Duke Research Blog

Following the people and events that make up the research community at Duke.

Category: Statistics Page 1 of 4

Digging Into Durham’s Eviction Problem

This is what 20 years of evictions looks like. It’s an animated heat map of Durham, the streets overlaid with undulating blobs of red and orange and yellow, like a grease stain.

Duke students in the summer research program Data+ have created a time-lapse map of the more than 200,000 evictions filed in Durham County since 2000.

Dark red areas represent eviction hotspots. These neighborhoods are where families cook their favorite meals, where children do their homework, where people celebrate holidays. They’re also where many people live one crisis away from losing their neighbors, or becoming homeless themselves.

Duke junior Samantha Miezio points to a single census tract along NC 55 where, in the wake of an apartment building sale, more than 100 households received an eviction notice in that spot in one month alone. It “just speaks to the severity of the issue,” Miezio said.

Miezio was part of a team that spent 10 weeks this summer mapping and analyzing evictions data from the Durham County Sheriff’s Office, thanks to an effort by DataWorks NC to compile such data and make it more accessible.

The findings are stark.

Every hour in Durham, at least one renter is threatened with losing their home. About 1,000 eviction cases were filed a month against tenants between 2010 and 2017. That’s roughly one for every 280 residents in Durham, where evictions per capita is one of the highest in the state and double the national average.

The data tell us that while Durham’s evictions crisis has actually improved from where it was a few years ago, stubborn hotspots persist, said team member Ellis Ackerman, a math major at North Carolina State University.

When the students looked at the data month by month, a few things stood out. For one, winter evictions are common. While some countries such as France and Austria ban winter evictions to keep from pushing people onto the street in the cold, in Durham, “January is the worst month by far,” said team member Rodrigo Araujo, a junior majoring in computer science. “In the winter months utility bills are higher; they’re struggling to pay for that.”

Rodrigo Araujo (Computer Science, 2021) talks about the Durham evictions project.

The team also investigated the relationship between evictions and rents from 2012 to 2014 to see how much they move in tandem with each other. Their initial results using two years’ worth of rent data showed that when rents went up, evictions weren’t too far behind.

“Rents increased, and then two months later, evictions increased,” Miezio said.

But the impacts of rising rents weren’t felt evenly. Neighborhoods with more residents of color were significantly affected while renters in white neighborhoods were not. “This crisis is disproportionately affecting those who are already at a disadvantage from historical inequalities,” Miezio said.

A person can be evicted for a number of reasons, but most evictions happen because people get behind on their rent. The standard guideline is no more than 30% of your monthly income before taxes should go to housing and keeping the lights on.

But in Durham, where 47% of households rent rather than own a home, only half of renters meet that goal. As of 2019 an estimated 28,917 households are living in rentals they can’t afford.

The reason is incomes haven’t kept pace with rents, especially for low-wage workers such as waiters, cooks, or home health aides.

Durham’s median rents rose from $798 in 2010 to $925 in 2016. That’s out of reach for many area families. A minimum wage worker in Durham earning $7.25/hour would need to work a staggering 112 hours a week — the equivalent of nearly three full-time jobs — to afford a modest two-bedroom unit in 2019 at fair market rent, according to a report by the National Low Income Housing Coalition.

Spending a sizable chunk of your income on housing means having less left over for food, child care, transportation, savings, and other basic necessities. One unexpected expense or emergency — maybe the kid gets sick or the car needs repairs, or there’s a cut back on hours at work — can mean tenants have a harder time making the rent.

“Evictions are traumatic life experiences for the tenants,” and can have ripple effects for years, Miezio said.

Tenants may have only a few days to pay what’s due or find a new place and move out. The Sheriff may come with movers and pile a person’s belonging on the curb, or move them to a storage facility at the tenant’s expense.

A forced move can also mean children must change schools in the middle of the school year.

Benefits may go to the wrong address. Families are uprooted from their social support networks of friends and neighbors.

Not every case filed ends with the tenant actually getting forced out, “but those filings can still potentially inhibit their ability to find future housing,” Miezio said. Not to mention the cost and hassle of appearing in court and paying fines and court fees.

