Markus Andrews

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    A comprehensive study of technological change

    The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

    New research from MIT aims to assist in the prediction of technology performance improvement using U.S. patents as a dataset. The study describes 97 percent of the U.S. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential.

    “The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”

    A previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. patent system. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.

    Overlap and centrality

    A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

    To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

    “Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

    Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

    “Fast-improving domains are concentrated in a few technological areas,” says Magee. “The domains that show improvement rates greater than the predicted rate for integrated chips — 42 percent, from Moore’s law — are predominantly based upon software and algorithms.”

    TechNext Inc.

    The researchers built an online interactive system where domains corresponding to technology-related keywords can be found along with their improvement rates. Users can input a keyword describing a technology and the system returns a prediction of improvement for the technological domain, an automated measure of the quality of the match between the keyword and the domain, and patent sets so that the reader can judge the semantic quality of the match.

    Moving forward, the researchers have founded a new MIT spinoff called TechNext Inc. to further refine this technology and use it to help leaders make better decisions, from budgets to investment priorities to technology policy. Like any inventors, Magee and his colleagues want to protect their intellectual property rights. To that end, they have applied for a patent for their novel system and its unique methodology.

    “Technologies that improve faster win the market,” says Singh. “Our search system enables technology managers, investors, policymakers, and entrepreneurs to quickly look up predictions of improvement rates for specific technologies.”

    Adds Magee: “Our goal is to bring greater accuracy, precision, and repeatability to the as-yet fuzzy art of technology forecasting.” More

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    Study finds lockdowns effective at reducing travel in Sierra Leone

    Throughout the Covid-19 pandemic, governments have used data on people’s movements to inform strategies for containing the spread of the virus. In Europe and the United States, for example, contact-tracing apps have used Bluetooth signals in smartphones to alert people when they’ve spent time near app users who have tested positive for Covid-19. 

    But how can governments make evidence-based decisions in countries where such fine-grained data isn’t available? In recent findings, MIT researchers, in collaboration with Sierra Leone’s government, use cell tower records in Sierra Leone to show that people were traveling less during lockdowns. “When the government implemented novel three-day lockdowns, there was a dual aim to reduce virus spread and also limit social impacts, like increased hunger or food insecurity,” says Professor Lily L. Tsai, MIT Governance Lab’s (MIT GOV/LAB) director and founder. “We wanted to know if shorter lockdowns would be successful.”   

    The research was conducted by MIT GOV/LAB and MIT’s Civic Data Design Lab (CDDL), in partnership with Sierra Leone’s Directorate for Science, Innovation and Technology (DSTI) and Africell, a wireless service provider. The findings will be published as a chapter in the book “Urban Informatics and Future Cities,” a selection of research submitted to the 2021 Computational Urban Planning and Urban Management conference. 

    A proxy for mobility: cell tower records

    Any time someone’s cellphone sends or receives a text, or makes or receives a call, the nearest cell tower is pinged. The tower collects some data (call-detail records, or CDRs), including the date and time of the event and the phone number. By tracking which towers a certain (anonymized) phone number pings, the researchers could approximately measure how much someone was moving around.  

    These measurements showed that, on average, people were traveling less during lockdowns than before lockdowns. Professor Sarah Williams, CDDL’s director, says the analysis also revealed frequently traveled routes, which “allow the government to develop region-specific lockdowns.” 

    While more fine-grained GPS data from smartphones paint a more accurate picture of movement, “there just isn’t a systematic effort in many developing countries to build the infrastructure to collect this data,” says Innocent Ndubuisi-Obi Jr., an MIT GOV/LAB research associate. “In many cases, the closest thing we can use as a proxy for mobility is CDR data.”

    Measuring the effectiveness of lockdowns

    Sierra Leone’s government imposed the three-day lockdown, which required people stay in their homes, in April 2020. A few days after the lockdown ended, a two-week inter-district travel ban began. “Analysis of aggregated CDRs was the quickest means to understanding mobility prior to and during lockdowns,” says Michala Mackay, DSTI’s director and chief operating officer. 

    The data MIT and DSTI received was anonymized — an essential part of ensuring the privacy of the individuals whose data was used. 

    Extracting meaning from the data, though, presented some challenges. Only about 75 percent of adults in Sierra Leone own cellphones, and people sometimes share phones. So the towers pinged by a specific phone might actually represent the movement of several people, and not everyone’s movement will be captured by cell towers. 

