhoven es notable, dado que es la continuación de un auge reciente de descubrimientos prototípicos relacionados entre sí.

El marzo pasado, una empresa llamada Icon, de Austin, Texas, usó su enfoque sobre la tecnología de impresión 3D para construir una vivienda moderna e intencionalmente minimalista de 32,5 metros cuadrados por USD 10.000, según lo informado, y predijo que podría abaratar el costo a USD 4.000 si sigue modificando el diseño para reducir aún más los elementos no “impresos”. La estructura se construyó de conformidad con los códigos locales de vivienda y hoy se utiliza como casa y oficina modelo. Icon concibe al proyecto como una posible solución a las necesidades de vivienda en los países en vías de desarrollo, y trabaja con la asociación sin fines de lucro New Story para llevar su enfoque a El Salvador.

Y, en julio, una familia francesa de cinco integrantes fue seleccionada para, aparentemente, ser la primera en el mundo en mudarse a una casa impresa en 3D: una vivienda de interés social no adosada, de 93 metros cuadrados, con curvas vistosas. Los organizadores de ese proyecto dicen que cuesta unos USD 200.000, lo cual, según dicen, es un 20 por ciento menos que una versión idéntica construida con los métodos tradicionales. La impresión de la estructura llevó 54 horas, pero la terminación de otros elementos no impresos, como las ventanas y el techo, demoró otros 4 meses.

Se proyecta reducir los costos y los tiempos de construcción a medida que se mejora el proceso. Sin embargo, lo que importará a largo plazo es cómo se fusiona la tecnología con los objetivos existentes de planificación urbana, y también con un razonamiento más abierto sobre el desarrollo y el uso del suelo. Ese es el otro motivo por el cual el proyecto de Eindhoven es convincente: una de las varias partes que busca explorar e influir en el futuro de la construcción es la ciudad misma.

“Me preguntaba por qué [la construcción] es un sector tan tradicional”, reflexiona Yasin Torunoglu, el vicealcalde. “Las casas y los edificios nuevos siempre se construyeron del mismo modo”. Y eso llevó a problemas prácticos en el día a día. Por ejemplo, en los Países Bajos hay escasez de albañiles experimentados, lo cual parece ser un motivo típico del s. XX para demoras en la construcción. “Me preguntaba dónde estaba la revolución [tecnológica]”, dice Torunoglu.

Al ayudar a coordinar la sociedad que impulsa el proyecto que hoy se denomina Milestone, Torunoglu quería que la ciudad se involucrara directamente para dar forma al impacto de la tecnología y a las implicaciones normativas, en vez de ir a la zaga del cambio generado por otros y reaccionar a él, como suele suceder con las disrupciones tecnológicas.

La labor incluye a otros colaboradores, como la Universidad Tecnológica de Eindhoven y la empresa de diseño Houben en Van Mierlo Architecten. La tecnología principal, desarrollada por la universidad, se usó para construir el primer puente de concreto del mundo impreso en 3D, en 2017. Una gran boquilla mecánica montada en un marco exprime concreto en cantidades precisas y con un patrón programado, para construir paredes y formas al igual que una impresora 3D, pero a mayor escala.

La última labor se desarrollará en cinco años y, al igual que cualquier impresión 3D, se construirá capa por capa. La primera vivienda es una estructura de un piso con dos habitaciones, y debería estar lista para el año próximo. La mayor parte, se realizará en el laboratorio de construcción de la universidad, y luego se transportará al sitio y se ensamblará. Las tuberías y el cableado se diagraman en los diseños impresos y se terminan en el sitio. Las viviendas subsiguientes crecen cada vez más en tamaño y ambición. El equipo aprenderá lecciones de cada experiencia para dar forma a la siguiente, desde detalles de la construcción hasta problemas de codificación. La estructura final se “imprimirá” en el sitio.

Los diseños son llamativos e idiosincráticos, y tendrán formas difusas, con curvas inconfundibles e impredecibles. Este es un resultado directo del proceso de impresión 3D. Los diseños se pueden alterar y modificar para cada casa, de modo que se puede obtener una “personalización verdaderamente masiva”, según dijo el constructor van Gurp: “Cada vivienda tendrá un aspecto diferente”. Torunoglu, vicealcalde de Eindhoven, dio un argumento similar: que el proceso podría “democratizar la industria” y ofrecer un aporte de diseño sin precedentes para los compradores de viviendas.

