John Podesta at the White House Blog: “Over the past several days, severe storms have battered Arkansas, Oklahoma, Mississippi and other states. Dozens of people have been killed and entire neighborhoods turned to rubble and debris as tornadoes have touched down across the region. Natural disasters like these present a host of challenges for first responders. How many people are affected, injured, or dead? Where can they find food, shelter, and medical attention? What critical infrastructure might have been damaged?
Drawing on open government data sources, including Census demographics and NOAA weather data, along with their own demographic databases, Esri, a geospatial technology company, has created a real-time map showing where the twisters have been spotted and how the storm systems are moving. They have also used these data to show how many people live in the affected area, and summarize potential impacts from the storms. It’s a powerful tool for emergency services and communities. And it’s driven by big data technology.
In January, President Obama asked me to lead a wide-ranging review of “big data” and privacy—to explore how these technologies are changing our economy, our government, and our society, and to consider their implications for our personal privacy. Together with Secretary of Commerce Penny Pritzker, Secretary of Energy Ernest Moniz, the President’s Science Advisor John Holdren, the President’s Economic Advisor Jeff Zients, and other senior officials, our review sought to understand what is genuinely new and different about big data and to consider how best to encourage the potential of these technologies while minimizing risks to privacy and core American values.
Over the course of 90 days, we met with academic researchers and privacy advocates, with regulators and the technology industry, with advertisers and civil rights groups. The President’s Council of Advisors for Science and Technology conducted a parallel study of the technological trends underpinning big data. The White House Office of Science and Technology Policy jointly organized three university conferences at MIT, NYU, and U.C. Berkeley. We issued a formal Request for Information seeking public comment, and hosted a survey to generate even more public input.
Today, we presented our findings to the President. We knew better than to try to answer every question about big data in three months. But we are able to draw important conclusions and make concrete recommendations for Administration attention and policy development in a few key areas.
There are a few technological trends that bear drawing out. The declining cost of collection, storage, and processing of data, combined with new sources of data like sensors, cameras, and geospatial technologies, mean that we live in a world of near-ubiquitous data collection. All this data is being crunched at a speed that is increasingly approaching real-time, meaning that big data algorithms could soon have immediate effects on decisions being made about our lives.
The big data revolution presents incredible opportunities in virtually every sector of the economy and every corner of society.
Big data is saving lives. Infections are dangerous—even deadly—for many babies born prematurely. By collecting and analyzing millions of data points from a NICU, one study was able to identify factors, like slight increases in body temperature and heart rate, that serve as early warning signs an infection may be taking root—subtle changes that even the most experienced doctors wouldn’t have noticed on their own.
Big data is making the economy work better. Jet engines and delivery trucks now come outfitted with sensors that continuously monitor hundreds of data points and send automatic alerts when maintenance is needed. Utility companies are starting to use big data to predict periods of peak electric demand, adjusting the grid to be more efficient and potentially averting brown-outs.
Big data is making government work better and saving taxpayer dollars. The Centers for Medicare and Medicaid Services have begun using predictive analytics—a big data technique—to flag likely instances of reimbursement fraud before claims are paid. The Fraud Prevention System helps identify the highest-risk health care providers for waste, fraud, and abuse in real time and has already stopped, prevented, or identified $115 million in fraudulent payments.
But big data raises serious questions, too, about how we protect our privacy and other values in a world where data collection is increasingly ubiquitous and where analysis is conducted at speeds approaching real time. In particular, our review raised the question of whether the “notice and consent” framework, in which a user grants permission for a service to collect and use information about them, still allows us to meaningfully control our privacy as data about us is increasingly used and reused in ways that could not have been anticipated when it was collected.
Big data raises other concerns, as well. One significant finding of our review was the potential for big data analytics to lead to discriminatory outcomes and to circumvent longstanding civil rights protections in housing, employment, credit, and the consumer marketplace.
No matter how quickly technology advances, it remains within our power to ensure that we both encourage innovation and protect our values through law, policy, and the practices we encourage in the public and private sector. To that end, we make six actionable policy recommendations in our report to the President:
Advance the Consumer Privacy Bill of Rights. Consumers deserve clear, understandable, reasonable standards for how their personal information is used in the big data era. We recommend the Department of Commerce take appropriate consultative steps to seek stakeholder and public comment on what changes, if any, are needed to the Consumer Privacy Bill of Rights, first proposed by the President in 2012, and to prepare draft legislative text for consideration by stakeholders and submission by the President to Congress.