    Furthermore, some districts in Sierra Leone have significantly fewer towers than others. When the data were collected, Falaba, a rural district in the northeast, had only five towers, while over 100 towers were clustered in and around Freetown, the capital. In areas with very few towers, it’s harder to detect changes in how much people are traveling. 

    Since each district had a unique tower distribution, the researchers looked at each district separately, establishing a baseline for average distance traveled in each district before the lockdowns, then measuring how movement compared to this average during lockdowns. They found that travel to other districts declined in every district, by as much as 72 percent and by as little as 16 percent. Travel within districts also dropped in all but one district. 

    This map shows change in average distance traveled per trip to other districts in Sierra Leone in 2020.

    Image courtesy of the MIT GOV/LAB and CDDL.

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    Lockdowns have greater costs in poorer areas

    While movement did decline in all districts, the effect was less dramatic in poorer, more sparsely populated areas. This finding was to be expected; other studies have shown that poorer people often can’t afford to comply with lockdowns, since they can’t take time off work or need to travel to get food. Evidence showing how lockdowns are less effective in poorer areas highlights the importance of distributing resources to poorer areas during crises, which could both provide support during a particularly challenging time and make it less costly for people to comply with social distancing measures. 

    “In low-income communities that demonstrated moderate or low compliance, one of the most common reasons why people left their homes was to search for water,” says Mackay. “A policy takeaway was that lockdowns should only be implemented in extreme cases and for no longer than three days at a time.”

    Throughout the project, the researchers collaborated intimately with DSTI. “This meant government officials learned along with the MIT researchers and added crucial local knowledge,” says Williams. “We hope this model can be replicated elsewhere — especially during crises.” 

    The researchers will be developing an MITx course teaching government officials and MIT students how to collaboratively use CDR data during crises, with a focus on how to do the analysis in a way that protects people’s privacy.

    Ndubuisi-Obi Jr. also has led a training on CDR analysis for Sierra Leonean government officials and has written a guide on how policymakers can use CDRs safely and effectively. “Some of these data sets will help us answer really important policy questions, and we have to balance that with the privacy risks,” he says. More

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    “To make even the smallest contribution to improving my country would be my dream”

    Thailand has become an economic leader in Southeast Asia in recent decades, but while the country has rapidly industrialized, many Thai citizens have been left behind. As a child growing up in Bangkok, Pavarin Bhandtivej would watch the news and wonder why families in the nearby countryside had next to nothing. He aspired to become a policy researcher and create beneficial change.

    But Bhandtivej knew his goal wouldn’t be easy. He was born with a visual impairment, making it challenging for him to see, read, and navigate. This meant he had to work twice as hard in school to succeed. It took achieving the highest grades for Bhandtivej to break through stigmas and have his talents recognized. Still, he persevered, with a determination to uplift others. “I would return to that initial motivation I had as a kid. For me, to make even the smallest contribution to improving my country would be my dream,” he says.

    “When I would face these obstacles, I would tell myself that struggling people are waiting for someone to design policies for them to have better lives. And that person could be me. I cannot fall here in front of these obstacles. I must stay motivated and move on.”

    Bhandtivej completed his undergraduate degree in economics at Thailand’s top college, Chulalongkorn University. His classes introduced him to many debates about development policy, such as universal basic income. During one debate, after both sides made compelling arguments about how to alleviate poverty, Bhandtivej realized there was no clear winner. “A question came to my mind: Who’s right?” he says. “In terms of theory, both sides were correct. But how could we know what approach would work in the real world?”

    A new approach to higher education

    The search for those answers would lead Bhandtivej to become interested in data analysis. He began investigating online courses, eventually finding the MIT MicroMasters Program in Data, Economics, and Development Policy (DEDP), which was created by MIT’s Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL). The program requires learners to complete five online courses that teach quantitative methods for evaluating social programs, leading to a MicroMasters credential. Students that pass the courses’ proctored exams are then also eligible to apply for a full-time, accelerated, on-campus master’s program at MIT, led by professors Esther Duflo, Abhijit Banerjee, and Benjamin Olken.

    The program’s mission to make higher education more accessible worked well for Bhandtivej. He studied tirelessly, listening and relistening to online lectures and pausing to scrutinize equations. By the end, his efforts paid off — Bhandtivej was the MicroMasters program’s top scorer. He was soon admitted into the second cohort of the highly selective DEDP master’s program.