Multiple groups are working to help Durham residents avoid eviction and stay in their homes. In a partnership between Duke Law and Legal Aid of North Carolina, the Civil Justice Clinic’s 2-year-old Eviction Diversion Program provides free legal assistance to people who are facing eviction.

“The majority of people who have an eviction filed against them don’t have access to an attorney,” Miezio said.

In a cost-benefit analysis, the team’s models suggest that “with a pretty small increase in funding to reduce evictions, on the order of $100,000 to $150,000, Durham could be saving millions of dollars” in the form of reduced shelter costs, hospital costs, plus savings on mental health services other social services, Ackerman said.

Ellis Ackerman, a senior math major from NC State University, talks about the Durham evictions research project.

Moving forward, they’re launching a website in order to share their findings. “I’ve learned HTML and CSS this summer,” said Miezio, who is pursuing an individualized degree program in urban studies. “That’s one of the things I love about Data+. I’m getting paid to learn.”

Miezio plans to continue the project this fall through an independent study course focused on policy solutions to evictions, such as universal right to counsel.

“Housing access and stability are important to Durham,” said Duke’s vice president for Durham affairs Stelfanie Williams. “Applied research projects such as this, reflecting a partnership between the university and community, are opportunities for students to ‘learn by doing’ and to collaborate with community leaders on problem-solving.”

Data+ 2019 is sponsored by Bass Connections, the Rhodes Information Initiative at Duke, the Social Science Research Institute, the Duke Energy Initiative, and the departments of Mathematics and Statistical Science.

Other Duke sponsors include DTECH, Science, Law, and Policy Lab, Duke Health, Duke University Libraries, Sanford School of Public Policy, Nicholas School of the Environment, Duke Global Health Institute, Development and Alumni Affairs, the Duke River Center, Representing Migrations Humanities Lab, Energy Initiative, Franklin Humanities Institute, Duke Forge, the K-Lab, Duke Clinical Research, Office for Information Technology and the Office of the Provost, as well as the departments of Electrical & Computer Engineering, Computer Science, Biomedical Engineering, Biostatistics & Bioinformatics and Biology.

Government funding comes from the National Science Foundation. Outside funding comes from Exxon Mobil, the International Institute for Sustainable Development (IISD), Global Financial Markets Center, and Tether Energy.

Post by Robin Smith, Duke Office of News and Communications
Post by Robin Smith

Science in haiku: // Interdisciplinary // Student poetry

On Friday, August 2, ten weeks of research by Data+ and Code+ students wrapped up with a poster session in Gross Hall where they flaunted their newly created posters, websites and apps. But they weren’t expecting to flaunt their poetry skills, too! 

Data+ is one of the Rhodes Information Initiative programs at Duke. This summer, 83 students addressed 27 projects addressing issues in health, public policy, environment and energy, history, culture, and more. The Duke Research Blog thought we ought to test these interdisciplinary students’ mettle with a challenge: Transforming research into haiku.

Which haiku is your
favorite? See all of their
finished work below!

Eric Zhang (group members Xiaoqiao Xing and Micalyn Struble not pictured) in “Neuroscience in the Courtroom”
Maria Henriquez and Jake Sumner on “Using Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk for Student Athletes”
Samantha Miezio, Ellis Ackerman, and Rodrigo Aruajo in “Durham Evictions: A snapshot of costs, locations, and impacts”
Nikhil Kaul, Elise Xia, and Mikaela Johnson on “Invisible Adaptations”
Karen Jin, Katherine Cottrell, and Vincent Wang in “Data-driven approaches to illuminate the responses of lakes to multiple stressors”.

By Vanessa Moss

Kicking Off a Summer of Research With Data+

If the May 28 kickoff meeting was any indication, it’s going to be a busy summer for the more than 80 students participating in Duke’s summer research program, Data+.

Offered through the Rhodes Information Initiative at Duke  (iiD), Data+ is a 10-week summer program with a focus on data-driven research. Participants come from varied backgrounds in terms of majors and experience. Project themes range  from health, public policy, energy and environment, and interdisciplinary inquiry.

“It’s like a language immersion camp, but for data science,” said Ariel Dawn, Rhodes iiD Events & Communication Specialist. “The kids are going to have to learn some of those [programming] languages like Java or Python to have their projects completed,” Dawn said.

