Senseable City Lab :.:: Massachusetts Institute Of

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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.

Real Time Cities

Ten years ago, the Senseable City Lab launched a research initiative to use cell phone data, aggregated at the cell phone tower coverage area, to understand the intensity in the use of different areas of the city by its inhabitants. Cell phone tower coverage area (cell) collects the location of cell phones based on the signals transmitted and received by these devices. Even when off, cell phones send signals to the transceiver station, and when in movement, they 'hand over' signals between transceivers. Although not as precise as using the GPS location of each individual device, at the time we started this research the use of data from the cell phone towers presented the advantage of harnessing vast amounts of data from existing urban infrastructures.

Aggregated data of cell phone location at the tower level is anonymous —the log uses as unique identification the International Mobile Subscriber Identity (IMSI). An encrypted code that is renewed daily (which hinders long-term tracking), IMSI contains a random number instead of the mobile phone number, a code for the current service provider, the home country service provider (as roaming partner), the network area, and the network cell. Each time the cell phone sends and receives signals to/from the tower, an event with a timestamp is logged. This information permits to map the spatio-temporal distribution of the cell phones, which can be used as proxy for population distribution.

Mapping the movements of cell phones among the cells creates a fairly clear picture of human interactions in space and time. Indeed, not long after these initial explorations, researchers used cell phone data in Haiti in the weeks following the 2010 earthquake to quantify the number of Haitians (more than 600,000) who fled Port au Prince, the country' capital (Bengtsson et al., 2011), and track their movements within the country. Such dynamic maps based on the intensity of cell phone usage have also been used to understand the macro movements taking place in cities, and inform urban and transportation planning (Calabrese et al., 2011; Çolak et al., 2015) or understand economic activities (Reades et al., 2009). Not surprisingly, IT companies such as Cisco and Siemens are investing in systems that can mine such data, not only to better understand but also predict mobility patterns.

The Senseable City Lab's first initiative using cell phone data to understand human movements happened in Graz, Austria, in 2005. Customers of A1 Mobicom Austria network were invited to allow us to 'ping' their cell phones, which generated data of their position and movements in the city. In 2007, we monitored cell phone usage combined with GPS location from volunteers, to have a more granular understanding of human movements during the Culture Night (Copenhagen's biggest annual one-day cultural event). The underlying research interest of these early projects was to use cell phone data to describe and represent

563 urban dynamics at a moment when mobile devices had started to become ubiquitous. It was clear that a new tool of analytical inquiry for cities was emerging.

In 2006, the Senseable City Lab displayed Real Time Rome during the Venice Biennale. This project used aggregated data from cell phone towers and taxi and bus movements in Rome between July and September of 2006. The dataset included more than one million TIM (Telecom Italia) subscribers and visitors to Rome, totaling 3.5 million observations. During this period, we focused on two special events: the FIFA World Cup final match, between Italy and France, and a concert by Madonna. Before the match, the location of the calls showed a regular pattern, representing a normal day in the city. During the match, though, it became clear how the events in the soccer field influenced the intensity in the number of calls —moments of silence when France scored, peaks when Italy scored and, eventually, won the match and the World Cup. Peaks spread across Rome in celebration, culminating with the reception of the Italian team in the following day at the Circo Massimo. While the World Cup final match showed the city pulsating differently according to specific events, which varied in time and space, the Madonna concert showed a different spatiotemporal pattern. In this case, the intensity in cell phone use was focused on a single location and during a very specific time period.

Real Time Cities demonstrated how cities behave differently in the relative short timeframe of only a few months, reflecting how particular events influence urban dynamics. By revealing how the intensity of the calls varied spatially and temporally, we demonstrated novel ways to understand urban dynamics, also revealing the emotional landscape of Rome. In a more practical way, this knowledge can inform transportation management in a more responsive way. Indeed, in the following years similar projects have emerged in different research centers, pointing to a tendency to create dynamic and collaborative mapping that can inform transportation management and urban design.

