Tag: Data

School bus routes are expensive and hard to plan. We calculated a better way

Here’s a math problem even the brightest school districts struggle to solve: getting hordes of elementary, middle and high school students onto buses and to school on time every day. The Conversation

Transporting all of these pupils presents a large and complex problem. Some school districts use existing software systems to develop their bus routes. Others still develop these routes manually.

In such problems, improving operational efficiency even a little could result in great advantages. Each school bus costs school districts somewhere between US$60,000 and $100,000. So, scheduling the buses more efficiently will result in significant monetary savings.

Over the past year, we have been working with the Howard County Public School System (HCPSS) in Maryland to analyze its transportation system and recommend ways to improve it. We have developed a way to optimize school bus routes, thanks to new mathematical models.

Finding the optimal solution to this problem is very valuable, even if that optimal solution is only slightly better than the current plan. A solution that is only one percent worse would require a considerable number of additional buses due to the size of the operation.

By optimizing bus routes, schools can cut down on costs, while still serving all of the children in their district. Our analysis shows that HCPSS can save between five and seven percent on the number of buses needed.

Route planning

A bus trip in the afternoon starts from a given school and visits a sequence of stops, dropping off students until the bus is empty. A route is a sequence of trips from different schools that are linked together to be served by one bus.

Our goal was to reduce both the total time buses run without students on board – also known as aggregate deadhead time – as well as the number of routes. Fewer routes require fewer buses since each route is assigned to a single bus. Our approach uses data analysis and mathematical modeling to find the optimal solution in a relatively short time.

To solve this problem, a computer algorithm considers all of the bus trips in the district. Without modifying the trips, the algorithm assigns them to routes such that the aggregate deadhead time and the number of routes are minimized. Individual routes become longer, allowing the bus to serve more trips in a single route.

Since the trips are fixed, in this way we can decrease the total time the buses are en route. Minimizing the deadhead travel results in cost savings and reductions in air pollution.

The routes that we generated can be viewed as a lower bound to the number of buses needed by school districts. We can find the optimal solution for HCPSS in less than a minute.

Serving all students

While we were working on routes, we decided to also tackle the problem of the bus trips themselves. To do this, we needed to determine what trips are required to serve the students for each school in the system, given bus capacities, stop locations and the number of students at each stop. This has a direct impact on how routes are chosen.

Most existing models aim to minimize either the total travel time or the total number of trips. The belief in such cases is that, by minimizing the number of trips, you can minimize the number of buses needed overall.

However, our work shows that this is not always the case. We found a way to cut down on the number of buses needed to satisfy transportation demands, without trying to minimize either of the above two objectives. Our approach considers not only minimizing the number of trips but also how these trips can be linked together.

New start times

Last October, we presented our work at the Maryland Association of Pupil Transportation conference. An audience member at that conference suggested that we analyze school start and dismissal times. By changing the high school, middle school and elementary school start times, bus operations could potentially be even more efficient. Slight changes in school start times can make it possible to link more trips together in a single bus route, hence decreasing the number of buses needed overall.

We developed a model that optimizes the school bell times, given that each of the elementary, middle and high school start times fall within a prespecified time window. For example, the time window for elementary school start times would be from 8:15 to 9:25 a.m.; for middle schools, from 7:40 to 8:30 a.m.; and all high schools would start at 7:25 a.m.

Our model looks at all of the bus trips and searches for the optimal combination of school dismissal time such that the number of school buses, which is the major contributing factor to costs, is minimized. We found that, in most cases, optimizing the bell times results in significant savings regarding the number of buses.

Next steps

Using our model, we ran many different “what if?” scenarios using different school start and dismissal times for the HCPSS. Four of these are currently under consideration by the Howard County School Board for possible implementation.

We are also continuing to enhance our current school bus transportation models, as well developing new ways to further improve efficiency and reduce costs.

For example, we are building models that can help schools select the right vendors for their transportation needs, as well as minimize the number of hours that buses run per day.

In the future, the type of models we are working on could be bundled into a software system that schools can use by themselves. There is really no impediment in using these types of systems as long as the school systems have an electronic database of their stops, trips, and routes.

Such software could potentially be implemented in all school districts in the nation. Many of these districts would benefit from using such models to evaluate their current operations and determine if any savings can be realized. With many municipalities struggling with budgets, this sort of innovation could save money without degrading service.