Pass National Data Breach Legislation. Big data technologies make it possible to store significantly more data, and further derive intimate insights into a person’s character, habits, preferences, and activities. That makes the potential impacts of data breaches at businesses or other organizations even more serious. A patchwork of state laws currently governs requirements for reporting data breaches. Congress should pass legislation that provides for a single national data breach standard, along the lines of the Administration’s 2011 Cybersecurity legislative proposal.
Extend Privacy Protections to non-U.S. Persons. Privacy is a worldwide value that should be reflected in how the federal government handles personally identifiable information about non-U.S. citizens. The Office of Management and Budget should work with departments and agencies to apply the Privacy Act of 1974 to non-U.S. persons where practicable, or to establish alternative privacy policies that apply appropriate and meaningful protections to personal information regardless of a person’s nationality.
Ensure Data Collected on Students in School is used for Educational Purposes. Big data and other technological innovations, including new online course platforms that provide students real time feedback, promise to transform education by personalizing learning. At the same time, the federal government must ensure educational data linked to individual students gathered in school is used for educational purposes, and protect students against their data being shared or used inappropriately.
Expand Technical Expertise to Stop Discrimination. The detailed personal profiles held about many consumers, combined with automated, algorithm-driven decision-making, could lead—intentionally or inadvertently—to discriminatory outcomes, or what some are already calling “digital redlining.” The federal government’s lead civil rights and consumer protection agencies should expand their technical expertise to be able to identify practices and outcomes facilitated by big data analytics that have a discriminatory impact on protected classes, and develop a plan for investigating and resolving violations of law.
Amend the Electronic Communications Privacy Act. The laws that govern protections afforded to our communications were written before email, the internet, and cloud computing came into wide use. Congress should amend ECPA to ensure the standard of protection for online, digital content is consistent with that afforded in the physical world—including by removing archaic distinctions between email left unread or over a certain age.
We also identify several broader areas ripe for further study, debate, and public engagement that, collectively, we hope will spark a national conversation about how to harness big data for the public good. We conclude that we must find a way to preserve our privacy values in both the domestic and international marketplace. We urgently need to build capacity in the federal government to identify and prevent new modes of discrimination that could be enabled by big data. We must ensure that law enforcement agencies using big data technologies do so responsibly, and that our fundamental privacy rights remain protected. Finally, we recognize that data is a valuable public resource, and call for continuing the Administration’s efforts to open more government data sources and make investments in research and technology.
While big data presents new challenges, it also presents immense opportunities to improve lives, the United States is perhaps better suited to lead this conversation than any other nation on earth. Our innovative spirit, technological know-how, and deep commitment to values of privacy, fairness, non-discrimination, and self-determination will help us harness the benefits of the big data revolution and encourage the free flow of information while working with our international partners to protect personal privacy. This review is but one piece of that effort, and we hope it spurs a conversation about big data across the country and around the world.
Read the Big Data Report.
See the fact sheet from today’s announcement.“
Saving Big Data from Big Mouths
Cesar A. Hidalgo in Scientific American: “It has become fashionable to bad-mouth big data. In recent weeks the New York Times, Financial Times, Wired and other outlets have all run pieces bashing this new technological movement. To be fair, many of the critiques have a point: There has been a lot of hype about big data and it is important not to inflate our expectations about what it can do.
But little of this hype has come from the actual people working with large data sets. Instead, it has come from people who see “big data” as a buzzword and a marketing opportunity—consultants, event organizers and opportunistic academics looking for their 15 minutes of fame.
Most of the recent criticism, however, has been weak and misguided. Naysayers have been attacking straw men, focusing on worst practices, post hoc failures and secondary sources. The common theme has been to a great extent obvious: “Correlation does not imply causation,” and “data has biases.”
Critics of big data have been making three important mistakes:
First, they have misunderstood big data, framing it narrowly as a failed revolution in social science hypothesis testing. In doing so they ignore areas where big data has made substantial progress, such as data-rich Web sites, information visualization and machine learning. If there is one group of big-data practitioners that the critics should worship, they are the big-data engineers building the social media sites where their platitudes spread. Engineering a site rich in data, like Facebook, YouTube, Vimeo or Twitter, is extremely challenging. These sites are possible because of advances made quietly over the past five years, including improvements in database technologies and Web development frameworks.
Big data has also contributed to machine learning and computer vision. Thanks to big data, Facebook algorithms can now match faces almost as accurately as humans do.
And detractors have overlooked big data’s role in the proliferation of computational design, data journalism and new forms of artistic expression. Computational artists, journalists and designers—the kinds of people who congregate at meetings like Eyeo—are using huge sets of data to give us online experiences that are unlike anything we experienced in paper. If we step away from hypothesis testing, we find that big data has made big contributions.