    “You can imagine how time-consuming it was to use text-to-speech to get through a 30-page reading with numerous equations, tables, and graphs,” he explains. “Luckily, Disability and Access Services provided accommodations to timed exams and I was able to push through.”   

    In the gap year before the master’s program began, Bhandtivej returned to Chulalongkorn University as a research assistant with Professor Thanyaporn Chankrajang. He began applying his newfound quantitative skills to study the impacts of climate change in Thailand. His contributions helped uncover how rising temperatures and irregular rainfall are leading to reduced rice crop yields. “Thailand is the world’s second largest exporter of rice, and the vast majority of Thais rely heavily on rice for its nutritional and commercial value. We need more data to encourage leaders to act now,” says Bhandtivej. “As a Buddhist, it was meaningful to be part of generating this evidence, as I am always concerned about my impact on other humans and sentient beings.”

    Staying true to his mission

    Now pursuing his master’s on campus, Bhandtivej is taking courses like 14.320 (Econometric Data Science) and studying how to design, conduct, and analyze empirical studies. “The professors I’ve had have opened a whole new world for me,” says Bhandtivej. “They’ve inspired me to see how we can take rigorous scientific practices and apply them to make informed policy decisions. We can do more than rely on theories.”

    The final portion of the program requires a summer capstone experience, which Bhandtivej is using to work at Innovations for Poverty Action. He has recently begun to analyze how remote learning interventions in Bangladesh have performed since Covid-19. Many teachers are concerned, since disruptions in childhood education can lead to intergenerational poverty. “We have tried interventions that connect students with teachers, provide discounted data packages, and send information on where to access adaptive learning technologies and other remote learning resources,” he says. “It will be interesting to see the results. This is a truly urgent topic, as I don’t believe Covid-19 will be the last pandemic of our lifetime.”

    Enhancing education has always been one of Bhandtivej’s priority interests. He sees education as the gateway that brings a person’s innate talent to light. “There is a misconception in many developing countries that disabled people cannot learn, which is untrue,” says Bhandtivej. “Education provides a critical signal to future employers and overall society that we can work and perform just as well, as long as we have appropriate accommodations.”

    In the future, Bhandtivej plans on returning to Thailand to continue his journey as a policy researcher. While he has many issues he would like to tackle, his true purpose still lies in doing work that makes a positive impact on people’s lives. “My hope is that my story encourages people to think of not only what they are capable of achieving themselves, but also what they can do for others.”

    “You may think you are just a small creature on a large planet. That you have just a tiny role to play. But I think — even if we are just a small part — whatever we can do to make life better for our communities, for our country, for our planet … it’s worth it.” More

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    Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

    As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

    The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

    “We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today,” says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory’s AI Software Architectures and Algorithms Group.

    The themes of the virtual event revolved around some of the year’s most relevant issues, such as analyzing disinformation on social media, modeling the pandemic’s spread, and using graph-based machine learning models to speed drug design.

    “The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation,” says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

    Social networks

    Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

    Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

    “A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives,” says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

    To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account’s level of influence in spreading the narrative within that network.

    The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They’ve also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. “This suggests that these accounts will continue to transition to disinformation narratives,” Mackin says.

    Pandemic modeling

    Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

    Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani’s model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops “synthetic populations” that are built by using publicly available, highly detailed datasets about U.S. households. “We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals,” he says. This information feeds back into the model to predict the spread of the disease. 

    Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. “It’s still a work in progress that is extremely interesting,” he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

    As researchers model the virus’ spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

    As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

    “Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don’t have to shut down a full country.” Laird adds that their planner offers a “sandbox environment” for exploring intervention strategies in the future.

    Machine learning with graphs

    Graph-based machine learning is receiving increasing attention for its potential to “learn” the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

    “We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine,” says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

    Zitnik’s research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik’s team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

    At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory’s team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

    “Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems,” Caceres says. 

    A vibrant community

    Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year’s cancellation due to Covid-19. “One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it’s ever been,” Streilein says. “Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact.”

    In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories. More

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    Lockdowns reveal inequities in opportunities for walking activities

    Lockdowns saved lives during the global SARS-CoV-2 pandemic. But as much as they have slowed the spread of Covid-19, there have been some unintended consequences.

    New MIT research shows that lockdowns in 10 metropolitan areas throughout the United States led to a marked reduction in walking. These decreases were mostly seen among residents living in lower-income areas of the city, effectively reducing access to physical activity for minorities and people suffering from illnesses such as obesity and diabetes.

