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A Common Economic Language for Development

Transportation and land-use agencies often don't work with the same data as economic development offices. A new tool aims to bridge that gap.

Train speeding by a bus in Portland, Ore.
Rail and bus transit move through Portland, Ore. The urban planning tool Replica paints a detailed picture of how the transportation system is used in a metro region.
In an age of data and disruption, cities and regions need modern tools to visualize how their economies operate across their landscapes. That reality was made quite clear during the recent Amazon HQ2 sweepstakes, in which the company defined a region's competiveness not just by its traditional economic assets, such as tech talent, but also by how well transit connectivity and neighborhood livability created a platform for long-run economic growth.

The problem is that most transportation and land-use agencies struggle to frame their decisions through an economic lens. The de facto standard is that those agencies measure such items as travel volumes and acres of developable land, while leaving questions such as where income inequality may be growing or where high-tech firms have begun to cluster to economic development offices. This narrow approach creates a major weakness: If transportation and land-use staff don't have a way to translate economic values into their operational DNA, how can we ever expect to build the kind of places we all want to live in?

One way to bridge this data gap is to build new tools that create a common language. Call it a Rosetta Stone between economic development professionals and their peers in land use and infrastructure.

Over the past 18 months, the Brookings Institution's Metropolitan Policy Program and Metro, the regional government and planning organization serving greater Portland, Ore., set out to build such a translational tool. The result is the country's first Economic Value Atlas (EVA), which uses mapping technology to simultaneously evaluate economic, social and land-use conditions at the neighborhood scale and relate them to metropolitan trends. It's a solution that could scale to any metropolitan area.

It's not hard to see how land-use and transportation decisions impact economic competiveness. Domestic and global trade connections are essential to allow industries to grow. Commuting choices and local walkability help attract new talent to a region. A range of housing types is essential to hedge against displacement and discrimination. Sustainable urban design better positions a region to withstand threats from climate change. And history tells us that getting these decisions wrong can have detrimental effects, from promoting decay in the urban core to spatial mismatch on the periphery.

Not only are our policy frameworks not designed for that kind of multidisciplinary thinking, but they also fail to leverage impressive new data capabilities. The country has never had a better feel for how metropolitan economic performance compares across places. Economic data at the neighborhood scale is also richer than ever, whether through federal sources such as the Census Bureau or private providers such as real-estate firms.

By providing a common mapping platform that is available to the public and can make calculations in seconds, the EVA creates a common economic language to inform local conversations. And we're just now beginning to see what's possible.

For example, the EVA can manage questions around the rise of e-commerce and where to put all those new warehouses. By stacking three critical variables at once -- freight market connectivity, labor access and developable industrial land -- the EVA map of Portland revealed hotspots especially attuned to warehousing's needs. What amazed us is that the EVA pinpointed the exact neighborhood where Amazon ended up building its Portland facility: a logistics- and land-rich area with easy access for entry-level workers.

Or let's consider gentrification, which is a major issue in Portland's central-city neighborhoods. We decided to stack five variables: rental housing affordability, housing construction, walkability, median income growth and high non-white populations. Not only did the map reveal where gentrification may have already occurred but it also revealed diverse, livable places that, due to housing construction and rising incomes, could soon price certain groups out.

These maps are intriguing, but what's most exciting is how the tool's outputs can impact local decision-making. Consider, for example, the landmark affordable-housing bond measure approved by the Portland region's voters last year. A tool like the EVA can help Metro target funding by geographies, populations and community needs. Similarly, as the region evaluates where a future transportation funding measure should invest its precious capital, the EVA can help policymakers better understand and communicate which investments provide which benefits. That kind of economic accountability can help build public trust.

We can no longer afford the outdated model of making decisions in silos and behind closed doors. Fortunately, we have new data and planning capabilities to meet not only today's challenges but also those of the coming decades. Tools like the EVA are just the beginning.

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