The second mistake critics often make is to confuse the limitations of prototypes with fatal flaws. This is something I have experienced often. For example, in Place Pulse—a project I created with my team the M.I.T. Media Lab—we used Google Street View images and crowdsourced visual surveys to map people’s perception of a city’s safety and wealth. The original method was rife with limitations that we dutifully acknowledged in our paper. Google Street View images are taken at arbitrary times of the day and showed cities from the perspective of a car. City boundaries were also arbitrary. To overcome these limitations, however, we needed a first data set. Producing that first limited version of Place Pulse was a necessary part of the process of making a working prototype.
A year has passed since we published Place Pulse’s first data set. Now, thanks to our focus on “making,” we have computer vision and machine-learning algorithms that we can use to correct for some of these easy-to-spot distortions. Making is allowing us to correct for time of the day and dynamically define urban boundaries. Also, we are collecting new data to extend the method to new geographical boundaries.
Those who fail to understand that the process of making is iterative are in danger of being too quick to condemn promising technologies. In 1920 the New York Times published a prediction that a rocket would never be able to leave atmosphere. Similarly erroneous predictions were made about the car or, more recently, about iPhone’s market share. In 1969 the Times had to publish a retraction of their 1920 claim. What similar retractions will need to be published in the year 2069?
Finally, the doubters have relied too heavily on secondary sources. For instance, they made a piñata out of the 2008 Wired piece by Chris Anderson framing big data as “the end of theory.” Others have criticized projects for claims that their creators never made. A couple of weeks ago, for example, Gary Marcus and Ernest Davis published a piece on big data in the Times. There they wrote about another of one of my group’s projects, Pantheon, which is an effort to collect, visualize and analyze data on historical cultural production. Marcus and Davis wrote that Pantheon “suggests a misleading degree of scientific precision.” As an author of the project, I have been unable to find where I made such a claim. Pantheon’s method section clearly states that: “Pantheon will always be—by construction—an incomplete resource.” That same section contains a long list of limitations and caveats as well as the statement that “we interpret this data set narrowly, as the view of global cultural production that emerges from the multilingual expression of historical figures in Wikipedia as of May 2013.”
Bickering is easy, but it is not of much help. So I invite the critics of big data to lead by example. Stop writing op–eds and start developing tools that improve on the state of the art. They are much appreciated. What we need are projects that are worth imitating and that we can build on, not obvious advice such as “correlation does not imply causation.” After all, true progress is not something that is written, but made.”
Using data to treat the sickest and most expensive patients
Dan Gorenstein for Marketplace (radio): “Driving to a big data conference a few weeks back, Dr. Jeffrey Brenner brought his compact SUV to a full stop – in the middle of a short highway entrance ramp in downtown Philadelphia…
Here’s what you need to know about Dr. Jeffrey Brenner: He really likes to figure out how things work. And he’s willing to go to extremes to do it – so far that he’s risking his health policy celebrity status.
Perhaps it’s not the smartest move from a guy who just last fall was named a MacArthur Genius, but this month, Brenner began to test his theory for treating some of the sickest and most expensive patients.
“We can actually take the sickest and most complicated patients, go to their bedside, go to their home, go with them to their appointments and help them for about 90 days and dramatically improve outcomes and reduce cost,” he says.
That’s the theory anyway. Like many ideas when it comes to treating the sickest patients, there’s little data to back up that it works.
Brenner’s willing to risk his reputation precisely because he’s not positive his approach for treating folks who cycle in and out of the healthcare system — “super-utilizers” — actually works.
“It’s really easy for me at this point having gotten a MacArthur award to simply declare what we do works and to drive this work forward without rigorously testing it,” Brenner said. “We are not going to do that,” he said. “We don’t think that’s the right thing to do. So we are going to do a randomized controlled trial on our work and prove whether it works and how well it works.”
Helping lower costs and improve care for the super-utilizers is one of the most pressing policy questions in healthcare today. And given its importance, there is a striking lack of data in the field.
People like to call randomized controlled trials (RCTs) the gold standard of scientific testing because two groups are randomly assigned – one gets the treatment, while the other doesn’t – and researchers closely monitor differences.
But a 2012 British Medical Journal article found over the last 25 years, a total of six RCTs have focused on care delivery for super-utilizers.
Perhaps it’s not the smartest move from a guy who just last fall was named a MacArthur Genius, but this month, Brenner began to test his theory for treating some of the sickest and most expensive patients.