    “Walking is the cheapest, most accessible physical exercise that you can do,” says Esteban Moro, visiting research scientist in the MIT Connection Science Group and senior author on the Nature Communications paper published on June 16. “Places in which people have lower incomes, less park access, and more obesity prevalence were more affected by this walking reduction — which you can think of as another pandemic, the lack of access to affordable exercise.”

    The research focused on recreational versus utilitarian walking done by residents in the U.S. cities of New York, Los Angeles, Chicago, Boston, Miami, Dallas, San Francisco, Seattle, Philadelphia, and Washington D.C. (Utilitarian walking is defined as having a goal; for example, walking to the store or to public transportation. Recreational walking is a walk meant for leisure or exercise.)

    Comparing cellphone data from February 2020 to different time points throughout 2020 lockdowns, the researchers saw an average 70 percent decrease in the number of walks — which remained down by about 18 percent after loosened restrictions — a 50 percent decrease in distance walked, and a 72 percent decrease in utilitarian walking — which remained down by 39 percent even after restrictions were lifted.

    On their face, these findings may not be surprising. When people couldn’t leave their homes, they walked less. But digging deeper into the data yields troubling insights. For example, people in lower-income regions are more likely to rely on public transportation. Lockdowns cut back on those services, meaning fewer people walking to trains and buses.

    Another statistic showed that people in higher-income areas reduced their number of utilitarian walks but were able to replace some of the lost movement with recreational walks around their neighborhoods or in nearby parks.

    “People in higher-income areas generally not only have a park nearby, but also have jobs that give them a degree of flexibility. Jobs that permit them to take a break and walk,” says Moro. “People in the low-income regions often don’t have the ability, the opportunity or even the facilities to actually do this.”

    How it was done

    The researchers used de-identified mobile data obtained through a partnership within the company Cuebiq’s Data for Good COVID-19 Collaborative program. The completely anonymized dataset consisted of GPS locations gathered from smartphone accelerometers from users who opted into the program. Moro and his collaborators took these data and, using specifically designed algorithms, determined when people walked, for how long, and for what purpose. They compared this information from before the pandemic, at different points throughout lockdown, and at a point when most restrictions had been eased. They matched the GPS-identified locations of the smartphones with census data to understand income level and other demographics.

    To make sure their dataset was robust, they only used information from areas that could reasonably be considered pedestrian. The researchers also acknowledge that the dataset may be incomplete, considering people may have occasionally walked without their phones on them.

    Leisure versus utilitarian walks were separated according to distance and/or destination. Utilitarian walks are usually shorter and involve stops at destinations other than the starting point. Leisure walks are longer and usually happen closer to home or in dedicated outdoor spaces.

    For example, many of the walks recorded pre-Covid-19 were short and occurred at around 7 a.m. and between 3 and 5 p.m., which would indicate a walking commute. These bouts of walking were replaced on weekends by short walks around noon.

    The key takeaway is that most walking in cities occurs with the goal of getting to a place. If people don’t have the opportunity to walk to places they need to go, they will reduce their walking activity overall. But when provided opportunity and access, people can supplement utilitarian activity with leisure walking.

    What can be done about it

    Taking into account the public health implications of physical inactivity, the authors argue a reduction in access to walking should be considered a second pandemic and be addressed with the same rigor as the Covid-19 pandemic.

    They suggest several tactical urbanization strategies (defined as non-permanent but easily accessible measures) to increase safety and appeal for both utilitarian and recreational walkers. Many of these have already been implemented in various cities around the world to ease economic and other hardships of the pandemic. Sections of city streets have been closed off to cars on weekends or other non-busy times to allow for pedestrian walking areas. Restaurants have been given curb space to allow for outdoor dining.

    “But most of these pop-up pedestrian areas happen in downtown, where people are high-income and have easier access to more walking opportunities,” notes Moro.

    The same attention needs to be paid to lower-income areas, the researchers argue. This study’s data showed that people explored their own neighborhoods in a recreational way more during lockdown than pre-pandemic. Such wanderings, the researcher say, should be encouraged by making any large, multi-lane intersections safer to cross for the elderly, sick, or those with young children. And local parks, usually seen as places for running laps, should be made more attractive destinations by adding amenities like water fountains, shaded pavilions, and hygiene and sanitation spaces.

    This study was unique in that its data came straight from mobile devices, rather than being self-reported in surveys. This more reliable method of tracking made this study more data-driven than other, similar efforts. And the geotagged data allowed the researchers to dig into socioeconomic trends associated with the findings.

