An operator checks the electric power control panel for the smart city project in Kashiwa, Japan, on July 7, 2014. By 2030, the world's population is projected to be 8.5 billion and increase to 9.7 billion by 2050 and 11.2 billion by 2100. Many cities are experiencing exponential growth as people move from rural areas in search of better jobs and education. Consequently, cities' services and infrastructures are being stretched to their limits in terms of scalability, environment, and security as they adapt to support this population growth. Visionaries and planners are thus seeking a sustainable, post-carbon economy20 to improve energy efficiency and minimize carbon-emission levels.
Cormenzana, Berta (BCN Ecologia) | Fabregas, Ferran (BCN Ecologia) | Marinescu, Maria-Cristina (Barcelona Supercomputing Center) | Marrero, Monica (Barcelona Supercomputing center) | Rueda, Salvador (BCN Ecologia) | Uceda-Sosa, Rosario (IBM Research)
As the complexity and abundance of city data increases, reusable semantic models that can integrate heterogeneous data sources in a lightweight manner enable a holistic view of the city data, which is key to Urban Ecology. Our multi-disciplinary team has built an ontology for Urban Ecology that not only captures a field-validated urban model and certification process, but also explores the reuse of semantic models and their interaction with domain experts.
Why do certain neighborhoods feel safe while some feel dangerous? Why are others considered beautiful? How do cities develop and change over time? And most importantly, how can we quantify these observations about the way we perceive cities, and use it to plan urban areas that are more equitable? César Hidalgo, the director of the Collective Learning group at the MIT Media Lab, has spent years using crowdsourced data and machine vision technology to build models of cities that can answer questions that statistics and surveys simply can't.
Machine learning is helping urbanists confirm–or disprove–long-standing theories about cities. Why do certain neighborhoods feel safe while some feel dangerous? Why are others considered beautiful? How do cities develop and change over time? And most importantly, how can we quantify these observations about the way we perceive cities, and use it to plan urban areas that are more equitable?
Suddenly, the world is awash in smart cities. Google is about to build one in Toronto. Hudson Yards in New York City is one, kind of. China and India have both declared national missions to construct or retrofit dozens of smart city projects all at once. And global tech giants and startups are racing to offer customers urban dashboards, "smart'' systems, and new-age transit networks to make life happier and more efficient. But no one seems able to agree on what a smart city actually is.