Ali Haghani, Professor of Civil & Environmental Engineering, University of Maryland and Ali Shafahi, Ph.D. Candidate in Computer Science, University of Maryland

Photo Credit: Dean Hochman

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This article was originally published on The Conversation. Read the original article.


Can Trump resist the power of behavioral science’s dark side like other politicians?

More than two dozen governments, including the U.S., now have a team of behavioral scientists tasked with trying to improve bureaucratic efficiency to “nudge” their citizens toward what they deem to be higher levels of well-being.

A few recent examples include a push by the socialist French government to increase the numbers of organ donors, a conservative UK government plan to prevent (costly) missed doctor appointments, and efforts by the Obama White House to boost voter turnout on Election Day.

While the government’s use of our psychological quirks to affect behavior rubs some people the wrong way, most of us can agree that the above examples achieve positive ends. More organ donors mean more lives saved, fewer missed doctor appointments mean the government or health industry is more efficient, and increased voting means stronger citizen engagement in democracy.

But “nudges” themselves are value neutral. That is, they can be used to both achieve altruistic ends or more malicious ones. Just as behavioral science can be used to increase voter turnout, it can also be used to suppress the votes of specific individuals likely to favor the opposing side, as reportedly happened in the recent U.S. presidential election.

The nudge, in other words, has a dark side.

My research explores how behavioral science can help people follow through on their intentions where they make better or longer-term choices that increase their well-being. Because choices are influenced by the environment in which they are made, changing the environment can change decision outcomes.

This can be positive to the extent that those designing interventions have good intentions. But what happens when someone uses these insights to systematically influence others’ behavior to favor his or her own interests – even at the expense of everyone else’s?

That’s my concern with President Donald Trump, whose campaign appears to have exploited behavioral science to suppress the vote of Hillary Clinton supporters.

What’s in a nudge?

Behavioral science is a relatively young field, and governments have only recently begun using its insights to inform public policy.

The UK was the first in 2010 when it created its Behavioral Insights Team. In subsequent years, dozens of governments around the world followed, including Canada with its Behavioral Insights Unit and the U.S., which in 2015 officially launched the White House Social and Behavioral Sciences Team.

The teams’ missions are all relatively similar: to leverage insights from behavioral science to make public services more cost-effective and easier to use, to help people make better choices for themselves, and to improve well-being.

In the UK, for example, the Behavioral Insights Team was able to persuade about 100,000 more people a year to donate their organs by tweaking a message people received when renewing their car tax. Here in the U.S., the Social and Behavioral Science Team helped the Department of Defense increase the amount of retirement savings accounts for service members by 8.3 percent.

These kinds of interventions have been criticized for unjustly interfering with an individual’s autonomy. Some even compare it with mind-control.

However, as I have pointed out elsewhere, our environment (and the government) is always exerting some influence on our behavior, so we’re always being nudged. The question is therefore not whether we will be nudged, but how and in what direction.

For example, when you sit down to dinner, the size of your plate can make a big difference in how much you eat. Studies show you’re more likely to consume less food if you use a smaller plate. So if the government is handing out the dinnerware, and if most us wanted to avoid overeating, why not set the default plate to a small one?

But now let’s consider the dark side: a restaurant might hand out a small plate if it means it can charge more for less food and thus make more money. The owner likely doesn’t care about your waist size.

Any intervention based on behavioral science is therefore neither good nor bad. What matters is the intention behind it, the aim which the nudge is ultimately supposed to help achieve.

Potential for abuse

Take the case of what Cambridge Analytica – a company founded in 2013 and reportedly funded by the family of billionaire conservative donor Robert Mercer – did during the election. This team of data scientists and behavioral researchers claims to have collected thousands of data points on 220 million Americans in order to “model target audience groups and predict the behavior of like-minded people.”

Essentially, all that data can be used to deduce individual’s personality traits and then send them messages that match their personality, which are more likely to be persuasive. For example, highly neurotic Jane will be more receptive to a political message that promises safety, as opposed to financial gains, which may be more compelling to conscientious Joe.

So what’s the problem? In and of itself, this analysis can be a neutral tool. A government might want to use this approach to provide helpful information to at-risk populations, for example by providing suicide prevention hotlines to severely depressed individuals, as Facebook is currently doing. One might even argue that Cambridge Analytica, first hired by the Cruz campaign and later by Trump, was not acting unethically when it sent such personalized messaging to convince undecided voters to support the eventual Republican nominee. After all, this is what all marketing campaigns set out to do.