Dawn, who previously worked for the Office of the Vice Provost for Research, arrived during the program’s humble beginnings in 2015. Data+ began in 2014 as a small summer project in Duke’s math department funded by a grant from the National Science Foundation. The following year the program grew to 40 students, and it has grown every year since.

Today, the program also collaborates with the Code+ and CS+ summer programs, with  more than 100 students participating. Sponsors have grown to include major corporations such as Exxonmobil, which will fund two Data+ projects on oil research within the Gulf of Mexico and the United Kingdom in 2019.

“It’s different than an internship, because an internship you’re kind of told what to do,” said Kathy Peterson, Rhodes iiD Business Manager. “This is where the students have to work through different things and make discoveries along the way,” Peterson said.

From late May to July, undergraduates work on a research project under the supervision of a graduate student or faculty advisor. This year, Data+ chose more than 80 eager students out of a pool of over 350 applicants. There are 27 projects being featured in the program.

Over the summer, students are given a crash course in data science, how to conduct their study and present their work in front of peers. Data+ prioritizes collaboration as students are split into teams while working in a communal environment.

“Data is collected on you every day in so many different ways, sometimes we can do a lot of interesting things with that,” Dawn said.  “You can collect all this information that’s really granular and relates to you as an individual, but in a large group it shows trends and what the big picture is.”

Data+ students also delve into real world issues. Since 2013, Duke professor Jonathan Mattingly has led a student-run investigation on gerrymandering in political redistricting plans through Data+ and Bass Connections. Their analysis became part of a 205-page Supreme Court ruling.

The program has also made strides to connect with the Durham community. In collaboration with local company DataWorks NC, students will examine Durham’s eviction data to help identify policy changes that could help residents stay in their homes.

“It [Data+] gives students an edge when they go look for a job,” Dawn said. “We hear from so many students who’ve gotten jobs, and [at] some point during their interview employers said, ‘Please tell us about your Data+ experience.’”

From finding better sustainable energy to examining story adaptations within books and films, the projects cover many topics.

A project entitled “Invisible Adaptations: From Hamlet to the Avengers,” blends algorithms with storytelling. Led by UNC-Chapel Hill grad student Grant Class, students will make comparisons between Shakespeare’s work and today’s “Avengers” franchise.

“It’s a much different vibe,” said computer science major Katherine Cottrell. “I feel during the school year there’s a lot of pressure and now we’re focusing on productivity which feels really good.”

Cottrell and her group are examining the responses to lakes affected by multiple stressors.

Data+ concludes with a final poster session on Friday, August 2, from 2 p.m. to 4 p.m. in the Gross Hall Energy Hub. Everyone in the Duke Community and beyond is invited to attend. Students will present their findings along with sister programs Code+ and the summer Computer Science Program.

Writing by Deja Finch (left)
Art by Maya O’Neal (right)

Math on the Basketball Court

Boston Celtics data analyst David Sparks, Ph.D, really knew his audience Thursday, November 8, when he gave a presentation centered around the two most important themes at Duke: basketball and academics. He gave the crowd hope that you don’t have to be a Marvin Bagley III to make a career out of basketball — in fact, you don’t have to be an athlete at all; you can be a mathematician.

David Sparks (photo from Duke Political Science)

Sparks loves basketball, and he spends every day watching games and practices for his job. What career fits this description, you might ask? After graduating from Duke in 2012 with a Ph.D. in Political Science, Sparks went to work for the Boston Celtics, as the Director of Basketball Analytics. His job entails analyzing basketball data and building statistical models to ensure that the team will win.

The most important statistic when looking at basketball data is offensive / defensive efficiency, Sparks told the audience gathered for the “Data Dialogue” series hosted by the Information Initiative at Duke. Offensive efficiency translates to the number of points per possession while defensive efficiency measures how poorly the team forced the other offense to perform. These are measured with four factors: effective field goal percentage (shots made/ shots taken), turnover rate, successful rebound percentage, and foul rate. By looking at these four factors for both offensive and defensive efficiency, Sparks can figure out which of these areas are lacking, and share with the coach where there is room for improvement. “We all agree that we want to win, and the way you win is through efficiency,” Sparks said.

