• Sam Zegas

It's time for a new science of cities

Updated: Jun 26, 2020

This article was published as an op ed in the MIT Technology Review in Spanish on August 7, 2019. The original article can be viewed here.

Cities around the world face common challenges. On issues from adapting to climate risks to achieving sustainable growth and equitably-distributed economic prosperity, our cities are the economic and political engines we need to tackle the world’s biggest problems. Yet our understanding of how cities function and how to harness them to address big problems is still based on inexact science.

Why is it difficult to understand exactly how cities function? The answer is complexity. Cities are among the most complex systems that humans create. They are dense environments where dozens of major infrastructure systems interact with each other at different scales, where people and objects circulate continuously, and where idiosyncratic economic and political forces operate with limited visibility. It is no surprise that we do not yet have an exact scientific understanding of cities. They are simply too complex for traditional analytical methods to grasp without drastic simplification.

Today, we stand at the frontier of new scientific approaches that will give us novel perspectives on how cities work. It’s time for a new scientific theory of cities.

The promise of new scientific approaches to understand cities lies in a combination of rapidly-evolving disciplines. The Internet of Things and the rise of Smart Cities are providing more data than ever before. Complexity science offers novel perspectives on how to conceptualize and model vastly complex environments like cities. Network theory provides fresh approaches for understanding the nature of connections between the components of complex systems. Machine learning allows us to extract clear patterns lessons from chaotic data and synthesize the results into actionable recommendations for planning and design. Together, these approaches empower us to understand cities as never before possible: in all their complexity.

Our own entry into this work began with a joint master’s thesis project in Design Engineering at Harvard University. The project focused on Innovation Districts and led to the creation of the first-ever Atlas of Innovation Districts, through which we conducted a deep study of how innovation ecosystems work. The work led us to found Aretian | Urban Analytics and Design, a Harvard-affiliated startup dedicated to improving lives through powerful new techniques in analytics and design of cities.

The conclusion of our study of Innovation Districts is that innovation, which has long been treated as an inscrutable black box, is actually discrete, analyzable, and within our power to understand and control. We believe that the study’s general conclusions suggest exciting applications for policy makers, developers, and urban designers. But more importantly, we believe that there are many potential applications for the methods that produced this study of innovation. From evaluating climate risk to planning civil infrastructure, and from optimizing food systems to mapping social vulnerability, society faces many challenges could benefit from complex system modeling and advanced analytics.

In publishing the Atlas of Innovation Districts, we hope to provoke a public conversation about how a new scientific theory of cities, driven by data science and complexity modeling, might improve the lives of people everywhere. We also hope to collaborate with others to expand the Atlas to other regions of the world. Please join us.

MIT Technology Review

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