Tracking urban flows

Trackers used in the Real Time Cities projects were opportunistic sensors: The fact that these sensors collect data from the users —such as their location— is essential to the functioning of the technology. In this section we explore both opportunistic sensors and purpose-built sensors, which are designed and deployed with specific research questions in mind.

In 2009, the Senseable City Lab launched Trash Track. The basic idea behind the project was to understand the removal chain with the same accuracy the contemporary society understands the supply chain. Virtually all components of consumables can be tracked back to their origin —in many cases, this is mandated by laws and regulations. However, once a product is discarded, it immediately disappears from the user's mind: you take the garbage to a trash bin, or put it at the curbside, and it magically disappears. We assume that someone is taking care of it, collecting our trash and bringing it to a recycling facility, an incinerator, or a dumpsite. But what actually happens with the things we discard? We decided to take a close look.

In Seattle, hundreds of people brought us items they were about to discard: sneakers, tires, canned food, computers. We attached trackers to 3,000 objects, and people returned home and discarded their respective objects using the usual channels. Each tracker was equipped with GPS, battery and GSM/GPRS modems (Offenhuber, 2017). Following and mapping the routes each object took to its final destination, we found that while perishables ended up close to the city, some types of objects (such as electronics) travelled a lot, ending up in different states, after months and thousands of kilometers far from the moment and location they had disappeared from the user's mind.

564 Mapping trash movements throughout the United States made clear that the removal chain is overall inefficient. This in itself was an eye opener to many citizens, who could bring this information back to public authorities and demand more efficient ways to deal with discarded objects. Beyond this general picture, we found that electronic devices, such as CRT and LCD screens, and printers showed a particularly awkward behavior: they travelled longer, and several of them ended up by the country's borders and in port cities. Regarding the country's borders, it was because for this deployment we only had domestic cell phone coverage: if a device had crossed the U.S. borders, we would have lost track of it. But what about the port cities? Why were electronics ending up there?

In order to investigate this question, we partnered with the Non-Government Organization Basel Action Network (BAN), whose mission is to monitor international trade of toxic waste, including electronics. BAN deployed trackers in 205 printers and LCD and CRT monitors. BAN’s researchers approached the owners of these devices before they donated these equipment to shops and recycling facilities certified by the e- Steward and R2 programs4. We were aware that 205 devices make a small sample, and do not necessarily represent what happens in the country. Still, the experiment was important to test whether certified recycling programs were respecting the Basel convention, which controls transboundary movements of hazardous wastes, such electronic devices.

However, when we mapped their traces, we found that after being delivered to centers where the owners imagined their equipments would be sent for recycling, electronic devices travelled on average 4,158 kilometers for about 100 days. Sixty nine of the 205 devices ended up abroad, most of them in China (Lee et al, 2017). In the Monitour project, we used the deployment of purpose-built sensors as an investigative tool to understand a problem (export of electronic waste) that, because of its potential illegality, was hard to investigate using traditional means, and whose existence, amounts and routes were largely based on proxies, such as the selling of electronics. Estimates based on selling number leads to largely rough and often different numbers (Lepawsky, 2015). Using trackers, which basically employ simple off-the-shelf technologies, BAN was able to launch an investigation that shed light on an unglamorous side of the information society.

Cellphone-based movement and location sensing devices are carried by millions of people daily. As Gina Neff and Dawn Nafus (2016) argue, it is impossible to live an untrackable life, and discuss how health and wellness-related trackers have been used to not only mirror the individual's performance and health signals, but also as a social tool through which people interact and learn from each other.

The profitable niche of health trackers is composed by devices and applications. The former are gadgets that collect several signals from the users' activity, from the number of steps a person takes daily to how long a person sleeps, from the heartbeat rate to the approximate arousal based on skin conductance. The latter are applications that can be installed in smartphones. These apps use several features embedded in the smartphones, such as GPS and accelerometers to measure distances and speed, and social media apps that compare the user's performance with her peers.

