Monthly Archives: August 2013

Princeton researchers use mobile phones to measure happiness (Demography)

By Tara Thean, Science-Writing Intern, Office of the Dean for Research

World map

Locations of study subjects on world map (Source: Demography)

Researchers at Princeton University are developing ways to use mobile phones to explore how one’s environment influences one’s sense of well-being.

In a study involving volunteers who agreed to provide information about their feelings and locations, the researchers found that cell phones can efficiently capture information that is otherwise difficult to record, given today’s on-the-go lifestyle. This is important, according to the researchers, because feelings recorded “in the moment” are likely to be more accurate than feelings jotted down after the fact.

To conduct the study, the team created an application for the Android operating system that documented each person’s location and periodically sent the question, “How happy are you?”

The investigators invited people to download the app, and over a three-week period, collected information from 270 volunteers in 13 countries who were asked to rate their happiness on a scale of 0 to 5. From the information collected, the researchers created and fine-tuned methods that could lead to a better understanding of how our environments influence emotional well-being. The study was published in the June issue of Demography.

The mobile phone method could help overcome some of the limitations that come with surveys conducted at people’s homes, according to the researchers. Census measurements tie people to specific areas — the census tracts in which they live — that are usually not the only areas that people actually frequent.

“People spend a significant amount of time outside their census tracks,” said John Palmer, a graduate student in the Woodrow Wilson School of Public and International Affairs and the paper’s lead author. “If we want to get more precise findings of contextual measurements we need to use techniques like this.”

Palmer teamed up with Thomas Espenshade, professor of sociology emeritus, and Frederic Bartumeus, a specialist in movement ecology at the Center for Advanced Studies of Blanes in Spain, along with Princeton’s Chang Chung, a statistical programmer and data archivist in the Office of Population Research; Necati Ozgencil, a former Professional Specialist at Princeton; and Kathleen Li, who earned her undergraduate degree in computer science from Princeton in 2010, to design the free, open source application for the Android platform that would record participants’ locations at various intervals based on either GPS satellites or cellular tower signals.

Though many of the volunteers lived in the United States, some were in Australia, Canada, China, France, Germany, Israel, Japan, Norway, South Korea, Spain, Sweden and the United Kingdom.

Palmer noted that the team’s focus at this stage was not on generalizable conclusions about the link between environment and happiness, but rather on learning more about the mobile phone’s capabilities for data collection. “I’d be hesitant to try to extend our substantive findings beyond those people who volunteered.” he said.

However, the team did obtain some preliminary results regarding happiness: for example, male subjects tended to describe themselves as less happy when they were further from their homes, whereas females did not demonstrate a particular trend with regards to emotions and distance.

“One of the limitations of the study is that it is not representative of all people,” Palmer said. Participants had to have smartphones and be Internet users. It is also possible that people who were happy were more likely to respond to the survey. However, Palmer said, the study demonstrates the potential for mobile phone research to reach groups of people that may be less accessible by paper surveys or interviews.

Palmer’s doctoral dissertation will expand on this research, and his adviser Marta Tienda, the Maurice P. During Professor in Demographic Studies, said she was excited to see how it will impact the academic community. “His applied research promises to redefine how social scientists understand intergroup relations on many levels,” she said.

This study involved contributions from the Center for Information Technology Policy at Princeton University, with institutional support from the National Institutes of Health Training Grant T32HD07163 and Infrastructure Grant R24HD047879.

Read the abstract.

Palmer, John R. B., Thomas J. Espenshade, Frederic Bartumeus, Chang Y. Chung, Necati Ercan Ozgencil and Kathleen Li. 2013. New Approaches to Human Mobility: Using Mobile Phones for Demographic Research. Demography 50:1105–1128. DOI 10.1007/s13524-012-0175-z

How will crops fare under climate change? Depends on how you ask (Global Change Biology)

Research image

Mechanistic (top row) and empirical (bottom row) simulations compared recent, or “baseline,” maize production in South Africa (1979-99) to projected future production under climate change (2046-65). While both models showed a reduction in output, the third column shows that the empirical model estimated a widespread yield loss of around 10 percent (in yellow), while the mechanistic model showed several areas of increased production (in green). (Image by Lyndon Estes)

Research image 2

For wheat, the mechanistic model (top row) projected greater wheat yields, while the empirical model (bottom row) suggested that wheat-growing areas would expand by almost 50 percent. (Image by Lyndon Estes)

By Morgan Kelly, Office of Communications

The damage scientists expect climate change to do to crop yields can differ greatly depending on which type of model was used to make those projections, according to research based at Princeton University. The problem is that the most dire scenarios can loom large in the minds of the public and policymakers, yet neither audience is usually aware of how the model itself influenced the outcome, the researchers said.

