Emerging Technology From the arXiv: The increasing availability of big data from mobile phones and location-based apps has triggered a revolution in the understanding of human mobility patterns. This data shows the ebb and flow of the daily commute in and out of cities, the pattern of travel around the world and even how disease can spread through cities via their transport systems.
So there is considerable interest in looking more closely at human mobility patterns to see just how well it can be predicted and how these predictions might be used in everything from disease control and city planning to traffic forecasting and location-based advertising.
Today we get an insight into the kind of detailed that is possible thanks to the work of Zimo Yang at Microsoft research in Beijing and a few pals. These guys start with the hypothesis that people who live in a city have a pattern of mobility that is significantly different from those who are merely visiting. By dividing travelers into locals and non-locals, their ability to predict where people are likely to visit dramatically improves.
Zimo and co begin with data from a Chinese location-based social network called Jiepang.com. This is similar to Foursquare in the US. It allows users to record the places they visit and to connect with friends at these locations and to find others with similar interests.
The data points are known as check-ins and the team downloaded more than 1.3 million of them from five big cities in China: Beijing, Shanghai, Nanjing, Chengdu and Hong Kong. They then used 90 per cent of the data to train their algorithms and the remaining 10 per cent to test it. The Jiapang data includes the users’ hometowns so it’s easy to see whether an individual is checking in in their own city or somewhere else.
The question that Zimo and co want to answer is the following: given a particular user and their current location, where are they most likely to visit in the near future? In practice, that means analysing the user’s data, such as their hometown and the locations recently visited, and coming up with a list of other locations that they are likely to visit based on the type of people who visited these locations in the past.
Zimo and co used their training dataset to learn the mobility pattern of locals and non-locals and the popularity of the locations they visited. The team then applied this to the test dataset to see whether their algorithm was able to predict where locals and non-locals were likely to visit.
They found that their best results came from analysing the pattern of behaviour of a particular individual and estimating the extent to which this person behaves like a local. That produced a weighting called the indigenization coefficient that the researchers could then use to determine the mobility patterns this person was likely to follow in future.
In fact, Zimo and co say they can spot non-locals in this way without even knowing their home location. “Because non-natives tend to visit popular locations, like the Imperial Palace in Beijing and the Bund in Shanghai, while natives usually check in around their homes and workplaces,” they add.
The team say this approach considerably outperforms the mixed algorithms that use only individual visiting history and location popularity. “To our surprise, a hybrid algorithm weighted by the indigenization coefficients outperforms the mixed algorithm accounting for additional demographical information.”
It’s easy to imagine how such an algorithm might be useful for businesses who want to target certain types of travelers or local people. But there is a more interesting application too.
Zimo and co say that it is possible to monitor the way an individual’s mobility patterns change over time. So if a person moves to a new city, it should be possible to see how long it takes them to settle in.
One way of measuring this is in their mobility patterns: whether they are more like those of a local or a non-local. “We may be able to estimate whether a non-native person will behave like a native person after a time period and if so, how long in average a person takes to become a native-like one,” say Zimo and co.
That could have a fascinating impact on the way anthropologists study migration and the way immigrants become part of a local community. This is computational anthropology a science that is clearly in its early stages but one that has huge potential for the future.”
Ref: arxiv.org/abs/1405.7769 : Indigenization of Urban Mobility
Humanitarians in the sky
Patrick Meier in the Guardian: “Unmanned aerial vehicles (UAVs) capture images faster, cheaper, and at a far higher resolution than satellite imagery. And as John DeRiggi speculates in “Drones for Development?” these attributes will likely lead to a host of applications in development work. In the humanitarian field that future is already upon us — so we need to take a rights-based approach to advance the discussion, improve coordination of UAV flights, and to promote regulation that will ensure safety while supporting innovation.
It was the unprecedentedly widespread use of civilian UAVs following typhoon Haiyan in the Philippines that opened my eyes to UAV use in post-disaster settings. I was in Manila to support the United Nations’ digital humanitarian efforts and came across new UAV projects every other day.