Researchers and the app-companies have been creating maps that show the most used routes, user's average speed, and the preferred areas of the cities to perform different outdoor activities. As a marketing strategy, apps are commonly designed for a specific public, such as cyclists, pedestrians, or runners, even though they use the same technologies embedded in most smartphones. In Cityways (2017), we used data from a single app to understand and compare the behavior of cyclists, pedestrians, and runners in Boston and San Francisco. Based on the speed, the app defines which activity the user is

4 e-Stewards (Standard for Responsible Recycling and Reuse of Electronic Equipment®), R2 (Responsible Recycling Practices) are the two standard certification programs for electronics recyclers in the U.S.

565 performing —for instance removing traces when the speed is high, which would indicate the use of motorized modes. Rather than centering on the person's performance, our goal was to understand the city through self-tracking apps. Anthony Vanky (2017) used this data to understand whether and how weather, geography, street network, urban greenery, and the presence of amenities such as shops or urban furniture, influence the location, frequency, and length of pedestrian, cyclist, and runner activities in Boston and San Francisco.

In a time when we are not only self-tracking ourselves, but making this data available —often times unaware of it— it is critical that we ask whether this phenomenon is empowering the users or it is a disguised form of social control (Nafus, 2016). Therefore, it is equally important to explore what benefits this data can bring to society. This was the focus of CityWays. Although designed to measure physical activities at the individual level, aggregated data from self-tracking devices can inform city officials and designers to propose public policies and design proposals that could trigger more physical activities and promote higher use of public spaces. Understanding the temporal use of cities by different people's profiles can make urban design more fitting to the city residents’ habits.

Emerging geographies

The Senseable City Lab has demonstrated that the use of cell phone data can shed light onto urban phenomena that happen at different scales, from the national to the neighborhood level, which would be impossible to detect using traditional fixed- and long-term databases. For example, in the project Many Cities (2014), we used the numbers of calls, SMS and data requests, as well as the amount of data uploaded and downloaded by subscribers in four major global cities: London, New York, Los Angeles and Hong Kong. The goal was to detect repeating patterns or special events based on cell phone traffic data, and compare how different cities behave using a common dataset. On the one hand, this project reveals that cities throughout the world share some activity patterns, such as the buzzing activities in the core financial centers, while other commercial and residential areas have more city-specific patterns. On the other hand, we also found unexpected urban patterns at the global scale, such as New York and Hong Kong sharing more similar urban-activity signatures than New York and London (for example, cell phone activity drops in London during the weekends much more than in New York and Hong Kong), even if the cultural and linguistic background of the latter are more closely related (Grauwin et al., 2015).

When we correlate data from cell phone usage with other datasets, we can understand urban phenomena in novel ways. In Urban Exposures, we faced one of the world’s most pressing environmental health concerns: the increasing number of people contributing to and being affected by air pollution, leading to 7 million early deaths each year. This problem is particularly relevant in cities, which usually have high motorization rates, making urban dwellers more exposed to pollutants. Measuring exposure to pollutants usually involves correlating pollution levels measured in fixed air quality network sites and static population distributions, such as home location.

However, correlating exposure to pollutants with home location misses a crucial aspect of urban life: people move, and move more frequently in urban areas, where pollution levels are also higher than in rural or suburban areas. In order to understand people's exposure to pollutants we need to take into consideration how people move in space and time in cities. Researchers have used time-activity diaries and questionnaires to monitor a person's movement in space and time, but this method is laborious and requires the active participation of the subjects. Tied to GPS monitors, such time-activity diaries become less time-consuming and more efficient; however, so far most of the studies focus on small samples (Steinle et al., 2013). The combination of large networks of air quality sensors and self-tracking devices is a fertile research field to correlate exposure to pollutants with people's daily movements. Nazelle et al. (2013) monitored 36 volunteers in Barcelona using CalFit-equipped smartphones (which uses the

566 integrated GPS and accelerometer technologies embedded in smartphones to record a subject’s time- location patterns and energy expenditure) to correlate their physical activity and geographic location with spatiotemporal air pollution mapping. Similarly, other research initiatives (Hasenfratz et al., 2012) have attached air quality sensors to smartphones to collect data at a person's level, with the constraint that this methodology requires finding volunteers, and data from low-cost monitors should be calibrated to static and professional ones.