“We can actually take the sickest and most complicated patients, go to their bedside, go to their home, go with them to their appointments and help them for about 90 days and dramatically improve outcomes and reduce cost,” he says.
That’s the theory anyway. Like many ideas when it comes to treating the sickest patients, there’s little data to back up that it works.
Brenner’s willing to risk his reputation precisely because he’s not positive his approach for treating folks who cycle in and out of the healthcare system — “super-utilizers” — actually works.
“It’s really easy for me at this point having gotten a MacArthur award to simply declare what we do works and to drive this work forward without rigorously testing it,” Brenner said. “We are not going to do that,” he said. “We don’t think that’s the right thing to do. So we are going to do a randomized controlled trial on our work and prove whether it works and how well it works.”
Helping lower costs and improve care for the super-utilizers is one of the most pressing policy questions in healthcare today. And given its importance, there is a striking lack of data in the field.
People like to call randomized controlled trials (RCTs) the gold standard of scientific testing because two groups are randomly assigned – one gets the treatment, while the other doesn’t – and researchers closely monitor differences.
But a 2012 British Medical Journal article found over the last 25 years, a total of six RCTs have focused on care delivery for super-utilizers.
…Every major health insurance company – Medicare and Medicaid, too – has spent billions on programs for super-utilizers. The absence of rigorous evidence raises the question: Is all this effort built on health policy quicksand?
Not being 100 percent sure can be dangerous, says Duke behavioral scientist Peter Ubel, particularly in healthcare.
Ubel said back in the 1980s and 90s doctors prescribed certain drugs for irregular heartbeats. The medication, he said, made those weird rhythms go away, leaving beautiful-looking EKGs.
“But no one had tested whether people receiving these drugs actually lived longer, and many people thought, ‘Why would you do that? We can look at their cardiogram and see that they’re getting better,’” Ubel said. “Finally when somebody put that evidence to the test of a randomized trial, it turned out that these drugs killed people.”
WellPoint’s Nussbaum said he hoped Brenner’s project would inspire others to follow his lead and insert data into the discussion.
“I believe more people should be bold in challenging the status quo of our delivery system,” Nussbaum said. “The Jeff Brenners of the world should be embraced. We should be advocating for them to take on these studies.”
So why aren’t more healthcare luminaries putting their brilliance to the test? There are a couple of reasons.
Harvard economist Kate Baicker said until now there have been few personal incentives pushing people.
“If you’re focused on branding and spreading your brand, you have no incentive to say, ‘How good is my brand after all?’” she said.
And Venrock healthcare venture capitalist Bob Kocher said no one would fault Brenner if he put his brand before science, an age-old practice in this business.
“Healthcare has benefitted from the fact that you don’t understand it. It’s a bit of an art, and it hasn’t been a science,” he said. “You made money in healthcare by putting a banner outside your building saying you are a top something without having to justify whether you really are top at whatever you do.”
Duke’s Ubel said it’s too easy – and frankly, wrong – to say the main reason doctors avoid these rigorous studies is because they’re afraid to lose money and status. He said doctors aren’t immune from the very human trap of being sure their own ideas are right.
He says psychologists call it confirmation bias.
“Everything you see is filtered through your hopes, your expectations and your pre-existing beliefs,” Ubel said. “And that’s why I might look at a grilled cheese sandwich and see a grilled cheese sandwich and you might see an image of Jesus,” he says.
Even with all these hurdles, MIT economist Amy Finkelstein – who is running the RCT with Brenner – sees change coming.
“Providers have a lot more incentive now than they use to,” she said. “They have much more skin in the game.”
Finkelstein said hospital readmission penalties and new ways to pay doctors are bringing market incentives that have long been missing.
Brenner said he accepts that the truth of what he’s doing in Camden may be messier than the myth.
Feds see innovation decline within government
Federal Times: “Support for innovation is declining across the government, according to a report by the Partnership for Public Service released April 23.
Federal employee answers to three innovation-related questions on the annual Federal Employee Viewpoint Survey dropped from 61.5 out of 100 in 2012 to 59.4 out of 100, according to the report, produced in partnership with Deloitte.
Federal employee answers to three innovation-related questions on the annual Federal Employee Viewpoint Survey dropped from 61.5 out of 100 in 2012 to 59.4 out of 100, according to the report, produced in partnership with Deloitte.
This chart, extracted from the Partnership for Public Service report, shows the slow but steady decline of innovation measures. (Partnership for Public Service)
While 90 percent of employees surveyed report they are always looking for better ways to do their jobs only 54.7 percent feel encouraged to do so and only 33.4 percent believe their agency rewards creativity and innovation.
