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Big Data

When cities forget about pedestrians, big data and technology can serve as a friendly reminder

Bianca Bianchi Alves's picture
Photo: Lazyllama/Shutterstock
Paraisópolis, a nationally famous slum area in São Paulo, Brazil, is one of those bustling communities where everything happens. Despite being located in the middle of the city, it managed, unlike other poor slum areas, not to be reallocated to make room for more expensive housing or public infrastructure. The area boasts vibrant community life, with more than 40 active NGOs covering issues that range from waste management and health to ballet and cooking. Recently, the area also benefited from several community upgrading programs. In particular, investments in local roads have facilitated truck access to the community, bringing in large and small retailers, and generating lively economic activity along with job opportunities for local residents.

As we continue our efforts to increase awareness around on-foot mobility (see previous blog), today, I would like to highlight a project we developed for Paraisópolis.

While most of the community has access to basic services and there are opportunities for professional enhancement and cultural activities, mobility and access to jobs remains a challenge. The current inequitable distribution of public space in the community prioritizes private cars versus transit and non-motorized transport. This contributes to severe congestion and reduced transit travel speed; buses had to be reallocated to neighboring streets because they were always stuck in traffic. Pedestrians are always at danger of being hit by a vehicle or falling on the barely-existent sidewalks, and emergency vehicles have no chance of getting into the community if needed. For example, in the last year there were three fire events—a common hazard in such communities—affecting hundreds of homes, yet the emergency trucks could not come in to respond on time because of cars blocking the passage.

From data blur to slow-mo clarity: big data in trade and competitiveness

Prasanna Lal Das's picture

Tolstoy's War and Peace was the big data of its time. A memorable moment from the epic novel occurs when Prince Andrei awakens following a severe injury on the battlefield. He fears the worst but, "above him there was nothing but the sky, the lofty heavens, not clear, yet immeasurably lofty, with gray clouds slowly drifting across them. 'How quiet, solemn, and serene, not at all as it was when I was running.'" Time appears to slow down and the Prince sees life more lucidly than ever before as he discovers the potential for happiness within him.

In many ways the scene captures what we demand of big data—not the bustle of zillions of data points as confusing as the fog of war, but sharp, clear insights that bring the right information into relief and help us connect strands previously unseen. The question of whether this idea is achievable is the starting point of a paper about big data on trade and competitiveness just published by the World Bank Group. In it, we asked—can big data help policy makers see the world in ways they haven't before? Are decisions that are informed by the vast amounts of data that envelop us better than decisions based on traditional tools? We didn't want a story trumpeting the miracles of big data; we wanted instead to see the reality of big data in action, in its messiness and its splendor.

Can new developments in machine learning and satellite imagery be used to estimate jobs?

Alvaro Gonzalez's picture
 Orbital Insight satellite imagery/Airbus Defense and Space and DigitalGlobe)
"Before" and "after" satellite images analyzed for agricultural land, using algorithms. (Photo: Orbital Insight satellite imagery/Airbus Defense and Space and DigitalGlobe)


Methods that use satellite data and machine learning present a good peek into how Big Data and new analytical methods will change how we measure poverty. I am not a poverty specialist, so I am wondering if these data and techniques can help in how we estimate job growth. 

When it comes to measuring jobs, the need to refine traditional tools with new methods

Alvaro Gonzalez's picture
 Chhor Sokunthea / World Bank
One of the challenges of measuring jobs is addressing impacts that go beyond direct jobs. A tool called tracer surveys is helping do this.
Photo: Chhor Sokunthea / World Bank


In addition to correctly measuring the jobs directly generated from interventions and investments, development agencies also need to estimate the resulting indirect impacts and general equilibrium effects. These are hard to measure. My recent blog highlighted the progress that donors, international financial institutions, and other multilateral agencies are making in developing standardized tools to measure these impacts. In addition to standardization, the focus is now on strengthening existing measurement tools and addressing the challenges that are left.  

Media (R)evolutions: What’s the future of the sharing economy?

Darejani Markozashvili's picture
New developments and curiosities from a changing global media landscape: People, Spaces, Deliberation brings trends and events to your attention that illustrate that tomorrow's media environment will look very different from today's, and will have little resemblance to yesterday's.

Globally, more and more people are embracing the sharing or platform economy. Some estimate that the sector’s revenues will increase to $335 billion globally by 2025. According to the Future Jobs Survey, conducted by the World Economic Forum, among top technological drivers of industrial change by 2020, the sharing economy, crowdsourcing takes the fifth place, with mobile internet, cloud technology taking the lead.
 


So what will the impact of these drivers be on the industries? Will there be new industries born as a result of these transformations? If so, will we be able and ready to respond to those changes? Will we have necessary skill sets to compete in the work force? Future holds both opportunities and challenges for industries, corporations, governments, and others concerned with the technological advancements.
 