Since there is not a lot of room for improvement in the short windows between games during the regular season, a large component of Sparks’ job involves informing the draft and how the team should run practices during preseason.

David Sparks wins over his audience by showing Duke basketball clips to illustrate a point. Sparks spoke as part of the “Data Dialogue” series hosted by the Information Initiative at Duke.

Data collection these days is done by computer software. Synergy Sports Technology, the dominant data provider in professional basketball, has installed cameras in all 29 NBA arenas. These cameras are constantly watching and coding plays during games, tracking the locations of each player and the movements of the ball. They can analyze the amount of times the ball was touched and determine how long it was possessed each time, or recognize screens and calculate the height at which rebounds are grabbed. This software has revolutionized basketball analytics, because the implication of computer coding is that data scientists like Sparks can go back and look for new things later.

The room leaned in eagerly as Sparks finished his presentation, intrigued by the profession that is interdisciplinary at its core — an unlikely combination of sports and applied math. If math explains basketball, maybe we can all find a way to connect our random passions in the professional sphere.

Coding: A Piece of Cake

Image result for cake

Imagine a cake, your favorite cake. Has your interest been piqued?

“Start with Cake” has proved an effective teaching strategy for Mine Cetinkaya-Rundel in her introduction-level statistics classes. In her talk “Teaching Computing via Visualization,” she lays out her classroom approaches to helping students maintain an interest in coding despite its difficulty. Just like a cooking class, a taste of the final product can motivate students to master the process. Cetinkaya-Rundel, therefore, believes that instead of having students begin with the flour and sugar and milk, they should dive right into the sweet frosting. While bringing cake to the first day of class has a great success rate for increasing a class’s attention span (they’ll sugar crash in their next classes, no worries), what this statistics professor actually refers to is showing the final visualizations. By giving students large amounts of pre-written code and only one or two steps to complete during the first few class periods, they can immediately recognize coding’s potential. The possibilities become exciting and capture their attention so that fewer students attempt to vanish with the magic of drop/add period. For the student unsure about coding, immediately writing their own code can seem overwhelming and steal the joy of creating.

Example of a visualization Cetinkaya-Rundel uses in her classes

To accommodate students with less background in coding, Cetinkaya-Rundel believes that skipping the baby steps proves a better approach than slowing the pace. By jumping straight into larger projects, students can spend more time wrestling their code and discovering the best strategies rather than memorizing the definition of a histogram. The idea is to give the students everything on day one, and then slowly remove the pre-written coding until they are writing on their own. The traditional classroom approach involves teaching students line-by-line until they have enough to create the desired visualizations. While Cetinkaya-Rundel admits that her style may not suit every individual and creating the assignments does require more time, she stands by her eat-dessert-first perspective on teaching. Another way she helps students maintain their original curiosity is by cherishing day one through pre-installed packages which allow students to start playing with visualizations and altering code right away.

Not only does Cetinkaya-Rundel give mouth-watering cakes as the end results for her students but she also sometimes shows them burnt and crumbling desserts. “People like to critique,” she explains as she lays out how to motivate students to begin writing original code. When she gives her students a sloppy graph and tells them to fix it, they are more likely to find creative solutions and explore how to make the graph most appealing to them. As the scaffolding falls away and students begin diverging from the style guides, Cetinkaya-Rundel has found that they have a greater understanding of and passion for coding. A spoonful of sugar really does help the medicine go down.  

    Post by Lydia Goff

Becoming the First: Nick Carnes

Editor’s Note: In the “Becoming the First” series,  first-generation college student and Rubenstein Scholar Lydia Goff explores the experiences of Duke researchers who were the first in their families to attend college.

A portrait of Duke Professor Nick Carnes

Nick Carnes

Should we care that we are governed by professionals and millionaires? This is one of the questions Nick Carnes, an assistant professor in the Sanford School of Public Policy, seeks to answer with his research. He explores unequal social class representation in the political process and how it affects policy making. But do any real differences even exist between politicians from lower socioeconomic classes and those from the upper classes? Carnes believes they do, not only because of his research but also because of his personal experiences.