One team was flying rotary-wing UAVs to search for survivors among vast fields of debris that were otherwise inaccessible. Another flew fixed-wing UAVs around Tacloban to assess damage and produce high-quality digital maps. Months later, UAVs are still being used to support recovery and preparedness efforts. One group is working with local mayors to identify which communities are being overlooked in the reconstruction.
Humanitarian UAVs are hardly new. As far back as 2007, the World Food Program teamed up with the University of Torino to build humanitarian UAVs. But today UAVs are much cheaper, safer, and easier to fly. This means more people own personal UAVs. The distinguishing feature between these small UAVs and traditional remote control airplanes or helicopters is that they are intelligent. Most can be programmed to fly and land autonomously at designated locations. Newer UAVs also have on-board, flight-stabilization features that automatically adapt to changing winds, automated collision avoidance systems, and standard fail-safe mechanisms.
While I was surprised by the surge in UAV projects in the Philippines, I was troubled that none of these teams were aware of each other and that most were apparently not sharing their imagery with local communities. What happens when even more UAV teams show up following future disasters? Will they be accompanied by droves of drone journalists and “disaster tourists” equipped with personal UAVs? Will we see thousands of aerial disaster pictures and videos uploaded to social media rather than in the hands of local communities? What are the privacy implications? And what about empowering local communities to deploy their own UAVs?
There were many questions but few answers. So I launched the humanitarian UAV network (UAViators) to bridge the worlds of humanitarian professionals and UAV experts to address these questions. Our first priority was to draft a code of conduct for the use of UAVs in humanitarian settings to hold ourselves accountable while educating new UAV pilots before serious mistakes are made…”
Making cities smarter through citizen engagement
Vaidehi Shah at Eco-Business: “Rapidly progressing information communications technology (ICT) is giving rise to an almost infinite range of innovations that can be implemented in cities to make them more efficient and better connected. However, in order for technology to yield sustainable solutions, planners must prioritise citizen engagement and strong leadership.
This was the consensus on Tuesday at the World Cities Summit 2014, where representatives from city and national governments, technology firms and private sector organisations gathered in Singapore to discuss strategies and challenges to achieving sustainable cities in the future.
Laura Ipsen, Microsoft corporate vice president for worldwide public sector, identified globalisation, social media, big data, and mobility as the four major technological trends prevailing in cities today, as she spoke at the plenary session with a theme on “The next urban decade: critical challenges and opportunities”.
Despite these increasing trends, she cautioned, “technology does not build infrastructure, but it does help better engage citizens and businesses through public-private partnerships”.
For example, “LoveCleanStreets”, an online tool developed by Microsoft and partners, enables London residents to report infrastructure problems such as damaged roads or signs, shared Ipsen.
“By engaging citizens through this application, cities can fix problems early, before they get worse,” she said.
In Singapore, the ‘MyWaters’ app of PUB, Singapore’s national water agency, is also a key tool for the government to keep citizens up-to-date of water quality and safety issues in the country, she added.
Even if governments did not actively develop solutions themselves, simply making the immense amounts of data collected by the city open to businesses and citizens could make a big difference to urban liveability, Mark Chandler, director of the San Francisco Mayor’s Office of International Trade and Commerce, pointed out.
Opening up all of the data collected by San Francisco, for instance, yielded 60 free mobile applications that allow residents to access urban solutions related to public transport, parking, and electricity, among others, he explained. This easy and convenient access to infrastructure and amenities, which are a daily necessity, is integral to “a quality of life that keeps the talented workforce in the city,” Chandler said….”
Twitter releasing trove of user data to scientists for research
Joe Silver at ArsTechnica: “Twitter has a 200-million-strong and ever-growing user base that broadcasts 500 million updates daily. It has been lauded for its ability to unsettle repressive political regimes, bring much-needed accountability to corporations that mistreat their customers, and combat other societal ills (whether such characterizations are, in fact, accurate). Now, the company has taken aim at disrupting another important sphere of human society: the scientific research community.