“The bottom line is that federal workers are motivated to improve the way they do their work, but they do not feel supported by their organizations,” the report said.
Dave Dye, a director of human capital at Deloitte, LLP, said the report is a message to agency leaders to pay attention and have discussions on innovation and make concerted efforts to enhance innovation in their areas.
“It’s not that leaders have to be innovative in their own right it means they need to set up environments for people to feel that innovation Is encouraged, rewarded and respected,” Dye said.
Most agencies saw a decline in their “innovation score” according to the report, including:
■ The Army saw one of the largest drops in its innovation score – from 64.2 out of 100 I 2012 to 60.1 out of 100 in 2013.
■ NASA – which had the highest score at 76.0 out of 100 in 2013 – also dropped from 76.5 in 2012.
■ The Financial Crimes Enforcement Network at the Treasury Department saw one of the largest drops among component agencies, from 63.8 out of 100 in 2012 to 52.0 in 2013.
Some agencies that have shown improvement are the National Science Foundation and the Peace Corps. Some NASA facilities also saw improvement, including the John C. Stennis Space Center in Mississippi and the George C. Marshall Space Flight Center in Alabama.
The ongoing effects of sequestration, budget cuts and threat of furloughs may also have had a dampening effect on federal employees, Dye said.
“When people feel safer or more sure about whats going on they are going to better focus on the mission,” he said.
Agency managers should also work to improve their work environments to build trust and confidence in their workforce by showing concerns about people’s careers and supporting development opportunities while recognizing good work, according to Dye.
The report recommends that agencies recognize employees at team meetings or with more formal awards to highlight innovation and creativity and reward success. Managers should make sure to share specific goals, provide a forum for open discussion and work to build trust among the workforce that is needed to help spur innovation.”
Paying Farmers to Welcome Birds
Jim Robbins in The New York Times: “The Central Valley was once one of North America’s most productive wildlife habitats, a 450-mile-long expanse marbled with meandering streams and lush wetlands that provided an ideal stop for migratory shorebirds on their annual journeys from South America and Mexico to the Arctic and back.
Farmers and engineers have long since tamed the valley. Of the wetlands that existed before the valley was settled, about 95 percent are gone, and the number of migratory birds has declined drastically. But now an unusual alliance of conservationists, bird watchers and farmers have joined in an innovative plan to restore essential habitat for the migrating birds.
The program, called BirdReturns, starts with data from eBird, the pioneering citizen science project that asks birders to record sightings on a smartphone app and send the information to the Cornell Lab of Ornithology in upstate New York.
By crunching data from the Central Valley, eBird can generate maps showing where virtually every species congregates in the remaining wetlands. Then, by overlaying those maps on aerial views of existing surface water, it can determine where the birds’ need for habitat is greatest….
BirdReturns is an example of the growing movement called reconciliation ecology, in which ecosystems dominated by humans are managed to increase biodiversity.
“It’s a new ‘Moneyball,’ ” said Eric Hallstein, an economist with the Nature Conservancy and a designer of the auctions, referring to the book and movie about the Oakland Athletics’ data-driven approach to baseball. “We’re disrupting the conservation industry by taking a new kind of data, crunching it differently and contracting differently.”
The Transformative Impact of Data and Communication on Governance
Steven Livingston at Brookings: “How do digital technologies affect governance in areas of limited statehood – places and circumstances characterized by the absence of state provisioning of public goods and the enforcement of binding rules with a monopoly of legitimate force? In the first post in this series I introduced the limited statehood concept and then described the tremendous growth in mobile telephony, GIS, and other technologies in the developing world. In the second post I offered examples of the use of ICT in initiatives intended to fill at least some of the governance vacuum created by limited statehood. With mobile phones, for example, farmers are informed of market conditions, have access to liquidity through M-Pesa and similar mobile money platforms….
This brings to mind another type of ICT governance initiative. Rather than fill in for or even displace the state some ICT initiatives can strengthen governance capacity. Digital government – the use of digital technology by the state itself — is one important possibility. Other initiatives strengthen the state by exerting pressure. Countries with weak governance sometimes take the form of extractive states or those, which cater to the needs of an elite, leaving the majority of the population in poverty and without basic public services. This is what Daron Acemoglu and James A. Robinson call extractive political and economic institutions. Inclusive states, on the other hand, are pluralistic, bound by the rule of law, respectful of property rights, and, in general, accountable. Accountability mechanisms such as a free press and competitive multiparty elections are instrumental to discourage extractive institutions. What ICT-based initiatives might lend a hand in strengthening accountability? We can point to three examples.