When Carnes entered Princeton University as a political science graduate student, he was the only member of his cohort who had done restaurant, construction or factory work. While obtaining his undergraduate degree from the University of Tulsa, he worked twenty hours a week and during the summer clocked in at sixty to seventy hours a week between two jobs. He considered himself and his classmates “similar on paper,” just like how politicians from a variety of socioeconomic classes can also appear comparable. However, Carnes noticed that he approached some problems differently than his classmates and wondered why. After attributing his distinct approach to his working class background, without the benefits of established college graduate family members (his mother did go to college while he was growing up), he began developing his current research interests.

Carnes considers “challenging the negative stereotypes about working class people” the most important aspect of his research. When he entered college, his first meeting with his advisor was filled with confusion as he tried to decipher what a syllabus was. While his working class status did restrict his knowledge of college norms, he overcame these limitations. He is now a researcher, writer, and professor who considers his job “the best in the world” and whose own story proves that working class individuals can conquer positions more often inhabited by the experienced. As Carnes states, “There’s no good reason to not have working class people in office.” His research seeks to reinforce that.

His biggest challenge is that the data he needs to analyze does not exist in a well-documented manner. Much of his research involves gathering data so that he can generate results. His published book, White-Collar Government: The Hidden Role of Class in Economic Policy Making, and his book coming out in September, The Cash Ceiling: Why Only the Rich Run for Office–and What We Can Do About It, contain the data and results he has produced. Presently, he is beginning a project on transnational governments because “cash ceilings exist in every advanced democracy.” Carnes’ research proves we should care that professionals and millionaires run our government. Through his story, he exemplifies that students who come from families without generations of college graduates can still succeed.    

 

Post by Lydia Goff

 

What is a Model?

When you think of the word “model,” what do you think?

As an Economics major, 
the first thing that comes to my mind is a statistical model, modeling phenomena such as the effect of class size on student test scores. A
car connoisseur’s mind might go straight to a model of their favorite vintage Aston
Martin. Someone else studying fashion even might imagine a runway model. The point is, the term “model” is used in popular discourse incredibly frequently, but are we even sure what it implies?

Annabel Wharton, a professor of Art, Art History, and Visual Studies at Duke, gave a talk entitled “Defining Models” at the Visualization Friday Forum. The forum is a place “for faculty, staff and students from across the university (and beyond Duke) to share their research involving the development and/or application of visualization methodologies.” Wharton’s goal was to answer the complex question, “what is a model?”

Wharton began the talk by defining the term “model,” knowing that it can often times be rather ambiguous. She stated the observation that models are “a prolific class of things,” from architectural models, to video game models, to runway models. Some of these types of things seem unrelated, but Wharton, throughout her talk, pointed out the similarities between them and ultimately tied them together as all being models.

The word “model” itself has become a heavily loaded term. According to Wharton, the dictionary definition of “model” is 9 columns of text in length. Wharton then stressed that a model “is an autonomous agent.” This implies that models must be independent of the world and from theory, as well as being independent of their makers and consumers. For example, architecture, after it is built, becomes independent of its architect.

Next, Wharton outlined different ways to model. They include modeling iconically, in which the model resembles the actual thing, such as how the video game Assassins Creed models historical architecture. Another way to model is indexically, in which parts of the model are always ordered the same, such as the order of utensils at a traditional place setting. The final way to model is symbolically, in which a model symbolizes the mechanism of what it is modeling, such as in a mathematical equation.

Wharton then discussed the difference between a “strong model” and a “weak model.” A strong model is defined as a model that determines its weak object, such as an architect’s model or a runway model. On the other hand, a “weak model” is a copy that is always less than its archetype, such as a toy car. These different classifications include examples we are all likely aware of, but weren’t able to explicitly classify or differentiate until now.

Wharton finally transitioned to discussing one of her favorite models of all time, a model of the Istanbul Hagia Sophia, a former Greek Orthodox Christian Church and later imperial mosque. She detailed how the model that provides the best sense of the building without being there is found in a surprising place, an Assassin’s Creed video game. This model is not only very much resembles the actual Hagia Sophia, but is also an experiential and immersive model. Wharton joked that even better, the model allows explorers to avoid tourists, unlike in the actual Hagia Sophia.