Back in February, the site announced its plan—in collaboration with Gnip—to provide a handful of research institutions with free access to its data sets from 2006 to the present. It’s a pilot program called “Twitter Data Grants,” with the hashtag #DataGrants. At the time, Twitter’s engineering blog explained the plan to enlist grant applications to access its treasure trove of user data:
Twitter has an expansive set of data from which we can glean insights and learn about a variety of topics, from health-related information such as when and where the flu may hit to global events like ringing in the new year. To date, it has been challenging for researchers outside the company who are tackling big questions to collaborate with us to access our public, historical data. Our Data Grants program aims to change that by connecting research institutions and academics with the data they need.
In April, Twitter announced that, after reviewing the more than 1,300 proposals submitted from more than 60 different countries, it had selected six institutions to provide with data access. Projects approved included a study of foodborne gastrointestinal illnesses, a study measuring happiness levels in cities based on images shared on Twitter, and a study using geosocial intelligence to model urban flooding in Jakarta, Indonesia. There’s even a project exploring the relationship between tweets and sports team performance.
Twitter did not directly respond to our questions on Tuesday afternoon regarding the specific amount and types of data the company is providing to the six institutions. But in its privacy policy, Twitter explains that most user information is intended to be broadcast widely. As a result, the company likely believes that sharing such information with scientific researchers is well within its rights, as its services “are primarily designed to help you share information with the world,” Twitter says. “Most of the information you provide us is information you are asking us to make public.”
While mining such data sets will undoubtedly aid scientists in conducting experiments for which similar data was previously either unavailable or quite limited, these applications raise some legal and ethical questions. For example, Scientific American has asked whether Twitter will be able to retain any legal rights to scientific findings and whether mining tweets (many of which are not publicly accessible) for scientific research when Twitter users have not agreed to such uses is ethically sound.
In response, computational epidemiologists Caitlin Rivers and Bryan Lewis have proposed guidelines for ethical research practices when using social media data, such as avoiding personally identifiable information and making all the results publicly available….”
Closing the Feedback Loop: Can Technology Bridge the Accountability Gap
(WorldBank) Book edited by Björn-Sören Gigler and Savita Bailur: “This book is a collection of articles, written by both academics and practitioners as an evidence base for citizen engagement through information and communication technologies (ICTs). In it, the authors ask: how do ICTs empower through participation, transparency and accountability? Specifically, the authors examine two principal questions: Are technologies an accelerator to closing the “accountability gap” – the space between the supply (governments, service providers) and demand (citizens, communities, civil society organizations or CSOs) that requires bridging for open and collaborative governance? And under what conditions does this occur? The introductory chapters lay the theoretical groundwork for understanding the potential of technologies to achieving intended goals. Chapter 1 takes us through the theoretical linkages between empowerment, participation, transparency and accountability. In Chapter 2, the authors devise an informational capability framework, relating human abilities and well-being to the use of ICTs. The chapters to follow highlight practical examples that operationalize ICT-led initiatives. Chapter 3 reviews a sample of projects targeting the goals of transparency and accountability in governance to make preliminary conclusions around what evidence exists to date, and where to go from here. In chapter 4, the author reviews the process of interactive community mapping (ICM) with examples that support general local development and others that mitigate natural disasters. Chapter 5 examines crowdsourcing in fragile states to track aid flows, report on incitement or organize grassroots movements. In chapter 6, the author reviews Check My School (CMS), a community monitoring project in the Philippines designed to track the provision of services in public schools. Chapter 7 introduces four key ICT-led, citizen-governance initiatives in primary health care in Karnataka, India. Chapter 8 analyzes the World Bank Institute’s use of ICTs in expanding citizen project input to understand the extent to which technologies can either engender a new “feedback loop” or ameliorate a “broken loop”. The authors’ analysis of the evidence signals ICTs as an accelerator to closing the “accountability gap”. In Chapter 9, the authors conclude with the Loch Ness model to illustrate how technologies contribute to shrinking the gap, why the gap remains open in many cases, and what can be done to help close it. This collection is a critical addition to existing literature on ICTs and citizen engagement for two main reasons: first, it is expansive, covering initiatives that leverage a wide range of technology tools, from mobile phone reporting to crowdsourcing to interactive mapping; second, it is the first of its kind to offer concrete recommendations on how to close feedback loops.”