Example One: Using ICT to Protect Human Rights
Nonstate actors now use commercial, high-resolution remote sensing satellites to monitor weapons programs and human rights violations. Amnesty International’s Remote Sensing for Human Rights offers one example, and Satellite Sentinel offers another. Both use imagery from DigitalGlobe, an American remote sensing and geospatial content company. Other organizations have used commercially available remote sensing imagery to monitor weapons proliferation. The Institute for Science and International Security, a Washington-based NGO, revealed the Iranian nuclear weapons program in 2003 using commercial satellite imagery…
Example Two: Crowdsourcing Election Observation
Others have used mobile phones and GIS to crowdsource election observation. For the 2011 elections in Nigeria, The Community Life Project, a civil society organization, created ReclaimNaija, an elections process monitoring system that relied on GIS and amateur observers with mobile phones to monitor the elections. Each of the red dots represents an aggregation of geo-located incidents reported to the ReclaimNaija platform. In a live map, clicking on a dot disaggregates the reports, eventually taking the reader to individual reports. Rigorous statistical analysis of ReclaimNaija results and the elections suggest it contributed to the effectiveness of the election process.
ReclaimNaija: Election Incident Reporting System Map
Example Three: Using Genetic Analysis to Identify War Crimes
In recent years, more powerful computers have led to major breakthroughs in biomedical science. The reduction in cost of analyzing the human genome has actually outpaced Moore’s Law. This has opened up new possibilities for the use of genetic analysis in forensic anthropology. In Guatemala, the Balkans, Argentina, Peru and in several other places where mass executions and genocides took place, forensic anthropologists are using genetic analysis to find evidence that is used to hold the killers – often state actors – accountable…”
The Data Mining Techniques That Reveal Our Planet's Cultural Links and Boundaries
Emerging Technology From the arXiv: “The habits and behaviors that define a culture are complex and fascinating. But measuring them is a difficult task. What’s more, understanding the way cultures change from one part of the world to another is a task laden with challenges.
The gold standard in this area of science is known as the World Values Survey, a global network of social scientists studying values and their impact on social and political life. Between 1981 and 2008, this survey conducted over 250,000 interviews in 87 societies. That’s a significant amount of data and the work has continued since then. This work is hugely valuable but it is also challenging, time-consuming and expensive.
Today, Thiago Silva at the Universidade Federal de Minas Gerais in Brazil and a few buddies reveal another way to collect data that could revolutionize the study of global culture. These guys study cultural differences around the world using data generated by check-ins on the location-based social network, Foursquare.
That allows these researchers to gather huge amounts of data, cheaply and easily in a short period of time. “Our one-week dataset has a population of users of the same order of magnitude of the number of interviews performed in [the World Values Survey] in almost three decades,” they say.
Food and drink are fundamental aspects of society and so the behaviors and habits associated with them are important indicators. The basic question that Silva and co attempt to answer is: what are your eating and drinking habits? And how do these differ from a typical individual in another part of the world such as Japan, Malaysia, or Brazil?
Foursquare is ideally set up to explore this question. Users “check in” by indicating when they have reached a particular location that might be related to eating and drinking but also to other activities such as entertainment, sport and so on.
Silva and co are only interested in the food and drink preferences of individuals and, in particular, on the way these preferences change according to time of day and geographical location.
So their basic approach is to compare a large number individual preferences from different parts of the world and see how closely they match or how they differ.
Because Foursquare does not share its data, Silva and co downloaded almost five million tweets containing Foursquare check-ins, URLs pointing to the Foursquare website containing information about each venue. They discarded check-ins that were unrelated to food or drink.
That left them with some 280,000 check-ins related to drink from 160,000 individuals; over 400,000 check-ins related to fast food from 230,000 people; and some 400,000 check-ins relating to ordinary restaurant food or what Silva and co call slow food.
They then divide each of these classes into subcategories. For example, the drink class has 21 subcategories such as brewery, karaoke bar, pub, and so on. The slow food class has 53 subcategories such as Chinese restaurant, Steakhouse, Greek restaurant, and so on.
Each check-in gives the time and geographical location which allows the team to compare behaviors from all over the world. They compare, for example, eating and drinking times in different countries both during the week and at the weekend. They compare the choices of restaurants, fast food habits and drinking habits by continent and country. The even compare eating and drinking habits in New York, London, and Tokyo.
The results are a fascinating insight into humanity’s differing habits. Many places have similar behaviors, Malaysia and Singapore or Argentina and Chile, for example, which is just as expected given the similarities between these places.