Wharton described why the Assassin’s Creed model is a highly effective agent. Not only does the model closely resemble the actual architecture, but it also engages history by being surrounded by a historical fiction plot. Further, Wharton mentioned how the perceived freedom of the game is illusory, because the course of the game actually limits players’ autonomy with code and algorithms.

After Wharton’s talk, it’s clear that models are definitely “a prolific class of things.” My big takeaway is that so many thing in our everyday lives are models, even if we don’t classify them as such. Duke’s East Campus is a model of the University of Virginia’s campus, subtraction is a model of the loss of an entity, and an academic class is a model of an actual phenomenon in the world. Leaving my first Friday Visualization Forum, I am even more positive that models are powerful, and stretch so far beyond the statistical models in my Economics classes.


By Nina Cervantes

David Carlson: Engineering and Machine Learning for Better Medicine

How can we even begin to understand the human brain?  Can we predict the way people will respond to stress by looking at their brains?  Is it possible, even, to predict depression based on observations of the brain?

These answers will have to come from sets of data, too big for human minds to work with on our own. We need mechanical minds for this task.

Machine learning algorithms can analyze this data much faster than a human could, finding patterns in the data that could take a team of researchers far longer to discover. It’s just like how we can travel so much faster by car or by plane than we could ever walk without the help of technology.

David Carlson Duke

David Carlson in his Duke office.

I had the opportunity to speak to David Carlson, an assistant professor of Civil and Environmental Engineering with a dual appointment at the Department of Biostatistics and Bioinformatics at Duke University.  Through machine learning algorithms, Carlson is connecting researchers across campus, from doctors to statisticians to engineers, creating a truly interdisciplinary research environment around these tools.

Carlson specializes in explainable machine learning: algorithms with inner workings comprehensible by humans. Most deep machine learning today exists in a “black box” — the decisions made by the algorithm are hidden behind layers of reasoning that give it incredible predictive power but make it hard for researchers to understand the “why” and the “how” behind the results. The transparent algorithms used by Carlson offer a way to capture some of the predictive power of machine learning without sacrificing our understanding of what they’re doing.

In his most recent research, Carlson collaborated with Dr. Kafui Dzirasa, associate professor of psychiatry and behavioral sciences and assistant professor in neurobiology and neurosurgery, on the effects of stress on the brains of mice, trying to understand the underlying causes of depression.

“What’s happening in neuroscience is the amount of data we’re sorting through is growing rapidly, and it’s really beginning to outstrip our ability to use classical tools,” Carlson says. “A lot of these classical tools made a lot more sense when you had these small data sets, but now we’re talking about this canonically overused word, Big Data”

With machine learning algorithms, it’s easier than ever to find trends in these huge sets of data.  In his most recent study, Carlson and his fellow researchers could find patterns tied to stress and even to how susceptible a mouse was to depression. By continuing this project and looking at new ways to investigate the brain and check their results, Carlson hopes to help improve treatments for depression in the future.

In addition to his ongoing research into depression, Carlson has brought machine learning to a number of other collaborations with the medical center, including research into autism and patient care for diabetes. When there’s too much data for the old ways of data analysis, machine learning can step in, and Carlson sees potential in harnessing this growing technology to improve health and care in the medical field.

“What’s incredibly exciting is the opportunities at the intersection of engineering and medicine,” he said. “I think there’s a lot of opportunities to combine what’s happening in the engineering school and also what’s happening at the medical center to try to create ways of better treating people and coming up with better ways for making people healthier.”

Guest Post by Thomas Yang, a junior at North Carolina School of Math and Science.

Generating Winning Sports Headlines

What if there were a scientific way to come up with the most interesting sports headlines? With the development of computational journalism, this could be possible very soon.

Dr. Jun Yang is a database and data-intensive computing researcher and professor of Computer Science at Duke. One of his latest projects is computational journalism, in which he and other computer science researchers are considering how they can contribute to journalism with new technological advances and the ever-increasing availability of data.

An exciting and very relevant part of his project is based on raw data from Duke men’s basketball games. With computational journalism, Yang and his team of researchers have been able to generate diverse player or team factoids using the statistics of the games.

Grayson Allen headed for the hoop.

Grayson Allen headed for the hoop.