What exactly is the sharing economy? Are you using some of its platforms? Do you benefit from their services? 

Book Review: Social Physics: How social networks can make us smarter

Duncan Green's picture

https://flic.kr/p/rSxSDwMy Christmas reading included a book called Social Physics – yep, a party animal (my others were Lord of the Flies and Knausgard Vol 3, both wonderful). Here’s the review:

Airport bookstores are bewildering places – shelf after shelf of management gurus offering distilled lessons on leadership, change and everything else. How to distinguish snake oil from substance? My Christmas reading, based on a recommendation from someone attending a book launch in the US last month (thanks whoever you were – all a bit of a blur now) was Alex Pentland’s ‘Social Physics: How Social Networks can make us Smarter’. I must confess, as a lapsed physicist, the title swung it for me, but I learned a lot from this book. At least I think I did – let’s see if I am still using the ideas in a few months’ time.

Social Physics is not a new idea. Auguste Comte, the founder of modern sociology, coined the phrase back in the 19th century. Comte and his crew aspired to explain social reality by developing a set of universal laws—the sociological equivalent of physicists’ quest to create a theory of everything. As with economics, that kind of physics envy has proved largely delusional. Now though, Pentland argues that the arrival of Big Data means we can aspire to a ‘thermodynamics of society’, where behaviour is governed by discernible mathematical laws. It does not deny free will – Pentland does not claim to be able to predict individual behaviour, but finds a high degree of certainty in mass behaviours, which appear to follow particular patterns (like atoms in a gas).

Traveling with ease, carrying disease? Using mobile phone data to reduce malaria: Guest post by Sveta Milusheva

This is the eighth in our series of job market posts this year
The Global Fund has disbursed nearly $28.4 billion in the last decade to reduce the disease burden from malaria, TB and HIV (Global Fund 2016). However, travelers can reverse the progress from campaigns that have decreased infectious disease prevalence (Cohen 2012 et al, Lu et al 2014), or can rapidly spread emerging diseases such as Ebola and Zika (Tam et al 2016, Bogoch et al 2016). While policymakers have largely targeted environmental drivers of malaria, this research provides evidence that human movement can play an important role in spreading disease in areas where incidence has been reduced.  Given that migration has numerous economic and social benefits, policymakers face important trade-offs in designing policies to reduce travel-linked malaria cases.  This paper provides a useful framework for identifying high-risk populations in order to reduce malaria incidence with minimal interference to movement patterns.

Open data, closed algorithms, and the Black Box of Education

Michael Trucano's picture
hey, what's going on in there?
hey, what's going on in there?
Education is a ‘black box’ -- or so a prevailing view among many education policymakers and researchers goes.

For all of the recent explosion in data related to learning -- as a result of standardized tests, etc. -- remarkably little is known at scale about what exactly happens in classrooms around the world, and outside of them, when it comes to learning, and what the impact of this has.

This isn't to say that we know nothing, of course:

The World Bank (to cite an example from within my own institution) has been using standardized classroom observation techniques to help document what is happening in many classrooms around the world (see, for example, reports based on modified Stallings Method classroom observations across Latin America which seek to identify how much time is actually spent on instruction during school hours; in many cases, the resulting data generated are rather appalling).

Common sense holds various tenets dear when it comes to education, and to learning; many educators profess to know intuitively what works, based on their individual (and hard won) experience, even in the absence of rigorously gathered, statistically significant 'hard' data; the impact of various socioeconomic factors is increasingly acknowledged (even if many policymakers remain impervious to them); and cognitive neuroscience is providing many interesting insights.

But in many important ways, education policymaking and processes of teaching and learning are constrained by the fact that we don't have sufficient, useful, actionable data about what is actually happening with learners at a large scale across an education system -- and what impact this might have. Without data, as Andreas Schleicher likes to say, you are just another person with an opinion. (Of course, with data you might be a person with an ill-considered or poorly argued opinion, but that’s another issue.)
 
side observation: Echoing many teachers (but, in contrast to teaching professionals, usually with little or no formal teaching experience themselves), I find that many parents and politicians also profess to know intuitively ‘what works’ when it comes to teaching. When it comes to education, most everyone is an ‘expert’, because, well, after all, everyone was at one time a student. While not seeking to denigrate the ‘wisdom of the crowd’, or downplay the value of common sense, I do find it interesting that many leaders profess to have ready prescriptions at hand for what ‘ails education’ in ways that differ markedly from the ways in which they approach making decisions when it comes to healthcare policy, for example, or finance – even though they themselves have also been patients and make spending decisions in their daily lives.

