In this talk, we will review three machine learning applications to address city science challenges. The way we live and work is undergoing major upheaval. The digital revolution is changing the kind of work we do and the way we do it. More people are choosing to live in urban environments. At a global scale, we see the warning signs of our inability to navigate these changes. Growing income inequality threatens to destabilize our communities. Automation technologies, including artificial intelligence and robotics, threaten to displace people from the jobs that they rely on to cover their basic needs.
To be able to properly address such challenges, the cities of the future will need to be designed to facilitate the access of the majority of the population to attractive and fairly compensated employment opportunities. The physical urban environment has significant effects on the social and organizational interactions within cities. Understanding the impact of variables such as location, city design, connectivity, industry concentration, and urban infrastructure on the economy is paramount. How should we design our cities to contribute to a merit-based knowledge economy characterized by distributed prosperity?
The Economic Complexity model developed by César Hidalgo, Albert-László Barabási, and Ricardo Hausmann presents an original and rigorous methodology to describe the collective knowhow of countries, as well as an accurate growth and income inequality predictor at the national level. However, its main limitation is that the majority of decision-making impacting the economy is made at different sub-national levels. Harvard Researchers Jeremy Burke and Ramon Gras have developed a methodology to describe collective knowhow at the urban scale, at three different geographic scales.
Ramon Gras is an Urban Innovation Researcher and City Designer from Barcelona. Ramon is co-founder of the Harvard Urban Innovation study group, focused on deploying Complexity Science and Network Theory principles to analyze and illuminate the dynamics behind urban innovation eco-systems, by means of AI and Machine Learning methodologies. His current research focuses on urban design criteria for Innovation Districts, analyzing Economic Complexity dynamics at the urban scale, designing smart specialization strategies for metropolitan regions, and developing data-driven strategic decision-making policies to boost economic prosperity by means of knowledge-intensive urban development, high quality urban design, and infrastructure investment prioritization. Ramon Gras alongside fellow researcher and startup Co-Founder Jeremy Burke have recently published the very first Atlas of Innovation Districts, a white paper featured on MIT Technology Review, describing some of the key insights of their joint research thesis at Harvard. Ramon is also Co-Founder of the startup Aretian, incubated at the Harvard Innovation Lab.
At Harvard, Ramon graduated from the inaugural cohort of the Harvard MDE program (GSD & SEAS), where he developed a thesis around design criteria for innovation districts operating in synergy with high value-added logistics hubs. In the context of his Thesis, Ramon analyzed the top 50 Innovation Districts in the US, in cities such as NYC, San Francisco Bay Area, Boston, Los Angeles, Austin, Chicago, Pittsburgh, and Philadelphia.
Prior to developing his joint thesis at Harvard, Ramon worked at Ferrovial’s Innovation office in London, where he led design and technology projects at the London Heathrow Airport and the London Underground. Before his experience in London, Ramon’s thesis at MIT addressed the consolidation problem in air freight transportation by designing an advanced data science optimization model and Business Intelligence platform; the solution was subsequently adopted by a world class leading 3PL corporation. He expanded his training at MIT after working as a designer in major infrastructure projects involving bridge design, maritime infrastructure, high speed rail, and architectural design (a Richard Rogers project). Ramon’s early research at BarcelonaTech focused on bridge design, high performance materials, and nanotechnology applications for structural engineering. Ramon is interested in enhancing innovation around Cities, Technology and Infrastructure, by designing creative and rigorous interdisciplinary solutions to address large, complex challenges facing the cities of the future.
Ramon is MDE by Harvard, MEng in Systems Engineering by the Massachusetts Institute of Technology (MIT), and MSc Civil Engineer by UPC-BarcelonaTech.