Por supuesto, falta mucho para eso. El proceso es “muy interesante y podría reducir muchísimo el costo de las viviendas”, dijo Armando Carbonell, miembro sénior y presidente del Departamento de planificación y forma urbana del Instituto Lincoln de Políticas de Suelo. “Pero es una posibilidad”.

Si las mejoras prometidas se cumplen, este método sería un 40 por ciento más económico que una construcción estándar, según dicen los defensores de la pujante industria. Pero, a pesar de que eso puede ser muy importante, el impacto variará. En los mercados más codiciados, como Nueva York o San Francisco, la porción de costos de vivienda atribuibles al valor territorial duplica o triplica los costos de construcción; este método sería más efectivo en esas ciudades si se demuestra que se puede construir hacia “arriba” y aumentar la densidad. En los mercados más tranquilos, como Cleveland y St. Louis, en los que la construcción altera el costo de las viviendas, las casas impresas en 3D podrían reducir drásticamente dichos costos, según indica Carbonell. Esto podría tener un impacto aun más grande en los contextos de países en vías de desarrollo.

El proyecto de Eindhoven está más dirigido a compradores más lujosos, pero aun así, podría ofrecer una ayuda importante para establecer la impresión 3D como una opción viable para la construcción, porque el éxito o el fracaso dependen de la aceptación real de los consumidores. “Aprender de este proceso es un desafío”, dijo Torunoglu, vicealcalde de Eindhoven. “Debemos colaborar con el mercado”.

Más de 100 personas se han inscrito como potenciales arrendatarios; eso es una buena señal de dicha colaboración. Ese nivel de interés no se puede fabricar.

Rob Walker (robwalker.net) es columnista de la sección Sunday Business del New York Times.

Crédito de la imagen: Houben en Van Mierlo Architects

The Colorado River Basin includes four of the eight fastest-growing states in the nation: Arizona, Colorado, Nevada, and Utah. All seven of the basin states project strong population growth over the next decade, placing pressure on a river system that is already overallocated. Water conservation, water sharing agreements, and the integration of water into land use planning will be key strategies for ensuring long-term, sustainable resource use.

View the PDF version of this map for more detail and a key.

Source: The Place Database. www.lincolninst.edu/research-data/data/place-database

The desert city of Tucson, Arizona, has an average annual rainfall of just 12 inches. But when the rain comes, it often comes in the form of torrential downpours, causing damaging floods across the city. This is a perhaps ironic challenge for Tucson and the broader Pima County area in which it is situated, given that it’s part of a much larger region working to ensure that there is—and will continue to be—enough water to go around in a time of unrelenting drought.

Both of these distinct water-management challenges—too dry and too wet—can be addressed by thoughtful land use and infrastructure decisions. Of course, when making such decisions, it helps to have precise mapping data on hand. That’s why Pima County officials are working with the Lincoln Institute’s Babbitt Center for Land and Water Policy and other key partners to pilot the use of some of the most cutting-edge mapping and data analysis tools on the market.

For the Babbitt Center—founded in 2017 with the mission of providing land-use research, education, and innovation to communities throughout the Colorado River Basin—the partnership represents one early step in exploring how such technology can be used to help integrate water and land use management across the region.

The technology itself originated across the country, at the Conservation Innovation Center (CIC) of Maryland’s Chesapeake Conservancy, a key player in cleaning up the notoriously pollution-addled Chesapeake Bay. To oversimplify a bit: CIC has designed image analysis algorithms that provide distinctly more granular image data of the earth’s surface. The technology has enabled a shift from a resolution that made it possible to observe and classify land in 30-meter-square chunks to a resolution that makes that possible at one square meter.

The details are of course a little more complicated, explains Jeffrey Allenby, the Conservancy’s director of conservation technology. Allenby says the new technology addresses an historic challenge: the compromise between resolution and cost of image collection. Until relatively recently, you could get 30-meter data collected via satellite every couple of weeks or even days. Or you could get more granular data collected via airplane—but at such a high cost that it was only worth doing every few years at most, which meant it was less timely.