Politics or technology – which will save the world?
David Runciman in the Guardian: (Politics by David Runciman is due from Profile ..It is the first in a series of “Ideas in Profile”) “The most significant revolution of the 21st century so far is not political. It is the information technology revolution. Its transformative effects are everywhere. In many places, rapid technological change stands in stark contrast to the lack of political change. Take the United States. Its political system has hardly changed at all in the past 25 years. Even the moments of apparent transformation – such as the election of Obama in 2008 – have only reinforced how entrenched the established order is: once the excitement died away, Obama was left facing the same constrained political choices. American politics is stuck in a rut. But the lives of American citizens have been revolutionised over the same period. The birth of the web and the development of cheap and efficient devices through which to access it have completely altered the way people connect with each other. Networks of people with shared interests, tastes, concerns, fetishes, prejudices and fears have sprung up in limitless varieties. The information technology revolution has changed the way human beings befriend each other, how they meet, date, communicate, medicate, investigate, negotiate and decide who they want to be and what they want to do. Many aspects of our online world would be unrecognisable to someone who was transplanted here from any point in the 20th century. But the infighting and gridlock in Washington would be all too familiar.
This isn’t just an American story. China hasn’t changed much politically since 4 June 1989, when the massacre in Tiananmen Square snuffed out a would-be revolution and secured the current regime’s hold on power. But China itself has been totally altered since then. Economic growth is a large part of the difference. But so is the revolution in technology. A country of more than a billion people, nearly half of whom still live in the countryside, has been transformed by the mobile phone. There are currently over a billion phones in use in China. Ten years ago, fewer than one in 10 Chinese had access to one; today there is nearly one per person. Individuals whose horizons were until very recently constrained by physical geography – to live and die within a radius of a few miles from your birthplace was not unusual for Chinese peasants even into this century – now have access to the wider world. For the present, though maybe not for much longer, the spread of new technology has helped to stifle the call for greater political change. Who needs a political revolution when you’ve got a technological one?
Advertisement
Technology has the power to make politics seem obsolete. The speed of change leaves government looking slow, cumbersome, unwieldy and often irrelevant. It can also make political thinking look tame by comparison with the big ideas coming out of the tech industry. This doesn’t just apply to far‑out ideas about what will soon be technologically possible: intelligent robots, computer implants in the human brain, virtual reality that is indistinguishable from “real” reality (all things that Ray Kurzweil, co-founder of the Google-sponsored Singularity University, thinks are coming by 2030). In this post-ideological age some of the most exotic political visions are the ones that emerge from discussions about tech. You’ll find more radical libertarians and outright communists among computer scientists than among political scientists. Advances in computing have thrown up fresh ways to think about what it means to own something, what it means to share something and what it means to have a private life at all. These are among the basic questions of modern politics. However, the new answers rarely get expressed in political terms (with the exception of occasional debates about civil rights for robots). More often they are expressions of frustration with politics and sometimes of outright contempt for it. Technology isn’t seen as a way of doing politics better. It’s seen as a way of bypassing politics altogether.