    This is the team’s first analysis of physical activity during and just after lockdown. They hope to use lessons learned from this and planned follow-ups to encourage more permanent adoption of pedestrian-friendly pandemic-era changes.

    The Connection Science Group, co-led by faculty member Alex “Sandy” Pentland — who, along with Moro was a co-author on the paper along with six others from the UK, Brazil, and Australia — is part of the MIT Sociotechnical Systems Research Center within the MIT Institute for Data, Systems, and Society. The collaborative research exemplified in this study is core to the mission of the SSRC; in pairing computer science with public health, the group not only observes trends but also contextualizes data and use them to make improvements for everyone.

    “SSRC merges both the social and technological components of the research,” says Moro. “We’re not only building an analysis, but going beyond that to propose new policies and interventions to change what we are seeing for the better.” More

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    Driving commuters toward sustainable options

    When organizations like MIT transitioned to remote work and learning last year in the interest of health and safety, one impact of this change was immediately visible to many people — a large decrease in commuters on roads and public transportation. The change spurred theories of the potential positive impact on emissions, but also created many questions about the future of commuting and commuting benefits programs like those offered at MIT. With concerns over safety and large numbers of employees working remotely, public transportation use dwindled while the delicate balance of permits to parking spots on a dense urban campus like MIT didn’t demand the same level of focus as pre-pandemic.

    But now, more than a year after remote work became the norm for many, MIT is preparing for employees and a full cohort of students to return to campus in the fall. This transition back has come with many challenging questions, one being: How do you manage the commuting choices of thousands of staff to support safety and sustainability while strategically managing limited on-campus parking? For MIT, part of the answer is data.

    Earlier this year, the Office of Sustainability (MITOS) launched “Commuting at the Institute: The Story of Access MIT,” a new data dashboard in the Sustainability DataPool that shares aggregate data on employee commuting choices with data stretching back to 2016 — helping Institute leaders identify patterns and best practices for balancing commuting needs of employees with Institute resources and safety as MIT transitions back to a densely populated campus. 

    Access MIT launched in 2016 with the goal of reducing parking demand on campus by 10 percent over two years. The program itself was designed based on years of collaborative research and testing by the MIT Committee for Transportation and Parking, Transit Lab, Parking and Transportation Office, MITOS, and other partners to determine if commuter behavior could be influenced by incentives like cost-free public transportation — data showed that the answer was “yes.” Access MIT’s pre-pandemic design included cost-free local subway and bus and a flexible pay-per-day parking fee structure (among other benefits) to encourage all benefits-eligible MIT employees to choose sustainable, low-carbon commutes. Between 2016 and 2019, Access MIT drove a nearly 15 percent reduction in on-campus parking in gated lots and increased public transportation adoption by employees.

    The original intent of the data collection and sharing was to understand employee behaviors to continue to adjust the program to support low-carbon commutes; the dashboard displays overall use trends including which days have higher use rates for public transportation or parking. But as the pandemic upended daily life at MIT, the data began to tell a story of that impact.

    One impact was a substantial increase in the number of parking account requests that the Office of Transportation and Parking fielded — suggesting a projected increase of single-occupancy vehicle trips. In response, the office worked to further incentivize safe and sustainable commuting options with expanded subsidies for Bluebike membership, commuter rail passes, MBTA parking, and more, in addition to the free local bus and subway already offered through the Access MIT program, in an effort to return to the pre-pandemic trends illustrated on the transportation dashboard.

    “An unexpected outcome of this data collection and sharing is that we now have data to inform the return to campus,” explains Director of Sustainability Julie Newman, noting that campus growth like the new MIT Schwarzman College of Computing is also expected to impact parking demand and require strategic decision-making. “We now have the ability to look at the data to understand how much parking we do or do not need, or to explore additional opportunities for congestion mitigation strategies.”

    This type of responsive planning is the result of the changes demanded by the pandemic and now key to a successful return to campus and the future of work at MIT. In a recent MIT News article, Vice President for Campus Services and Stewardship Joe Higgins explained, “[T]he pandemic created a forced experiment in MIT’s operations. We learned what our current technology systems and policies can flexibly support, and where improvements could be rapidly applied to support our academic, research, and administrative functions.”