But other resemblances are more unexpected. A comparison of drinking habits show greater similarity between Brazil and France, separated by the Atlantic Ocean, than they do between France and England, separated only by the English Channel…
They point out only two major differences. The first is that no Islamic cluster appears in the Foursquare data. Countries such as Turkey are similar to Russia, while Indonesia seems related to Malaysia and Singapore.
The second is that the U.S. and Mexico make up their own individual cluster in the Foursquare data whereas the World Values Survey has them in the “English-speaking” and “Latin American” clusters accordingly.
That’s exciting data mining work that has the potential to revolutionize the way sociologists and anthropologists study human culture around the world. Expect to hear more about it
Ref: http://arxiv.org/abs/1404.1009: You Are What You Eat (and Drink): Identifying Cultural Boundaries By Analyzing Food & Drink Habits In Foursquare”.
Politics and the Internet
Edited book by William H. Dutton (Routledge – 2014 – 1,888 pages: “It is commonplace to observe that the Internet—and the dizzying technologies and applications which it continues to spawn—has revolutionized human communications. But, while the medium’s impact has apparently been immense, the nature of its political implications remains highly contested. To give but a few examples, the impact of networked individuals and institutions has prompted serious scholarly debates in political science and related disciplines on: the evolution of ‘e-government’ and ‘e-politics’ (especially after recent US presidential campaigns); electronic voting and other citizen participation; activism; privacy and surveillance; and the regulation and governance of cyberspace.
As research in and around politics and the Internet flourishes as never before, this new four-volume collection from Routledge’s acclaimed Critical Concepts in Political Science series meets the need for an authoritative reference work to make sense of a rapidly growing—and ever more complex—corpus of literature. Edited by William H. Dutton, Director of the Oxford Internet Institute (OII), the collection gathers foundational and canonical work, together with innovative and cutting-edge applications and interventions.
With a full index and comprehensive bibliographies, together with a new introduction by the editor, which places the collected material in its historical and intellectual context, Politics and the Internet is an essential work of reference. The collection will be particularly useful as a database allowing scattered and often fugitive material to be easily located. It will also be welcomed as a crucial tool permitting rapid access to less familiar—and sometimes overlooked—texts. For researchers, students, practitioners, and policy-makers, it is a vital one-stop research and pedagogic resource.”
Eight (No, Nine!) Problems With Big Data
Gary Marcus and Ernest Davis in the New York Times: “BIG data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.
Or so its champions allege. “In the next two decades,” the journalist Patrick Tucker writes in the latest big data manifesto, “The Naked Future,” “we will be able to predict huge areas of the future with far greater accuracy than ever before in human history, including events long thought to be beyond the realm of human inference.” Statistical correlations have never sounded so good.
Is big data really all it’s cracked up to be? There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I.B.M. “Jeopardy!” champion Watson, has involved the substantial crunching of large bodies of data. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.
The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful. A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply. But it’s hard to imagine there is any causal relationship between the two. Likewise, from 1998 to 2007 the number of new cases of autism diagnosed was extremely well correlated with sales of organic food (both went up sharply), but identifying the correlation won’t by itself tell us whether diet has anything to do with autism.
Second, big data can work well as an adjunct to scientific inquiry but rarely succeeds as a wholesale replacement. Molecular biologists, for example, would very much like to be able to infer the three-dimensional structure of proteins from their underlying DNA sequence, and scientists working on the problem use big data as one tool among many. But no scientist thinks you can solve this problem by crunching data alone, no matter how powerful the statistical analysis; you will always need to start with an analysis that relies on an understanding of physics and biochemistry.
Third, many tools that are based on big data can be easily gamed. For example, big data programs for grading student essays often rely on measures like sentence length and word sophistication, which are found to correlate well with the scores given by human graders. But once students figure out how such a program works, they start writing long sentences and using obscure words, rather than learning how to actually formulate and write clear, coherent text. Even Google’s celebrated search engine, rightly seen as a big data success story, is not immune to “Google bombing” and “spamdexing,” wily techniques for artificially elevating website search placement.
Fourth, even when the results of a big data analysis aren’t intentionally gamed, they often turn out to be less robust than they initially seem. Consider Google Flu Trends, once the poster child for big data. In 2009, Google reported — to considerable fanfare — that by analyzing flu-related search queries, it had been able to detect the spread of the flu as accurately and more quickly than the Centers for Disease Control and Prevention. A few years later, though, Google Flu Trends began to falter; for the last two years it has made more bad predictions than good ones.