What’s changing, says Allenby, is both the camera technology and the nature of the satellites used to deploy it. Instead of launching a super-expensive satellite built to last for decades, newer companies the CIC works with—Allenby mentions Planet Labs and DigitalGlobe—are using different approaches. “Smaller, replaceable” satellites, meant to last just a couple of years before they burn off in the atmosphere, can be equipped with the latest camera technology. Deployed in a kind of network, they offer coverage of most of the planet, producing new image data almost constantly.

Technology companies developed this business model to respond to commercial and investor demand for the most recent information available; tracking the number of cars in big-box store parking lots can, in theory, be a valuable economic indicator. Land use planners don’t need images quite that close to real time. But Allenby says the CIC began asking the tech companies, “What are you doing with the imagery that’s two weeks old?” It’s less expensive to acquire, but far better than what was previously available. The resulting images are interpreted by computers that classify them by type: irrigated land, bedrock, grassland, and so on. Doing that at a 30-square-meter level required a lot of compromise and imprecision; the one-meter-level is a different story.

The goal is to “model how water moves across a landscape,” as Allenby puts it, by combining the data with other resources, most notably LIDAR (Light Detection and Ranging) elevation data. Those are the “flour and eggs” of land use data projects, supplemented with other ingredients like reduction efficiencies or load rates from different land cover, depending on the project, Allenby says: “We’re building new recipes.” For Chesapeake Bay, those recipes are meant to help manage water quality. If you can determine where water is concentrating and, say, taking on nitrogen, you can deduce the most cost-effective spot to plant trees or place a riparian buffer to reduce that nitrogen load. (See “Precision Conservation,” October 2016 Land Lines.)

In the Colorado River Basin, the most urgent current water-management challenges are about quantity. Since water policy is largely hashed out at the local level despite the underlying land use issues having implications across multiple states, the Babbitt Center serves as a resource across a broad region. There’s currently a “heightened awareness” of water management among municipal and county policy makers, says Paula Randolph, the Babbitt Center’s associate director. “People are wanting to think about these issues and realizing they don’t have enough information.”

That brings us back to Pima County. Although it lies outside the basin, it boasts two features that make it a good place to evaluate how the uses of precision mapping data might be applied in the West: Basin-like geography and proactive municipal leaders. When the manager of technology for the Pima Association of Governments saw Allenby speak about the benefits of his work in the East, he contacted the CIC to discuss possibilities for the West. A year into the resulting project, several partners are on board, the group is mapping a 3,800-square-mile area, and the open-source data lives on the Pima Regional Flood Control District website, where others throughout the county are able to access and use it.

Broadly, this process has taken some effort, Randolph notes. Satellite data gathered in the West has different contours than the East Coast imagery that Chesapeake’s sophisticated software was used to, and that has required some adjustment—“teaching” the software the difference between a Southwestern rock roof and a front yard that both look (to the machine) like dirt. “We need human partners to fix that,” she says. “We strive for management-quality decision-making data.”

Even as such refinements continue, there are already some early results in Pima County. Clearer and more precise data about land cover is helping to identify areas that need flood mitigation. It has also been useful to identify “hot spots” where dangerous heat-island effects can occur, offering guidance for mitigation actions like adding shade trees. These maps provide a visual showcase about water flow and land use more efficiently than a field worker could.

Both Allenby and Randolph stress that this partnership is still in the early phases of exploring the potential uses and impacts of high-resolution map data. Randolph points out that while the Babbitt Center is working on this and another pilot project in the Denver area, the hope is that the results will contribute to a global conversation around water-management experimentation.

And Allenby suggests that the “recipes” being devised by technologists, policy makers, and planners will ideally lead to a shift in more accurately evaluating the efficiency and impact of various land use projects. This, he hopes, will lead to the most important outcome of all: “Making better decisions.” 

The Lincoln Institute has provided occasional financial support to the CIC for map- and data-related projects.

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

Image: High-resolution land cover data offers a closer look at Tucson, Arizona. Credit: Chesapeake Conservancy.