One of the great attractions of educational technologies for many people is their potential to help open up and peer inside this so-called black box. For example:
  • When teachers talk in front of a class, there are only imperfect records of what transpired (teacher and student notes, memories of participants, what's left on the blackboard -- until that's erased). When lectures are recorded, on the other hand, there is a data trail that can be examined and potentially mined for related insights.
  • When students are asked to read in their paper textbook, there is no record of whether the book was actually opened, let along whether or not to the correct page, how long a page was viewed, etc. Not so when using e-readers or reading on the web.
  • Facts, figures and questions scribbled on the blackboard disappear once the class bell rings; when this information is entered into, say,  Blackboard TM (or any other digital learning management system, for that matter), they can potentially live on forever. 
And because these data are, at their essence, just a collection of ones and zeroes, it is easy to share them quickly and widely using the various connected technology devices we increasingly have at our disposal.
 
A few years ago I worked on a large project where a government was planning to introduce lots of new technologies into classrooms across its education system. Policymakers were not primarily seeking to do this in order to ‘transform teaching and learning’ (although of course the project was marketed this way), but rather so that they could better understand what was actually happening in classrooms. If students were scoring poorly on their national end-of-year assessments, policymakers were wondering: Is this because the quality of instruction was insufficient? Because the learning materials used were inadequate? Or might it be because the teachers never got to that part of the syllabus, and so students were being assessed on things they hadn’t been taught? If technology use was mandated, at least they might get some sense about what material was being covered in schools – and what wasn’t. Or so the thinking went ....

Yes, such digital trails are admittedly incomplete, and can obscure as much as they illuminate, especially if the limitations of such data are poorly understood and data are investigated and analyzed incompletely, poorly, or with bias (or malicious intent). They also carry with them all sorts of very important and thorny considerations related to privacy, security, intellectual property and many other issues.

That said, used well, the addition of additional data points holds out the tantalizing promise of potentially new and/or deeper insights than has been currently possible within 'analogue' classrooms.

But there is another 'black box of education' worth considering.

In many countries, there have been serious and expansive efforts underway to compel governments make available more ‘open data’ about what is happening in their societies, and to utilize more ‘open educational resources’ for learning – including in schools. Many international donor and aid agencies support related efforts in key ways. The World Bank is a big promoter of many of these so-called ‘open data’ initiatives, for example. UNESCO has long been a big proponent of ‘open education resources’ (OERs). To some degree, pretty much all international donor agencies are involved in such activities in some way.

There is no doubt that increased ‘openness’ of various sorts can help make many processes and decisions in the education sector more transparent, as well as have other benefits (by allowing the re-use and ‘re-mixing’ of OERs, teachers and students can themselves help create new teaching and learning materials; civil society groups and private firms can utilize open data to help build new products and services; etc.).

That said:
  • What happens when governments promote the use of open education data and open education resources but, at the same time, refuse to make openly available the algorithms (formulas) that are utilized to draw insights from, and make key decisions based on, these open data and resources?
     
  • Are we in danger of opening up one black box, only to place another, more inscrutable back box inside of it?

The data revolution continues with the latest World Bank Innovation challenge

Marianne Fay's picture

On September 22, 2016, we launched the World Bank Big Data Innovation Challenge – a global call for big data solutions for climate resilience and sustainable development.

As the world grows more connected--through mobile phones, social media, internet, satellites, ground sensors and machines—governments and economies need better ways to harness these data flows for insights toward targeted policies and actions that boost climate resilience, especially amongst the most vulnerable. To make this data more useful for development, we need more data innovations and innovative public-private arrangements for data collaboration.

The World Bank Big Data Innovation Challenge invites innovators across the world to reimagine climate resilience through big data solutions that address the nexus areas of food security and nutrition, and forests and watersheds – high priority areas of the World Bank’s Climate and Forest Action Plans and the UN Sustainable Development Goals.

Big data innovation – moving from ideas to implementation

Trevor Monroe's picture

If you want to do something fast, do something that has already been done. If you want to hardwire a data innovation into World Bank Operations, be prepared to involve others in a process of learning by doing.  – Holly Krambeck, Senior Transport Specialist, WBG



As the world grows more connected, data flows from a multitude of sources. Mobile networks, social media, satellites, grounds sensors, and machine-to-machine transactions are being used along with traditional data--like household surveys--to improve insights and actions toward global goals.
 
At the World Bank, a cadre of pioneering economists and sector specialists are putting big data in action. Big data sources are being harnessed to lead innovations like:

  • satellites to track rural electrification, to monitor crop yields and to predict poverty;
  • taxi GPS data to monitor traffic flows and congestion
  • mobile phone data for insights into human mobility and behavior, as well as infrastructure and socio-economic conditions 

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