    The dynamic use and socializing of the data behind Access MIT is an example of the living lab culture on campus, where campus programs can be used to inform sustainability policies, research, and decision-making. For example, MITOS is also partnered with the Media Lab’s City Science research group, who are using the data to inform their research around the future of work.

    “Looking forward as we plan for a return in the fall, the transportation data dashboard can be used to look back at our pre-pandemic commuting behavior, inform future planning, and track our return to campus and administrative functions,” says Newman. “It’s really an invaluable tool.” More

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    New directions in real estate practice

    Among the courses taught by Siqi Zheng is one identifying how real estate companies can be profitable while building and operating sustainably. Her class, 11.S949 (Sustainable Real Estate), at the MIT Center for Real Estate (CRE) attracts students from throughout the MIT School of Architecture and Planning (SA+P) and MIT Sloan School of Management. Harvard University students also cross-register to attend her course.

    For Zheng, the Samuel Tak Lee Champion Professor of Urban and Real Estate Sustainability, there is a sense of coming full circle.

    “Like these students, I migrated from Harvard to MIT,” Zheng says. “Fifteen years ago, I was one of them. Now I attract Harvard students to my classes.”

    Not only has Zheng progressed from taking courses at CRE while a postdoc at Harvard’s Graduate School of Design to joining the SA+P faculty in 2017, she assumed the role of CRE’s faculty director last summer. Among her goals in this new position is encouraging the center’s culture of sustainability and innovation — the very qualities that brought her to MIT as a student.

    While Zheng’s doctoral studies focused on housing and China’s transition from a centrally planned economy to a market-based system, it was MIT’s focus on urban economics and the “clean air and blue skies” of Cambridge, Massachusetts — in contrast to the polluted air in Beijing — that altered her focus to urban sustainability.

    “Back in 2006, I audited several very good courses at CRE in urban and real estate economics. It opened a window for me to say, ‘I need to study cities instead of just housing — and in a broader way — to understand urban dynamics.’ My research area became the intersection of urban economics and environmental sustainability.”

    Following her postdoc, Zheng returned to Beijing and joined Tsinghua University as an assistant professor and director of its Hang Lung Center for Real Estate.

    Creative urban studies research

    Shortly after arriving at MIT, Zheng founded the China Future City Lab, giving her the opportunity to focus on that country’s rapid economic growth alongside the tension of more sustainable urbanization. Her research shows that Chinese urban households are willing to pay higher real estate prices to live in cities and locations with better environmental quality, and this demand has increased over time. She has also identified a substantial price premium for green buildings, which gives real estate developers a monetary incentive to build energy-efficient structures. Gradually, she says, her research and team expanded along with her interest in other fast-urbanizing countries; she renamed her lab the Sustainable Urbanization Lab.

    Zheng’s research is remarkably varied and prolific, with many collaborators in the United States and overseas. Last year, Zheng was one of six MIT faculty awarded a grant from Harvard Medical School to address the effects of Covid-19. While the other researchers focused on clinical areas, such as vaccine development and diagnostic tools, Zheng’s research explored the role of social distancing in shaping Covid-19’s curve. Currently under review for publication, Zheng’s research compares how people’s sentiment in cities globally responded to the shock of the pandemic and the policies each government mandated to slow the spread of the virus.

    “My overarching goal as a scholar is to build our understanding of the behavioral foundations for urban real estate and environmental actions aimed at sustainable urbanization,” Zheng says. “I look at incentives and how an individual’s behavior gets aggregated into our society and its outcomes. Last year, without a vaccine, we needed to slow the spread of the virus. We had to rely on people in all countries to socially distance. We wanted to understand the interactions between individual sentiments, voluntary behaviors, and government intervention — how they work together, and their outcomes.”

    Currently, Zheng’s team is monitoring social media data to detect behavior changes in the U.S. population before and after vaccination. Their theory is that individuals — once vaccinated against Covid-19 — are happier and take part in riskier behaviors, such as restaurant dining or not wearing a mask.

    “We’ve been monitoring emotional states on social media before the vaccination process began,” she says. “We can measure their emotional status and their activities from their social media posts. People lose their anxiety and fear after vaccination, and they stop taking precautions.”

    Zheng began using social media data as a tool to assess a population’s emotional status several years ago, when she studied emotions in conjunction with levels of air pollution in China. Her paper, “Air pollution lowers Chinese urbanites’ expressed happiness on social media,” appeared in Nature Human Behavior in 2019, and was the journal’s fourth-most popular paper that year.  

