Senseable City Lab :.:: Massachusetts Institute Of

Senseable City Lab :.:: Massachusetts Institute of Technology This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the deﬁnitive publisher-authenticated version, please refer directly to publishing house’s archive system SENSEABLE CITY LAB Designing cities within emerging geographies: the work of Senseable City Lab In: Banerjee, T. & Loukaitou-Sideris, A. (2019). The new companion to urban design. London: Routledge. (pp. 561-570)1 Fábio Duarte2 and Carlo Ratti3 Abstract Urban data is an old dream of architecture and planning. Ildefonso Cerdá, the father of modern Barcelona, was one of many who dreamt of a more quantitative urbanism, grounding his proposals in quantitative descriptions and analysis of the city. In a similar way, during the second half of the 20th century, urbanist William H. Whyte used on-site cameras to capture human flow inside New York’s buildings and public spaces. His methods were insightful but labor intensive. Today, with the diffusion of handheld electronics, such as cell phones and smartphones, data collection is becoming effortless. With more than 7 billion mobile devices worldwide, and 2.3 billion mobile-broadband subscriptions globally, cities in both the Global North and Global South are experiencing new forms of understanding urban phenomena and informing city design. Moreover, global telecommunications and faster computational power are closing the temporal gap between data gathering, data processing and analysis, and actuation cycles. In this chapter we focus on one technology, cell phones, and present a review of the use of cell phone data for urban planning applications, based on the Senseable City Lab’s work—from our initial experiments to the present day. In 2006, we took aggregated data from cell phones in Rome and mapped these calls onto the geography of the city during two special events in the summer, revealing the emotional landscape of the city. In the following years, similar projects emerged, pointing out to dynamic and collaborative mapping. Ten years later, we used cell phone data in New York to better understand a pressing environmental health concern: human exposure to air pollution, which leads to 7 million early deaths each year globally. Mapping the movements of several million people using cell phone data, and intersecting this information with neighborhood air pollution measures, the study reveals where and when New Yorkers are most at risk of exposure to air pollution. These projects, among other discussed in this chapter, demonstrate how the knowledge of human movement could inform design. If the built environment is a kind of “third skin” — after our biological one and clothing — it has long been a rigid one. Perhaps with better data, the built environment can adapt to us: a living, tailored architecture and urban form that is molded on inhabitants. Introduction Urban data is an old dream of architecture and planning. Ildefonso Cerdá, the father of modern Barcelona, was one of many urbanists who dreamed of a more quantitative urbanism. Indeed, Cerdà grounded his proposals on quantitative descriptions, scientific studies, and statistics about the physical and social aspects of the city. In a similar way, in the early 20th century other urbanists, such as Patrick Geddes, based their pursue to establish town and regional planning as a scientific discipline on data gathered at multiple scales; and during the second half of the 20th century, urbanist William H. Whyte used on-site cameras to capture human flows in New York’s buildings and public spaces. By extensively 1 [This is the version prior to the publisher formatting] 2 Fábio Duarte is research scientist at the MIT Senseable City Lab, and professor at the Pontifícia Universidade Católica do Paraná, Brazil. 3 Carlo Ratti is professor of the practice and director of the MIT Senseable City Lab. 561 monitoring how people use public spaces, Whyte wanted to understand whether and which physical elements and personal interactions induced different behaviors in public spaces. His work showed how quantitative data could reveal some of the intangible qualities of space and inform design. However, Whyte’s methods were insightful but labor intensive; in the age of big data, this might change, opening up unprecedented potential to urban planning and urban design. More than a decade ago Howard Rheingold (2002 p. 86) noted that "[t]he kind of world we will inhabit for decades to come could depend on the technical architecture adopted for the emerging mobile and pervasive infrastructure over the next few years". Since Rheingold's remarks, global communication networks, the miniaturization of electronics, and the vertiginous increase in data storage and processing speed have made digital technologies pervasive, productive, and powerful. The vital component that makes such technologies powerful, is the generation and traffic of data from different sources that can be combined through multiple technologies. Indeed, the generation of huge amounts of data in real time and their fine-grained spatial and temporal resolution is beginning to play a major role in planning and urban design. In 1995 Michael Batty (1995) proposed the vision of the “computable city.” With the refinement of computer and digital technologies, this vision became what Rob Kitchin and Martin Dodge (2011) referred to as “programmable urbanism” —that is, our dynamic ability to sense a city’s activity, understand its changes and fluctuations, and deliver tailored responses to meet the needs of its urban environment. This is partially due to a dimensional shift: we are now able to collect massive amounts of data of how urban activities change over time, whereas previously space was generally perceived as a fixed, or very slowly changing aspect of urban form. As William Mitchell (2002, p. 144) pointed out, "[b]y selectively loosening place-to-place contiguity requirements, wired networks produced fragmentation and recombination of familiar building types and urban pattern". Thus, we argue that the temporal dimension of urban life – urbanism, that is — is becoming a crucial outcome of contemporary urban design. These changes have important consequences to urban planning and design. Until recently, urban planning was mostly concerned with large spatial and temporal scales. As Michael Batty (2012) suggests, the little concern urban planning had for small-scale developments was partially due to the lack of available data. One might argue that the focus on comprehensive and long-term master plans, often based on coarse data updated every few years, which for long have characterized urban planning, was less an intrinsic characteristic of the field, and more due to the lack of more detailed and dynamic data. The challenge urban scholars and designers face now is to find novel ways to integrate short-term big data with urban planning and design concerns and strategies. Today, with the diffusion of handheld electronics, such as cell phones and smartphones, data collection is becoming effortless. With more than 7 billion mobile devices worldwide, and 2.3 billion mobile-broadband subscriptions globally, cities in both the Global North and Global South are experiencing new forms of understanding urban phenomena, of creating social interactions, and proposing the future of cities. Moreover, global telecommunications and faster computational power are closing the temporal gap between data gathering, data processing and analysis, and actuation cycles. In this chapter, we focus on one particular technology — movement and location sensing technologies embedded in personal mobile devices. We present a review of the use data collected by cell phones in the work of MIT’s Senseable City Lab. We report some of our initial experiments that included: using cell phone data to map people's quotidian flows in European cities, tracking discarded objects at a global scale, and predicting people's exposure to pollutants according to their daily movements in New York City. We group these cellphone-based movement and location sensing technologies into two categories: opportunistic sensors, and purpose-built sensors. Sensors are embedded in an increasing number of 562 devices we use daily, and many of these sensors collect data from the natural and built environment, and from human interactions without people's direct action. These are what we call opportunistic sensors. Another group of sensors are designed to collect data to respond to specific questions. These are the purpose-built sensors. Self-tracking sensors are an example of purpose-built sensors that have reached a significant commercial success in recent years. Embedded in wearables, these devices register the user's location and speed, heartbeat, and other personal data that, in addition to giving back information to the user, at an aggregated level might be useful to help us better understand and design urban spaces. The chapter is divided in three sections: Real Time Cities describes how we used cell phone-usage data to map the complexities of the spatiotemporal use of cities; projects discussed in Tracking Urban Flows explore GPS trackers embedded in objects that populate the cities, such as trash; and Emerging Geographies combines data from cell phones and self-tracking apps to propose new ways to understand urban phenomena, from comparing patterns in urban dynamics in cities around the world to having a better understanding of people's exposure to pollutants in urban areas.

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