In some circumstances, technology can and should bypass politics. The advent of widespread mobile phone ownership has allowed some of the world’s poorest citizens to wriggle free from the trap of failed government. In countries that lack basic infrastructure – an accessible transport network, a reliable legal system, a usable banking sector – phones enable people to create their own networks of ownership and exchange. In Africa, a grassroots, phone-based banking system has sprung up that for the first time permits money transfers without the physical exchange of cash. This makes it possible for the inhabitants of desperately poor and isolated rural areas to do business outside of their local communities. Technology caused this to happen; government didn’t. For many Africans, phones are an escape route from the constrained existence that bad politics has for so long mired them in.
But it would be a mistake to overstate what phones can do. They won’t rescue anyone from civil war. Africans can use their phones to tell the wider world of the horrors that are still taking place in some parts of the continent – in South Sudan, in Eritrea, in the Niger Delta, in the Central African Republic, in Somalia. Unfortunately the world does not often listen, and nor do the soldiers who are doing the killing. Phones have not changed the basic equation of political security: the people with the guns need a compelling reason not to use them. Technology by itself doesn’t give them that reason. Equally, technology by itself won’t provide the basic infrastructure whose lack it has provided a way around. If there are no functioning roads to get you to market, a phone is a godsend when you have something to sell. But in the long run, you still need the roads. In the end, only politics can rescue you from bad politics…”
Democracy and open data: are the two linked?
Molly Shwartz at R-Street: “Are democracies better at practicing open government than less free societies? To find out, I analyzed the 70 countries profiled in the Open Knowledge Foundation’s Open Data Index and compared the rankings against the 2013 Global Democracy Rankings. As a tenet of open government in the digital age, open data practices serve as one indicator of an open government. Overall, there is a strong relationship between democracy and transparency.
Using data collected in October 2013, the top ten countries for openness include the usual bastion-of-democracy suspects: the United Kingdom, the United States, mainland Scandinavia, the Netherlands, Australia, New Zealand and Canada.
There are, however, some noteworthy exceptions. Germany ranks lower than Russia and China. All three rank well above Lithuania. Egypt, Saudi Arabia and Nepal all beat out Belgium. The chart (below) shows the democracy ranking of these same countries from 2008-2013 and highlights the obvious inconsistencies in the correlation between democracy and open data for many countries.
There are many reasons for such inconsistencies. The implementation of open-government efforts – for instance, opening government data sets – often can be imperfect or even misguided. Drilling down to some of the data behind the Open Data Index scores reveals that even countries that score very well, such as the United States, have room for improvement. For example, the judicial branch generally does not publish data and houses most information behind a pay-wall. The status of legislation and amendments introduced by Congress also often are not available in machine-readable form.
As internationally recognized markers of political freedom and technological innovation, open government initiatives are appealing political tools for politicians looking to gain prominence in the global arena, regardless of whether or not they possess a real commitment to democratic principles. In 2012, Russia made a public push to cultivate open government and open data projects that was enthusiastically endorsed by American institutions. In a June 2012 blog post summarizing a Russian “Open Government Ecosystem” workshop at the World Bank, one World Bank consultant professed the opinion that open government innovations “are happening all over Russia, and are starting to have genuine support from the country’s top leaders.”
Given the Russian government’s penchant for corruption, cronyism, violations of press freedom and increasing restrictions on public access to information, the idea that it was ever committed to government accountability and transparency is dubious at best. This was confirmed by Russia’s May 2013 withdrawal of its letter of intent to join the Open Government Partnership. As explained by John Wonderlich, policy director at the Sunlight Foundation:
While Russia’s initial commitment to OGP was likely a surprising boon for internal champions of reform, its withdrawal will also serve as a demonstration of the difficulty of making a political commitment to openness there.
Which just goes to show that, while a democratic government does not guarantee open government practices, a government that regularly violates democratic principles may be an impossible environment for implementing open government.
A cursory analysis of the ever-evolving international open data landscape reveals three major takeaways:
- Good intentions for government transparency in democratic countries are not always effectively realized.
- Politicians will gladly pay lip-service to the idea of open government without backing up words with actions.