    Zheng used the same approach to understand how climate change affects people in China by coupling meteorological conditions with more than 400 million social media posts from 43 million users. Finding that extreme weather worsens emotional expressions on social media allowed the researchers to project the potentially harmful impacts of global warming on subjective well-being.

    CRE’s strategic directions

    Working with CRE Executive Director Professor Kairos Shen, and Associate Director Lisa Thoma, Zheng is mapping out a strategic plan for CRE. One emphasis is expanding interdisciplinary research. She is excited by the new work undertaken by the center’s postdocs and doctoral students, which she sees as fostering synergy with teaching.

    “This is MIT,” says Zheng. “We have excellent teaching — but that’s not enough. We need to have a strong research focus to support teaching because we need to introduce our brilliant students to the field’s frontiers.”

    A parallel strategy is expanding the center’s global perspective. Zheng notes the oft-used expression “location, location, location,” pointing out that, while CRE’s attention has leaned toward the United States and Boston, half of their students are from overseas and the majority of their alumni are based in Asia. As such, she is working to expand collaborations with academic institutions and alumni who are now leaders in the field in Korea, mainland China, Hong Kong, Japan, Singapore, and India. Asia is also the region with the fastest urbanization and real estate growth potential. That’s why Zheng and her colleagues are now developing their “MIT Asia Real Estate Initiative.”

    “I like creating things from scratch,” Zheng says. “The center is small, flexible, and forward-looking, so I have an opportunity to create some new exciting programs and generate new impact.”

    As part of her globalization strategy, Zheng also expects to expand MIT/CRE’s online education offerings. While the center admits only 30 graduate students each year, Zheng sees opportunities for professionals in the global real estate industry to expand their education with an online certificate program. Currently, Zheng is designing six new courses to join the two already online.

    Having begun her new role during the global pandemic, Zheng and her team have only worked remotely. While anxious to get to know her team members “in person and not only over Zoom,” Zheng keeps busy managing various research initiatives, teaching and deepening MIT/CRE’s global connections. She is active on her social media accounts, sharing the Center’s many research activities, industry developments, and student achievements. On weekends however, she posts photos of hiking and exploring with her husband and son.

    “I want to be less intense outside of work; spending time outside surrounded by nature helps me unwind,” she says. More

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    in Data Management & Statistics

    Why the Earth needs a course correction now

    The massive impact of the Covid-19 pandemic on lives and economies underscores that our collective survival and well-being hinges on our willingness to confront environmental threats that have global consequences. Key to protecting lives and making communities more resilient to such threats will be an emphasis on proactive, science-based decision-making at all levels of society. And among the most serious risks that science can help illuminate and alleviate are those resulting from human-induced climate change.

    To minimize those risks, the Paris Agreement aims to commit nearly 200 nations to implement greenhouse gas emissions-reduction policies consistent with keeping the increase in the global average temperature since preindustrial times to well below 2 degrees Celsius — and pursue efforts to further limit that increase to 1.5 C. Recognizing that the first set of submitted near-term Paris pledges, known as Nationally Determined Contributions (NDCs), are inadequate by themselves to put the globe on track to meet those long-term targets and thus avoid the worst consequences of climate change, the accord calls for participating nations to strengthen their NDCs over time. To that end, the United States and a few other nations announced more stringent emissions-reduction goals for 2030 at the virtual climate summit convened by President Joe Biden in April.  

    To support decision-makers now engaged in or impacted by this ongoing, international effort to stabilize the climate, the MIT Joint Program on the Science and Policy of Global Change has released its 2021 Global Change Outlook. Based on a rigorous, integrated analysis of population and economic growth, technological change, NDCs, Covid-19 impacts, and other factors, the report presents the Joint Program’s latest projections for the future of the Earth’s energy, food, water and climate systems, as well as prospects for achieving the Paris Agreement’s short and long-term climate goals.

    Projections are provided for a baseline “Paris Forever” scenario, in which current (as of March 2021) NDCs are maintained in perpetuity; a Paris 2 C scenario that caps global warming at 2 C by 2100; and two scenarios — “Accelerated Actions” (which includes the newly announced U.S. goal for 2030) and Paris 1.5 C — which limit warming to 1.5 C by 2100. Uncertainty is quantified using 400-member ensembles of projections for each scenario. This year’s outlook introduces a visualization tool that enables a higher-resolution exploration of the first three scenarios.

    Energy

    More aggressive emissions-reduction policies would accelerate a shift away from fossil fuels and toward renewable energy sources between now and 2050.