The report in the journal Global Change Biology is one of the first to compare the agricultural projections generated by empirical models — which rely largely on field observations — to those by mechanistic models, which draw on an understanding of how crop growth and development are affected by the environment. Building on similar studies from ecology, the researchers found yet more evidence that empirical models may show greater losses as a result of climate change, while mechanistic models may be overly optimistic.

The researchers ran an empirical and a mechanistic model to see how maize and wheat crops in South Africa — the world’s ninth largest maize producer, and sub-Saharan Africa’s second largest source of wheat — would fare under climate change in the years 2046 to 2065. Under the hotter, wetter conditions projected by the climate scenarios they used, the empirical model estimated that maize production could drop by 3.6 percent, while wheat output could increase by 6.2 percent. Meanwhile, the mechanistic model calculated that maize and wheat yields might go up by 6.5 and 15.2 percent, respectively.

In addition, the empirical model estimated that suitable land for growing wheat would drop by 10 percent, while the mechanistic model found that it would expand by 9 percent. The empirical model projected a 48 percent expansion in wheat-growing areas, but the mechanistic reported only 20 percent growth. In regions where the two models overlapped, the empirical model showed declining yields while the mechanistic model showed increases. These wheat models were less accurate, but still indicative of the vastly different estimates empirical and mechanistic can produce, the researchers wrote.

Disparities such as these aren’t just a concern for climate-change researchers, said first author Lyndon Estes, an associate research scholar in the Program in Science, Technology and Environmental Policy in Princeton’s Woodrow Wilson School of Public and International Affairs. Impact projections are crucial as people and governments work to understand and address climate change, but it also is important that people understand how they are generated and the biases inherent in them, Estes said. The researchers cite previous studies that suggest climate change will reduce South African maize and wheat yields by 28 to 30 percent — according to empirical studies. Mechanistic models project a more modest 10 to 19 percent loss. What’s a farmer or government minister to believe?

“A yield projection based only on empirical models is likely to show larger yield losses than one made only with mechanistic models. Neither should be considered more right or wrong, but people should be aware of these differences,” Estes said. “People who are interested in climate-change science should be aware of all the sources of uncertainty inherent in projections, and should be aware that scenarios based on a single model — or single class of models — are not accounting for one of the major sources of uncertainty.”

The researchers’ work relates to a broader effort in recent years to examine the biases introduced into climate estimates by the models and data scientists use, Estes said. For instance, a paper posted Aug. 7 by Global Change Biology — and includes second author and 2011 Princeton graduate Ryan Huynh — challenges predictions that higher global temperatures will result in the widespread extinction of cold-blooded forest creatures, particularly lizards. These researchers say that a finer temperature scale than existing projections use suggests that many cold-blooded species would indeed thrive on a hotter Earth.

Scientists are aware of the differences between empirical and mechanistic models, said Estes, who was prompted by a similar comparison that showed an empirical-mechanistic divergence in tree-growth models. Yet, only one empirical-to-mechanistic comparison (of which Estes also was first author) has been published in relation to agriculture — and it didn’t even examine the impact of climate change.

The solution would be to use both model classes so that researchers could identify each class’s biases and correct for it, Estes said. Each model has different strengths and weaknesses that can be complementary when combined.

Simply put, empirical models are built by finding the relationship between observed crop yields and historical environmental conditions, while mechanistic models are built on the physiological understanding of how the plant grows and reproduces in response to a range of conditions. Empirical models, which are simpler and require fewer inputs, are a staple in studying the possible effects of climate change on ecological systems, where the data and knowledge about most species is largely unavailable. Mechanistic models are more common in studying agriculture because there is a much greater wealth of data and knowledge that has accumulated over several thousand years of agricultural development, Estes said.

“These two model classes characterize different portions of the environmental space, or niche, that crops and other species occupy,” Estes said. “Using them together gives us a better sense of the range of uncertainty in the projections and where the errors and limitations are in the data and models. Because the two model classes have such different structures and assumptions, they also can improve our confidence in scenarios where their findings agree.”

Read the abstract.

Estes, Lyndon D., Hein Beukes, Bethany A. Bradley, Stephanie R. Debats, Michael Oppenheimer, Alex C. Ruane, Roland Schulze and Mark Tadross. 2013. Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches. Global Change Biology. Accepted, unedited article first published online: July 17, 2013. DOI: 10.1111/gcb.12325

The work was funded by the Princeton Environmental Institute‘s Grand Challenges Program.