- The transparency we’ve established can go away quickly without vigilant oversight and enforcement.”
What happened to the idea of the Great Society?
John Micklethwait and Adrian Wooldridge in the Financial Times: “Most of the interesting experiments in government are taking place far from Washington: in Singapore, which delivers much better public services at a fraction of the cost; in Brazil, with its “conditional” welfare payments, dependent on behaviour; in Scandinavia, where “socialist” Sweden has cut state spending from 67 per cent of GDP in 1993 to 49 per cent, introduced school vouchers and brought entitlements into balance by raising the retirement age. In the US, the dynamic bits of government are in its cities, where pragmatic mayors are experimenting with technology.
What will replace the Great Society? For Republicans, the answer looks easy: just shrink government. But this gut instinct runs up against two big problems. The assumption that government is evil means they never take it seriously (Singapore has a tiny state but pays its best civil servants $2m a year). And, in practice, American conservatives are addicted to Big Government: hence the $1.3tn of exemptions in the US tax code, most of which are in effect a welfare state for the rich.
For Democrats, the problem is even worse. Having become used to promising ever more entitlements to voters, they face a series of unedifying choices: whether to serve society at large (by making schools better) or to protect public sector unions (teachers account for many of their activists); and whether to offer ever less generous universal benefits to the entire population or to target spending on the disadvantaged.
This is where the politics of the future will be fought, on both sides of the Atlantic. It will not be as inspiring as the Great Society. It will be about slimming and modernising government, tying pensions to life expectancy and unleashing technology on the public sector.
But what the US – and Europe – needs is cool-headed pragmatism. Government is neither a monster nor a saviour but an indispensable part of a decent society that, like most organisations, works best when it focuses on doing a few things well.”
The Collective Intelligence Handbook: an open experiment
Michael Bernstein: “Is there really a wisdom of the crowd? How do we get at it and understand it, utilize it, empower it?
You probably have some ideas about this. I certainly do. But I represent just one perspective. What would an economist say? A biologist? A cognitive or social psychologist? An artificial intelligence or human-computer interaction researcher? A communications scholar?
For the last two years, Tom Malone (MIT Sloan) and I (Stanford CS) have worked to bring together all these perspectives into one book. We are nearing completion, and the Collective Intelligence Handbook will be published by the MIT Press later this year. I’m still relatively dumbfounded by the rockstar lineup we have managed to convince to join up.
It’s live.
Today we went live with the authors’ current drafts of the chapters. All the current preprints are here: http://cci.mit.edu/CIchapterlinks.html
And now is when you come in.
But we’re not done. We’d love for you — the crowd — to help us make this book better. We envisioned this as an open process, and we’re excited that all the chapters are now at a point where we’re ready for critique, feedback, and your contributions.
There are two ways you can help:
- Read the current drafts and leave comments inline in the Google Docs to help us make them better.
- Drop suggestions in the separate recommended reading list for each chapter. We (the editors) will be using that material to help us write an introduction to each chapter.
We have one month. The authors’ final chapters are due to us in mid-June. So off we go!”
Here’s what’s in the book:
Chapter 1. Introduction
Thomas W. Malone (MIT) and Michael S. Bernstein (Stanford University)
What is collective intelligence, anyway?
Chapter 2. Human-Computer Interaction and Collective Intelligence
Jeffrey P. Bigham (Carnegie Mellon University), Michael S. Bernstein (Stanford University), and Eytan Adar (University of Michigan)
How computation can help gather groups of people to tackle tough problems together.
Chapter 3. Artificial Intelligence and Collective Intelligence
Daniel S. Weld (University of Washington), Mausam (IIT Delhi), Christopher H. Lin (University of Washington), and Jonathan Bragg (University of Washington)
Mixing machine intelligence with human intelligence could enable a synthesized intelligent actor that brings together the best of both worlds.