As a recent article in the journal Science explained, one major contributing cause of the failures of Google Flu Trends may have been that the Google search engine itself constantly changes, such that patterns in data collected at one time do not necessarily apply to data collected at another time. As the statistician Kaiser Fung has noted, collections of big data that rely on web hits often merge data that was collected in different ways and with different purposes — sometimes to ill effect. It can be risky to draw conclusions from data sets of this kind.
A fifth concern might be called the echo-chamber effect, which also stems from the fact that much of big data comes from the web. Whenever the source of information for a big data analysis is itself a product of big data, opportunities for vicious cycles abound. Consider translation programs like Google Translate, which draw on many pairs of parallel texts from different languages — for example, the same Wikipedia entry in two different languages — to discern the patterns of translation between those languages. This is a perfectly reasonable strategy, except for the fact that with some of the less common languages, many of the Wikipedia articles themselves may have been written using Google Translate. In those cases, any initial errors in Google Translate infect Wikipedia, which is fed back into Google Translate, reinforcing the error.
A sixth worry is the risk of too many correlations. If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant — even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.
Seventh, big data is prone to giving scientific-sounding solutions to hopelessly imprecise questions. In the past few months, for instance, there have been two separate attempts to rank people in terms of their “historical importance” or “cultural contributions,” based on data drawn from Wikipedia. One is the book “Who’s Bigger? Where Historical Figures Really Rank,” by the computer scientist Steven Skiena and the engineer Charles Ward. The other is an M.I.T. Media Lab project called Pantheon.
Both efforts get many things right — Jesus, Lincoln and Shakespeare were surely important people — but both also make some egregious errors. “Who’s Bigger?” claims that Francis Scott Key was the 19th most important poet in history; Pantheon has claimed that Nostradamus was the 20th most important writer in history, well ahead of Jane Austen (78th) and George Eliot (380th). Worse, both projects suggest a misleading degree of scientific precision with evaluations that are inherently vague, or even meaningless. Big data can reduce anything to a single number, but you shouldn’t be fooled by the appearance of exactitude.
FINALLY, big data is at its best when analyzing things that are extremely common, but often falls short when analyzing things that are less common. For instance, programs that use big data to deal with text, such as search engines and translation programs, often rely heavily on something called trigrams: sequences of three words in a row (like “in a row”). Reliable statistical information can be compiled about common trigrams, precisely because they appear frequently. But no existing body of data will ever be large enough to include all the trigrams that people might use, because of the continuing inventiveness of language.
To select an example more or less at random, a book review that the actor Rob Lowe recently wrote for this newspaper contained nine trigrams such as “dumbed-down escapist fare” that had never before appeared anywhere in all the petabytes of text indexed by Google. To witness the limitations that big data can have with novelty, Google-translate “dumbed-down escapist fare” into German and then back into English: out comes the incoherent “scaled-flight fare.” That is a long way from what Mr. Lowe intended — and from big data’s aspirations for translation.
Wait, we almost forgot one last problem: the hype….
Smart cities are here today — and getting smarter
Computer World: “Smart cities aren’t a science fiction, far-off-in-the-future concept. They’re here today, with municipal governments already using technologies that include wireless networks, big data/analytics, mobile applications, Web portals, social media, sensors/tracking products and other tools.
These smart city efforts have lofty goals: Enhancing the quality of life for citizens, improving government processes and reducing energy consumption, among others. Indeed, cities are already seeing some tangible benefits.
But creating a smart city comes with daunting challenges, including the need to provide effective data security and privacy, and to ensure that myriad departments work in harmony.
The global urban population is expected to grow approximately 1.5% per year between 2025 and 2030, mostly in developing countries, according to the World Health Organization.
What makes a city smart? As with any buzz term, the definition varies. But in general, it refers to using information and communications technologies to deliver sustainable economic development and a higher quality of life, while engaging citizens and effectively managing natural resources.
Making cities smarter will become increasingly important. For the first time ever, the majority of the world’s population resides in a city, and this proportion continues to grow, according to the World Health Organization, the coordinating authority for health within the United Nations.
A hundred years ago, two out of every 10 people lived in an urban area, the organization says. As recently as 1990, less than 40% of the global population lived in a city — but by 2010 more than half of all people lived in an urban area. By 2050, the proportion of city dwellers is expected to rise to 70%.
As many city populations continue to grow, here’s what five U.S. cities are doing to help manage it all:
Scottsdale, Ariz.
The city of Scottsdale, Ariz., has several initiatives underway.
One is MyScottsdale, a mobile application the city deployed in the summer of 2013 that allows citizens to report cracked sidewalks, broken street lights and traffic lights, road and sewer issues, graffiti and other problems in the community….”