But there is a fine ethical line here that behavioral science can make easier to cross. In the same way that people can be influenced to engage in a behavior, they may also be discouraged from doing so. Bloomberg reported that Cambridge Analytica identified likely Clinton voters such as African-Americans and tried to dissuade them from going to the ballot box. The company denies discouraging any Americans from casting their vote.

Beyond hiring the company, the Trump administration has a direct tie to Cambridge Analytica through chief strategist Steve Bannon, who sits on its board.

Alexander Nix, CEO of Cambridge Analytica, talks about what his company does.

How might Trump nudge?

So far, it’s unclear whether or how the Trump administration might use behavioral science in the White House.

Trump, like most Republicans, has emphasized his desire to make government more efficient. Since behavioral science is generally a low-cost intervention strategy that provides tangible, measurable gains that should appeal to a business-minded president, Trump may very well turn to its insights to accomplish this goal. After all, the UK’s Behavioral Insights Team was kicked off under conservative leadership.

The White House Social and Behavioral Science Team’s impressive interventions have led to hundreds of millions of dollars in savings across a variety of departments and at the same time increased the well-being of millions of citizens. The future of the team is now unclear. Some members are worried that Trump will use their skills in less benevolent ways.

Trump’s apparent use of Cambridge Analytica to suppress Clinton turnout, however, is not a good sign. More broadly, the president does not seem to value ethics. Despite repeated warnings from government ethics watchdogs, he refuses to seriously deal with his innumerable conflicts of interest. Without the release of his tax returns, the true extent of his conflicts remain unknown.

And as we know from behavioral science, people frequently underestimate the influence conflicts of interests have on their own behavior.

In addition, studies show that people can easily set aside moral concerns in the pursuit of efficiency or other specific goals. People are also creative in rationalizing unethical behavior. It doesn’t seem to be a stretch to imagine that Trump, given his poor track record where ethics is concerned, could cross the fine ethical line and abuse behavioral science for self-serving ends.

A virus and a cure

Behavioral science has been heralded as part of the solution to many societal ills.

Behavioral economists Richard Thaler and Cass Sunstein, co-authors of the book “Nudge” coining the term, have been strong advocates of using the field’s tools to improve government policy – when the intentions are transparent and in the public interest.

But might the current administration use them in ways that go against our own interests? The problem is that we may not even be aware when it happens. People are often unable to tell whether they are being nudged and, even if they are, may be unable to tell how it’s influencing their behavior.

Governments around the world have found success using the burgeoning field of behavioral science to improve the efficiency of their policies and increase citizens’ well-being. While we should continue to find new ways to do this, we also need clear guidelines from Congress on when and how to use behavioral science in policy. That would help ensure the current or a future occupant of the White House doesn’t cross the line into the dark side of nudges.

The Conversation

Jon M Jachimowicz, PhD Student in Management, Columbia University

Photo Credit: Keyword Suggest

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This article was originally published on The Conversation. Read the original article.

What are some labor market challenges for black Americans?

By Matt Johnson

The mainstream media tends to focus on the simple things when it comes to discrepancies between black and white folks. However, it is always much more complex than they report.

In this blog, I have reported crime rates in depressed neighborhoods and wards of urban environments. I have also reported high unemployment rates and low education attainment rates. And I have also reported on decaying housing conditions such as foreclosures and condemned and vacant buildings.

I have illustrated in many articles that these areas of depression tend to be areas that are predominantly black. Sometimes I have done this explicitly and in other times I have done it implicitly. But either way, I have always included and highlighted these issues as multi-variable problems. That is, I have demonstrated that it is not just one problem; I have demonstrated it is a multitude of problems.

But what I have not yet written about is the labor market for this demographic group. I have not yet highlighted or discussed the importance of an education and how that would provide opportunity; and I have not yet highlighted or discussed the importance of skills and acquiring skills and how that would provide opportunity.

Moreover, I have not yet highlighted or discussed the importance of industry and how picking an industry will lead to greater wages and job security and how that would provide opportunity; and I have not yet highlighted or discussed geography and how that would provide access to a greater number of jobs and opportunity.

In this article, I will provide a short explanation of 4 factors that affect entrance into the labor market for a worker along with data illustrating the current location of black Americans.


One could make an argument that this is the beginning of the road. Why is this? Because success in life is correlated with education, and ridiculously so. In almost every economic measure, a person who has a degree has a higher probability of making more money and a higher probability of job security, although there is variation between industries.