Chapter 4. Collective Behavior in Animals: An Ecological Perspective
Deborah M. Gordon (Stanford University)
How do groups of animals work together in distributed ways to solve difficult problems?
Chapter 5. The Wisdom of Crowds vs. the Madness of Mobs
Andrew W. Lo (MIT)
Economics has studied a collectively intelligent forum — the market — for a long time. But are we as smart as we think we are?
Chapter 6. Collective Intelligence in Teams and Organizations
Anita Williams Woolley (Carnegie Mellon University), Ishani Aggarwal (Georgia Tech), Thomas W. Malone (MIT)
How do the interactions between groups of people impact how intelligently that group acts?
Chapter 7. Cognition and Collective Intelligence
Mark Steyvers (University of California, Irvine), Brent Miller (University of California, Irvine)
Understanding the conditions under which people are smart individually can help us predict when they might be smart collectively.
Chapter 8. Peer Production: A Modality of Collective Intelligence
Yochai Benkler (Harvard University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington)
What have collective efforts such as Wikipedia taught us about how large groups come together to create knowledge and creative artifacts?
The rise of open data driven businesses in emerging markets
Alla Morrison at the Worldbank blog:
Key findings —
- Many new data companies have emerged around the world in the last few years. Of these companies, the majority use some form of government data.
- There are a large number of data companies in sectors with high social impact and tremendous development opportunities.
- An actionable pipeline of data-driven companies exists in Latin America and in Asia. The most desired type of financing is equity, followed by quasi-equity in the amounts ranging from $100,000 to $5 million, with averages of between $2 and $3 million depending on the region. The total estimated need for financing may exceed $400 million.
“The economic value of open data is no longer a hypothesis
How can one make money with open data which is akin to air – free and open to everyone? Should the World Bank Group be in the catalyzer role for a sector that is just emerging? And if so, what set of interventions would be the most effective? Can promoting open data-driven businesses contribute to the World Bank Group’s twin goals of fighting poverty and boosting shared prosperity?
These questions have been top of the mind since the World Bank Open Finances team convened a group of open data entrepreneurs from across Latin America to share their business models, success stories and challenges at the Open Data Business Models workshop in Uruguay in June 2013. We were in Uruguay to find out whether open data could lead to the creation of sustainable new businesses and jobs. To do so, we tested a couple of hypotheses: open data has economic value, beyond the benefits of increased transparency and accountability; and open data companies with sustainable business models already exist in emerging economies.
Encouraged by our findings in Uruguay we set out to further explore the economic development potential of open data, with a focus on:
- Contribution of open data to countries’ GDP;
- Innovative solutions to tackle social problems in key sectors like agriculture, health, education, transportation, climate change, financial services, especially those benefiting low income populations;
- Economic benefits of governments’ buy-in into the commercial value of open data and resulting release of new datasets, which in turn would lead to increased transparency in public resource management (reductions in misallocations, a more level playing field in procurement) and better service delivery; and
- Creation of data-related private sector jobs, especially suited for the tech savvy young generation.
We proposed a joint IFC/World Bank approach (From open data to development impact – the crucial role of private sector) that envisages providing financing to data-driven companies through a dedicated investment fund, as well as loans and grants to governments to create a favorable enabling environment. The concept was received enthusiastically for the most part by a wide group of peers at the Bank, the IFC, as well as NGOs, foundations, DFIs and private sector investors.
Thanks also in part to a McKinsey report last fall stating that open data could help unlock more than $3 trillion in value every year, the potential value of open data is now better understood. The acquisition of Climate Corporation (whose business model holds enormous potential for agriculture and food security, if governments open up the right data) for close to a billion dollars last November and the findings of the Open Data 500 project led by GovLab of the NYU further substantiated the hypothesis. These days no one asks whether open data has economic value; the focus has shifted to finding ways for companies, both startups and large corporations, and governments to unlock it. The first question though is – is it still too early to plan a significant intervention to spur open data driven economic growth in emerging markets?”