An example factoid might be that, in the first 8 games of this season, Duke has won 100% of its games when Grayson Allen has scored over 20 points. While this fact is obvious, since Duke is undefeated so far this season, Yang’s programs will also be able to generate very obscure factoids about each and every player that could lead to unique and unprecedented headlines.

While these statistics relating player and team success can only imply correlation, and not necessarily causation, they definitely have potential to be eye-catching sports headlines.

Extracting factoids hasn’t been a particularly challenging part of the project, but developing heuristics to choose which factoids are the most relevant and usable has been more difficult.

Developing these heuristics so far has involved developing scoring criteria based on what is intuitively impressive to the researcher. Another possible measure of evaluating the strength of a factoid is ranking the types of headlines that are most viewed. Using this method, heuristics could, in theory, be based on past successes and less on one researcher’s human intuition.

Something else to consider is which types of factoids are more powerful. For example, what’s better: a bolder claim in a shorter period of time, or a less bold claim but over many games or even seasons?

The ideal of this project is to continue to analyze data from the Duke men’s basketball team, generate interesting factoids, and put them on a public website about 10-15 minutes after the game.

Looking forward, computational journalism has huge potential for Duke men’s basketball, sports in general, and even for generating other news factoids. Even further, computational journalism and its scientific methodology might lead to the ability to quickly fact-check political claims.

Right now, however, it is fascinating to know that computer science has the potential to touch our lives in some pretty unexpected ways. As our current men’s basketball beginning-of-season winning streak continues, who knows what unprecedented factoids Jun Yang and his team are coming up with.

By Nina Cervantes

Who Gets Sick and Why?

During his presentation as part of the Chautauqua lecture series, Duke sociologist Dr. Tyson Brown explained his research exploring the ways racial inequalities affect a person’s health later in life. His project mainly looks at the Baby Boomer generation, Americans born between 1946 and 1964.

With incredible increases in life expectancy, from 47 years in 1900 to 79 today, elderly people are beginning to form a larger percentage of the population. However among black people, the average life expectancy is three and a half years shorter.

“Many of you probably do not think that three and half years is a lot,” Brown said. “But imagine how much less time that is with your family and loved ones. In the end, I think all of us agree we want those extra three and a half years.”

Not only does the black population in America have shorter lives on average but they also tend to have sicker lives with higher blood pressures, greater chances of stroke, and higher probability of diabetes. In total, the number of deaths that would be prevented if African-American people had the same life expectancy as white people is 880,000 over a nine-year span. Now, the question Brown has challenged himself with is “Why does this discrepancy occur?”

Brown said he first concluded that health habits and behaviors do not create this life expectancy gap because white and black people have similar rates of smoking, drinking, and illegal drug use. He then decided to explore socioeconomic status. He discovered that as education increases, mortality decreases. And as income increases, self-rated health increases. He said that for every dollar a white person makes, a black person makes 59 cents.

This inequality in income points to the possible cause for the racial inequality in health, he said.  Additionally, in terms of wealth instead of income, a black person has 6 cents compared to the white person’s dollar. Possibly even more concerning than this inconsistency is the fact that it has gotten worse, not better, over time. Before the 2006 recession, blacks had 10-12 cents of wealth for every white person’s dollar.

Brown believes that this financial stress forms one of many stressors in black lives including chronic stressors, everyday discrimination, traumatic events, and neighborhood disorder which affect their health.

Over time, these stressors create something called physiological dysregulation, otherwise known as wear and tear, through repeated activation of  the stress response, he said. Recognition of the prevalence of these stressors in black lives has lead to Brown’s next focus on the extent of the effect of stressors on health. For his data, he uses the Health and Retirement Study and self-rated health (proven to predict mortality better than physician evaluations). For his methods, he employs structural equation modeling. Racial inequalities in socioeconomic resources, stressors and biomarkers of physiological dysregulation collectively explain 87% of the health gap with any number of causes capable of filling the remaining percentage.

Brown said his next steps include using longitudinal and macro-level data on structural inequality to understand how social inequalities “get under the skin” over a person’s lifetime. He suggests that the next steps for society, organizations, and the government to decrease this racial discrepancy rest in changing economic policy, increasing wages, guaranteeing work, and reducing residential segregation.

Post by Lydia Goff

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