Such studies use purpose-built sensors, and are based on very limited samples. In Urban Exposures, we decided to take advantage of one of the most pervasive opportunistic sensors: cell phones, which constantly generate spatiotemporal logs of millions of users. Marguerite Nyhan et al. (2016) propose to integrate mobility patterns of millions of cell phone users with spatiotemporal PM2.5 concentration level estimates, collected in 155 locations over all seasons in two-week intervals in 71 districts in New York City. Mapping the movement patterns of cell phone users demonstrates that air pollution exposures vary temporally and spatially when we compare "home population exposure" with "active population exposure", which reflects population activity based on extensive mobile device usage records. This result might bring important contributions to public health policies, clearly showing the value of using anonymized and aggregated cell phone usage data to understand urban phenomena in novel ways.

Conclusions

If the built environment is a kind of “third skin” —after our biological one and clothing— it has long been a rigid one. The understanding that the city exists through the information exchanges that happen among people, as well as between people and the natural and built environments is not new. However, either such understanding was mostly argued conceptually or measured in time-consuming and very localized ways. The abundance of data we generate daily, and which structure our contemporary society, represents a ‘game-changer’ in the way we make sense of urban phenomena. The widespread use of opportunistic sensors embedded in devices used daily, as well as purpose-built sensors more often deployed in buildings and infrastructures, opens the opportunity for gathering data from our daily activities with fine spatial and temporal granularity.

Although still in exploratory phases, several research initiatives, some of which we have discussed in this chapter, argue that gathering and analyzing such data abundance is not an end in itself. Both opportunistic and purpose-built sensors using movement and location sensing technologies embedded in personal mobile devices can inform planning and design in novel ways. In a series of projects combined in Real Time Cities, we pioneered the use of cell phone data to understand the temporal variations in how people move in cities and use public spaces, which vary according to daily patterns and special events. In Urban Exposures we used the same type of data to reveal a hidden side of public health: rather than quantifying the exposure to pollutants in urban areas based on people’s residence, we mapped their daily movements throughout the city. This approach helps to understand the actual levels of exposure of people, while they are moving and using different parts of the city in their daily routines. Also using movement and location sensing technologies embedded in personal mobile devices, we proposed a series of research initiatives to follow trash disposal both at the local and global level. This research demonstrated that contemporary consumption has spatial and temporal footprints far beyond what we usually imagine.

In practical ways, using movement and location sensing technologies embedded in personal mobile devices presents novel ways to understand urban dynamics that have the potential to improve traffic control and public transportation, public health and waste management, and to inform urban interventions aimed at activating particular urban areas. On the other hand, sensors embedded in the built environment to measure and adjust humidity, temperature, vibration, lighting, and energy consumption, are fostering

567 responsive architectures, which respond to the surrounding environment as well as to the type and intensity of their use. Thus, what we argue here is that with better data, the built environment can be adaptive to the human activities. The city can become a living, tailored environment that is molded on its inhabitants’ behaviors.

Acknowledgments

The authors would like to acknowledge the many researchers at the Senseable City Lab who have worked on these projects —they were responsible for generating ideas, building and deploying sensors, visualizing the data, and writing scientific papers that discuss in depth concepts and analysis described in this chapter. Each of them are credited on the website of the respective projects. The authors also thank Cisco, SNCF Gares & Connexions, Allianz, UBER, Fondation OCP, Volkswagen Group America, Liberty Mutual, Ericsson, Saudi Telecom, Philips, Austrian Institute of Technology, Fraunhofer Institute, Kuwait- MIT Center for Natural Resources, SMART Singapore MIT Alliance for Research and Technology, AMS Institute, and the Victoria State Government, and all the members of the MIT Senseable City Lab Consortium for supporting this research.

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