On June 6, 2016, The Brookings Institute published an article on 7 findings that illustrate racial disparities in education. Here is the list of those findings:

  1. School readiness gaps are improving, except for black kids
  2. Misbehavior in school can pay off for white, but not black students
  3. Teacher-student racial mismatch harms black kids
  4. White and Asian students are more likely to be exposed to advanced classes
  5. Gaps remain in high school completion rates
  6. Similar college enrollment rates mask unequal degree completion rates
  7. Black and white students do not attend colleges of equal quality

As with all science, more research is needed in these subsequent areas. In addition, one ought to ask the question, where are the issues more likely to take place?

Is a researcher more likely to find these disparities in a stable economic system with low unemployment, high education and income attainments, and low crime rates? Or is a researcher more than likely to find these disparities in an unstable system with high unemployment, low education and income attainments, and high crime rates?


As a person goes through life, their skill set will increase. And as their skill set increases so will their pay, which means a person will attain greater earning and purchasing power as they get older. And this is the case for all racial groups. So what are some factors that may influence earning potential?

First, an initial job during teenage years will increase one’s earnings potential over the course of a life-time. This is because teenagers will begin to learn basic market skills and an intuition of how the market works. However, the unemployment rates among racial groups between the ages 16 and 24 are divergent.

If one is black and male, or hispanic and male, then his unemployment rate is higher than the national average. Essentially, both groups are starting from the rear of the market earnings race. In contrast, if one is Asian and female, or white and female, then her unemployment rate is lower than the national average.

Here are the statistics:


Education will also affect skills. The market is built on science and math, and how science and math perpetuate market engines.

We published an article last summer titled Top 10 Paying Bachelor’s Degrees. In it, we shared with our readers which degrees were the top 10 earners straight out of college. All 10 were engineering degrees:

Rank Major Degree Type Early Career Pay
1 Petroleum Engineering Bachelor’s $101,000
2 Mining Engineering Bachelor’s $71,500
3 Chemical Engineering Bachelor’s $69,500
4 Computer Science Bachelor’s $69,100
5 Computer Engineering Bachelor’s $68,400
6 Nuclear Engineering Bachelor’s $68,200
7 Systems Engineering Bachelor’s $67,100
8 Electrical & Computer Engineering Bachelor’s $67,000
9 Electrical Engineering Bachelor’s $66,500
10 Aeronautical Engineering Bachelor’s $65,100

This of course doesn’t mean other degrees don’t pay well straight out of college or don’t have high potential earnings over the course of a life-time. However, what it does show is degrees in science, technology, engineering, and mathematics will pay dividends for those who obtain those degrees. In addition, degrees in economics and finance are competitive degrees in the marketplace. For example, those in finance usually have the highest weekly average wages of all industries in the Minneapolis/St. Paul market.

Industry can dramatically decide the potential earnings for a worker in the marketplace. As previously stated, those who work in the mathematical sciences, engineering, and finance industries are earning more out of the gate and over a life-time.

And so the question is, how do black Americans compare by industry? With a little help from BlackDemographics.com, the reader can see that black Americans

are again overrepresented in government jobs such as education, social assistance, and public administration. African-Americans also have a large presence in the health care industry which is expected to see substantial job growth for the foreseeable future.

Here’s the data provided by BlackDemographics.com:



This last category can also dramatically affect someone’s entrance in the marketplace. This is because American neighborhoods are still relatively segregated by racial group. For example, Milwaukee’s segregation was highlighted after the police shooting death of Sylville Smith in August of 2016. According to Business Insider, Milwaukee is the most segregated city in the United States. As this map illustrates, black Americans are highly concentrated in two distinct areas:

Milwaukee. Photo: Courtesy of the University of Virginia Weldon Cooper Center for Public Service.

The green dots are black Americans while the blue dots are white Americans. Of course a thorough geographical analysis of each American city would send this point home. But immediately, research by this publication has demonstrated correlation between black Americans and depressed environments which include adverse socio-economic factors such as high unemployment, relatively low earnings compared to other racial groups, low education rates, high crime rates, and disparate housing issues in the form of foreclosures and condemned and vacant buildings.

One last thought to consider, black businesses are more than likely to hire black employees while white businesses are more than likely to hire white employees despite federal laws. And this hiring behavior seems to make sense based off of geographical data.

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.