    Under the Paris Forever scenario, the share of fossil fuels in the world’s energy mix drops during this period from about 80 percent to 70 percent, wind and solar expand nearly six-fold and natural gas by 50 percent, and electric vehicles (EVs) account for 38 percent of the light-duty vehicle (LDV) fleet. In the Paris 2 C scenario, the fossil fuel share drops to about 50 percent, wind and solar energy grow almost nine times and natural gas use expands by 25 percent, and EVs account for 50 percent of the global LDV fleet. The Accelerated Actions scenario squeezes out fossil fuels further and makes two-thirds of global LDVs electric.  

    “Electricity generation from renewable sources becomes a dominant source of power by 2050 in all scenarios, providing 70-80 percent of global power generation by mid-century in the climate stabilization scenarios,” says Joint Program Deputy Director Sergey Paltsev, a lead author of the report. “Climate policies essentially eliminate coal-based generation, while natural gas still keeps a sizeable share because of the need to support variable renewables. Resolving long-term energy storage issues are critical to full decarbonization.”

    Food and water

    Under the Paris Forever scenario, agriculture and food production will keep growing. This will increase pressure for land-use change, water use, and use of energy-intensive inputs, which will also lead to higher greenhouse gas (GHG) emissions. The Paris 2 C scenario shows low impacts on agriculture and food production trends by mid-century. Although economic growth tends to shift demand toward more protein-rich food sources, higher carbon costs associated with livestock production drive demand downward, decreasing its prices, and such impacts are transmitted to the food sector.

    The Paris Forever scenario indicates that more than half of the world’s population will undergo stresses on its water supply by 2050, and that three of every 10 people will live in water basins where compounding societal and environmental pressures on water resources will be experienced. The majority of expected increases in population under heightened water stress by mid-century cannot be avoided or reduced by climate mitigation efforts alone. Worldwide increases in population, economic growth, and associated water demands are largely a challenge of sustainability — one that can only be alleviated through widespread transformations of water systems’ storage capacity, conveyance, and water-use efficiencies.

    Climate and Paris goals

    The outlook shows a wide gap between current (as of March 2021) GHG emissions-reduction commitments and those needed to put the world on track to meet the Paris Agreement’s long-term climate goals.

    “Our projected global climate responses under the Paris Forever scenario indicate with near-certainty that the world will surpass critical GHG concentration thresholds and climate targets in the coming decades,” says Joint Program Deputy Director C. Adam Schlosser, a lead author of the report.

    Under Paris Forever, the world is likely to exceed 2 C global climate warming by 2065, 2.8 C by 2100, and 4.1 C by 2150. While many countries have made good progress toward their NDCs and declared more ambitious GHG emissions mitigation goals, financing to assist the least-developed countries in sustainable development is not forthcoming at the levels needed.

    The report’s projections indicate that the long-term climate targets of the Paris Agreement remain achievable, but come with different levels of risk. The Paris 2 C scenario shows negligible likelihood of even the “coolest” trajectories remaining below 1.5 C at the end of the century. The Paris 1.5 C scenario, however, can virtually assure the world of remaining below 2 C of global warming.

    An important consequence of climate change is altered precipitation levels. Between now and 2050 under Paris Forever, global precipitation will likely increase by about 1.5 centimeters per year — approximately an additional 7,400 cubic kilometers (or nearly 2 quadrillion gallons) each year. By 2100, the total change in precipitation will most likely rise to about 4 cm/year (or 21,200 km3/yr) — nearly triple that of the mid-century change. Paris 2 C halves global precipitation increases, and Paris 1.5 C reduces them to almost a third of the Paris Forever increases. These aggressive mitigation scenarios convey considerable reductions in flood risk and associated adaptation costs.

    Reducing climate risk

    For the first time, the outlook explores two well-known sets of risks posed by climate change. Research highlighted in this report indicates that elevated climate-related physical risks will continue to evolve by mid-century, along with heightened transition risks that arise from shifts in the political, technological, social, and economic landscapes that are likely to occur during the transition to a low-carbon economy.

    “Our outlook shows that we could dramatically reduce overall climate risk through more ambitious and accelerated policy measures and investments aligned with meeting the Paris Agreement’s long-term 1.5 C or 2 C climate targets,” says MIT Joint Program Director Ronald Prinn. “Decision-makers in government, industry, and financial institutions can play a key role in moving us further along this path.” More