Photo credit: Pixabay



Copyright ©2017 – The Systems Scientist

In 2016, crime was ‘Up’ overall in Minneapolis but…

By Matt Johnson

After a first pass through the Minneapolis crime data, it appears reported crimes in Minneapolis increased in 2016. However, they didn’t increase by much. In total numbers, reported crimes increased from 21,341 in 2015 to 21,485 in 2016. That’s 144 reported crimes.

As a percent, that’s less than 1 percent. But of course, this crime data only tells us about the total number of crimes for the city of Minneapolis. It doesn’t tell us anything about where the majority of these crimes happened nor does it tell us anything about the types of crimes that are most prominent in these locations. And of course, it really does depend on location.


For example, North Minneapolis has some of the highest crime rates per square mile in the city. But not all neighborhoods and zip codes are created equal when it comes to crime. There are certain neighborhoods that experience much higher crime rates than others.

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The Jordan neighborhood, which resides in the central part of North Minneapolis in the 5th Ward, experienced the highest number of crimes and the highest number of crimes per square mile on the north side in 2015 and 2016. But the number of crimes and the crimes per square mile in the Jordan neighborhood decreased in 2016. In other words, they were higher in 2015.

The Jordan neighborhood is the only neighborhood in Minneapolis that is predominantly black.

In contrast, the crimes per square mile on average are much lower in the Harrison and Sumner Glenwood neighborhoods in the 5th Ward on the north side. And of course, the 4th Ward, which also resides on the north side, has its neighborhoods that are relatively quiet when it comes to crime and others that are active with higher numbers of crimes.

Harrison and Sumner Glenwood are predominantly white.

As my regular readers know, crime is usually associated with other adverse socio-economic factors such as higher rates of unemployment, lower rates of education, and housing issues. Sometimes this is referred to as urban decay or urban blight. But in the case of my research, I am using mathematical methods to lead me to this knowledge of urban environments and their respective discrepancies. But there are instances where I have found crime does exist on its own.

For Minneapolis, this happens in downtown Minneapolis, specifically in the Downtown West neighborhood, which experiences the highest number of crimes in the city month after month.

Why is this so? Well this is a question I will leave for you to ponder. Other questions you might think about as well are, why do these adverse socio-economic factors exist together with very few exceptions? Are policy makers aware of these facts? And if they are, why haven’t they done anything about it?

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.

Photo credit: Tony Webster




Copyright ©2017 – The Systems Scientist

What were the unemployment rates by race in 2016?

TSS Admin

The good news is the unemployment rates for all American groups have been trending downward since the Great Recession. However, discrepancies in unemployment rates still exist. That is, if Asian unemployment is compared to black unemployment, for example, one can see a staunched difference in unemployment rates over the course of the year.


Upon closer review, Asian unemployment was the lowest throughout the year and ended in December with a 2.6 unemployment rate, which was one and half times lower than white unemployment. Asian umemployment was also more than two times lower than Hispanic unemployment, and three times lower than black unemployment.

White unemployment followed with a close second throughout the year and finished with a 4.3 unemployment rate. And finally, Hispanic unemployment was consistently third while black unemployment was the highest throughout 2016.

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However, it should be noted these are national monthly rates and do not describe and make distinctions between unemployment rates between federal and state levels, between federal and city levels, and between state and city levels, although this website will make such distinctions in future articles.

And of course, this doesn’t take into account the national montly unemployment rate, which was 4.7 in December, for example.

If one wanted to make a distinction between the national monthly unemployment rate and the unemployment rates provided in the graph, one would see that Asian and white unemployment rates were below the December national monthly unemployment rate of 4.7; whereas, black and Hispanic unemployment rates were above the December national montly unemployment rate of 4.7


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Copyright ©2016 – The Systems Scientist

How Satellite Data Changed Chimpanzee Conservation Efforts

Approximately 345,000 or fewer chimpanzees remain in the wild, according to the International Union for the Conservation of Nature, a substantial decline from the more than two million that existed a hundred years ago. Humans’ closest genetic cousins, chimpanzees are an endangered species, and scientists and conservationists are turning to the NASA-U.S. Geological Survey Landsat satellites to help bolster their efforts to preserve their forest homes.

“Chimpanzees are in crisis,” said Lilian Pintea, a remote sensing specialist and vice president of conservation science for the Jane Goodall Institute, Vienna, Virginia, citing hunting and illegal bushmeat consumption, disease, illegal capture for the pet trade and habitat loss as the culprits.

Among these, habitat loss is visible from space.

side-by-side image showing deforestation
Landsat images compare the forest cover change between 1972 and 1999 in the region around Gombe National Park.
Credits: NASA/Goddard Scientific Visualization Studio/Cindy Starr

In 2000, Pintea saw his first side-by-side comparison of two Landsat satellite images, one taken in 1972 and the other in 1999, of the region around Gombe National Park, Tanzania. The 1972 image showed forests that stretched across the region. The 1999 image showed vast swaths of deforestation outside of the park, with its boundary written into the landscape. On one side of the park boundary were lush trees covering the steep slopes that rise from the east of Lake Tanganyika. On the other—bare hills.

topography and satellite data showing deforestation
This visualization shows the difference in the forest cover between the region within the Gombe National Park, to the left of the red boundary line, and the regions outside of the park to the right.
Credits: NASA/Goddard Scientific Visualization Studio/Cindy Starr

A joint mission of NASA and the U.S. Geological Survey or USGS, the Landsat series of satellites has provided a continuous record of Earth’s land use for 44 years. Images are available cost-free to the public.

“NASA satellite data helps us understand what it means to be a chimp by overlaying distribution of the habitat with the chimpanzee behavior and ranging data,” Pintea said. The combination allows him and other scientists to see where chimps are most at risk and design conservation strategies.

Chimpanzees in the region used to live in an uninterrupted belt of forest and woodlands from Lake Tanganyika westward through Uganda and the Congo Basin to western Africa. In the early 1970s, 10 or so years after Jane Goodall first arrived in the region and began observing chimpanzees, that forest began to be cut down. Increased pressures on the land from a population explosion as well as poverty have led to clearing forest for agriculture and local logging as well as charcoal production.

Data from Landsat satellites, a joint mission of NASA and the U.S. Geological Survey, have been critical to helping the Jane Goodall Institute in their work to protect chimpanzees and their habitat. In this video, Goodall and JGI scientist Lilian Pintea discuss the transformational role of seeing changing habitats from above.


“Today the belt per se has gone because it’s being divided into increasingly small fragments,” said Jane Goodall, who at 82 is still active in conservation efforts helmed by her namesake institute. These efforts near Gombe involve engaging the local communities in the conservation planning and monitoring process for the placement and protection of village forest reserves that support both people and chimpanzees. Central to that process has been getting everyone from the villagers to local government officials and other conservation groups to look at the same big picture.

“It was really exciting to see the impact of these images on the villagers,” Goodall said, adding that villagers could identify landmarks and sacred places in the satellite imagery. “It was like a piece of reality dropped magically from the sky.”

Unlike maps, which don’t show the chimpanzees’ habitat side-by-side with human activities, Landsat imagery shows both scientists and the villagers the direct result of various land uses – farming and logging for example – and how they shaped the surrounding terrain and forests.

“When deforestation happens, important ecological functions and services are lost which impacts both chimps and people,” Pintea said. The chimpanzees lose feeding and nesting grounds, and it is very difficult for the territorial animals to shift their home range to another location. People lose local forest resources like honey or specific valuable tree species, as well as suffer alterations of the local water cycle that make erosion and flash flooding new problems.

The satellite images were a game changer for improving local conservation efforts.

“We are informing these planning and management efforts with science and data, including satellite imagery from NASA. But at the end of the day, it’s the villagers’ land use plan and we are there just to facilitate the process. We don’t have a final say in how the communities decide to place a reserve, for example,” Pintea said. The Jane Goodall Institute provides additional support for the use of Android smartphones and tablets for village forest monitors to go into the field to confirm conditions on the ground and evaluate how the plans are working.

Working in partnership with the villagers, the monitoring data they collect combined with a variety of remote sensing imagery, including the Landsat satellite data, has helped Pintea and colleagues in the scientific community better track and understand the relationship between chimpanzee habitat needs and the status of forest habitats. Among the questions they ask are, as the land changes, how do forests, watersheds, and chimpanzees respond, and where are the most opportunities to restore and protect critical watersheds and chimpanzee habitats? They then take that information back to the villages surrounding Gombe so the villagers can make science-based conservation and land use decisions, such as where to restrict logging activities.

“We cannot do this project if the Landsat program doesn’t deliver this open data to the scientific community,” Pintea said. NASA and the USGS’s open data policy and research funding support has allowed him and other scientists to build a satellite-based decision support system for monitoring habitat health not only in Gombe but for the entire chimpanzee range in Africa, he said. “We are benefiting from these long-term investments now.”

Related Links:
Landsat Images Advance Watershed Restoration in Western Tanzania

Ellen Gray
NASA’s Earth Science News Team

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Editor: Karl Hille

From rural Kenya to a PhD in astronomy: how partnerships made it possible

I grew up in a Kenyan village with dark skies and vivid stars. We admired the sky and listened to stories about it told by the elders. There were few expectations that the children in our village would ever understand the sky’s secrets as this was unimaginable at the time.

I excelled at maths and science, eventually becoming a teacher in the subjects. Then came a Masters degree in Physics, followed by a second Masters through South Africa’s Square Kilometre Array (SKA) project. There the boy who had gazed up at mysterious skies turned into a man who wanted to become an astrophysicist.

But Africa has a challenge: astronomy as a profession is a little known field of science in all but one country, South Africa. Even high school science teachers are often not aware that astronomy is a branch of physics, is therefore a science, and could be presented to pupils as a viable career option. The construction of the mid frequency part of the SKA in South Africa, in partnership with eight other African countries, means the continent needs to encourage, produce and nurture young astrophysicists.

Very few African universities offer postgraduate degrees in astronomy. Most that do are based in South Africa; the others include the University of Mauritius and Kenya’s University of Nairobi.

This gap in knowledge, information and study is now being bridged by joint UK-South Africa project that trains students from Africa in the field of astronomy with a focus on radio astronomy. I am a student of the Development in Africa through Radio Astronomy project currently studying towards my PhD at the University of Leeds. The funding stems from the UK’s Newton Fund and is matched by funding from South Africa’s department of science and technology.

It’s a good example of how training and partnerships can help to build the scientists Africa needs to establish itself as a key player in astronomy, radio astronomy and astrophysics.

How the project works

DARA conducted training programs in Kenya and Zambia during 2015 building on a concurrent program in Ghana funded by the UK’s Royal Fund. There it equipped 40 students with the fundamentals of radio astronomy. It was a challenging, competitive and captivating two months consisting of four different training units. We were trained in the technical aspects of radio astronomy as well as learning about data collection and reduction. We collected and analyzed data from a nearby astrophysical object – the sun, for example.

Of those 40 trainees, six – myself among them – were sponsored for further postgraduate studies in the UK; ten others were funded to study further in South Africa. The selection was made with partner institutions in each participating country.

Leeds is one of four participating UK universities. The others are the University of Manchester, University of Hertfordshire and Oxford University. These are all centres of excellence. We will also, during our studies, spend some time in South Africa supervised by a South African collaborator. This is important preparation for future collaborations, which are crucial in science.

As trainees, we’ve enjoyed interactions with senior research scientists and presentations from renowned academics. We present our work to each other and develop the skills we’ll need to be working scientists. We’re also looking forward to welcoming fellow students from countries such as Namibia, Botswana, Mauritius, Madagascar and Mozambique as the project expands further across the continent.

This training formula has the potential to inspire and empower many more individuals across Africa. And the benefits won’t be felt just in the field of astronomy. The skills my colleagues and I are developing are widely applicable. They can be used in a number of areas: research, computing, telecommunications, land management and even business.

Preparing Africa for the SKA

Equally important is DARA’s role in preparing Africa ahead of the completion of the SKA, the world’s largest radio telescope. It is partially hosted in South Africa. SKA-Africa is also funding the conversion of the redundant satellite earth stations in Africa into radio telescopes that will form a network of telescopes called the African VLBI (Very Long Baseline Interferometry) Network – a technique that will simulate a telescope the size of Africa.

These are major investments in science. It’s important that preparations be made for their proper maintenance and successful operation – and that requires trained radio astronomers to do the work. The amount of data to be collected from these facilities is also large; if this collection is to be thorough and successful it will require properly trained big data managers and researchers. This is why DARA is preparing people like me for the future of African astronomy.

These telescopes can be seen as a sign of trust that the rest of the world has placed in Africa. They are capital intensive science facilities. With proper training programs and the development of more African astronomers, the continent can repay this trust many times over.

The Conversation

Willice O. Obonyo-PhD student (Radio Astronomy), University of Leeds


Photo Credit: The Conversation


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This article was originally published on The Conversation. Read the original article.