Country and Region: Americas

The promise and perils of predictive policing based on big data

Curated on Posted on by Stefaan Verhulst

H. V. Jagadish in the Conversation: “Police departments, like everyone else, would like to be more effective while spending less. Given the tremendous attention to big data in recent years, and the value it has provided in fields ranging from astronomy to medicine, it should be no surprise that police departments are using data analysis to inform deployment of scarce resources. Enter the era of what is called “predictive policing.”

Some form of predictive policing is likely now in force in a city near you.Memphis was an early adopter. Cities from Minneapolis to Miami have embraced predictive policing. Time magazine named predictive policing (with particular reference to the city of Santa Cruz) one of the 50 best inventions of 2011. New York City Police Commissioner William Bratton recently said that predictive policing is “the wave of the future.”

The term “predictive policing” suggests that the police can anticipate a crime and be there to stop it before it happens and/or apprehend the culprits right away. As the Los Angeles Times points out, it depends on “sophisticated computer analysis of information about previous crimes, to predict where and when crimes will occur.”

At a very basic level, it’s easy for anyone to read a crime map and identify neighborhoods with higher crime rates. It’s also easy to recognize that burglars tend to target businesses at night, when they are unoccupied, and to target homes during the day, when residents are away at work. The challenge is to take a combination of dozens of such factors to determine where crimes are more likely to happen and who is more likely to commit them. Predictive policing algorithms are getting increasingly good at such analysis. Indeed, such was the premise of the movie Minority Report, in which the police can arrest and convict murderers before they commit their crime.

Predicting a crime with certainty is something that science fiction can have a field day with. But as a data scientist, I can assure you that in reality we can come nowhere close to certainty, even with advanced technology. To begin with, predictions can be only as good as the input data, and quite often these input data have errors.

But even with perfect, error-free input data and unbiased processing, ultimately what the algorithms are determining are correlations. Even if we have perfect knowledge of your troubled childhood, your socializing with gang members, your lack of steady employment, your wacko posts on social media and your recent gun purchases, all that the best algorithm can do is to say it is likely, but not certain, that you will commit a violent crime. After all, to believe such predictions as guaranteed is to deny free will….

What data can do is give us probabilities, rather than certainty. Good data coupled with good analysis can give us very good estimates of probability. If you sum probabilities over many instances, you can usually get a robust estimate of the total.

For example, data analysis can provide a probability that a particular house will be broken into on a particular day based on historical records for similar houses in that neighborhood on similar days. An insurance company may add this up over all days in a year to decide how much to charge for insuring that house….(More)”

Questioning Smart Urbanism: Is Data-Driven Governance a Panacea?

Curated on Posted on by Stefaan Verhulst

Alice Tang at the Chicago Policy Review: “In the era of data explosion, urban planners are increasingly relying on real-time, streaming data generated by “smart” devices to assist with city management. “Smart cities,” referring to cities that implement pervasive and ubiquitous computing in urban planning, are widely discussed in academia, business, and government. These cities are characterized not only by their use of technology but also by their innovation-driven economies and collaborative, data-driven city governance. Smart urbanism can seem like an effective strategy to create more efficient, sustainable, productive, and open cities. However, there are emerging concerns about the potential risks in the long-term development of smart cities, including political neutrality of big data, technocratic governance, technological lock-ins, data and network security, and privacy risks.

In a study entitled, “The Real-Time City? Big Data and Smart Urbanism,” Rob Kitchin provides a critical reflection on the potential negative effects of data-driven city governance on social development—a topic he claims deserves greater governmental, academic, and social attention.

In contrast to traditional datasets that rely on samples or are aggregated to a coarse scale, “big data” is huge in volume, high in velocity, and diverse in variety. Since the early 2000s, there has been explosive growth in data volume due to the rapid development and implementation of technology infrastructure, including networks, information management, and data storage. Big data can be generated from directed, automated, and volunteered sources. Automated data generation is of particular interest to urban planners. One example Kitchin cites is urban sensor networks, which allow city governments to monitor the movements and statuses of individuals, materials, and structures throughout the urban environment by analyzing real-time data.

With the huge amount of streaming data collected by smart infrastructure, many city governments use real-time analysis to manage different aspects of city operations. There has been a recent trend in centralizing data streams into a single hub, integrating all kinds of surveillance and analytics. These one-stop data centers make it easier for analysts to cross-reference data, spot patterns, identify problems, and allocate resources. The data are also often accessible by field workers via operations platforms. In London and some other cities, real-time data are visualized on “city dashboards” and communicated to citizens, providing convenient access to city information.

However, the real-time city is not a flawless solution to all the problems faced by city managers. The primary concern is the politics of big, urban data. Although raw data are often perceived as neutral and objective, no data are free of bias; the collection of data is a subjective process that can be shaped by various confounding factors. The presentation of data can also be manipulated to answer a specific question or enact a particular political vision….(More)”

The Power of Nudges, for Good and Bad

Curated on Posted on by Stefaan Verhulst

Richard H. Thaler in the New York Times: “Nudges, small design changes that can markedly affect individual behavior, have been catching on. These techniques rely on insights from behavioral science, and when used ethically, they can be very helpful. But we need to be sure that they aren’t being employed to sway people to make bad decisions that they will later regret.

Whenever I’m asked to autograph a copy of “Nudge,” the book I wrote with Cass Sunstein, the Harvard law professor, I sign it, “Nudge for good.” Unfortunately, that is meant as a plea, not an expectation.

Three principles should guide the use of nudges:

■ All nudging should be transparent and never misleading.

■ It should be as easy as possible to opt out of the nudge, preferably with as little as one mouse click.

■ There should be good reason to believe that the behavior being encouraged will improve the welfare of those being nudged.
As far as I know, the government teams in Britain and the United States that have focused on nudging have followed these guidelines scrupulously. But the private sector is another matter. In this domain, I see much more troubling behavior.

For example, last spring I received an email telling me that the first prominent review of a new book of mine had appeared: It was in The Times of London. Eager to read the review, I clicked on a hyperlink, only to run into a pay wall. Still, I was tempted by an offer to take out a one-month trial subscription for the price of just £1. As both a consumer and producer of newspaper articles, I have no beef with pay walls. But before signing up, I read the fine print. As expected, I would have to provide credit card information and would be automatically enrolled as a subscriber when the trial period expired. The subscription rate would then be £26 (about $40) a month. That wasn’t a concern because I did not intend to become a paying subscriber. I just wanted to read that one article.

But the details turned me off. To cancel, I had to give 15 days’ notice, so the one-month trial offer actually was good for just two weeks. What’s more, I would have to call London, during British business hours, and not on a toll-free number. That was both annoying and worrying. As an absent-minded American professor, I figured there was a good chance I would end up subscribing for several months, and that reading the article would end up costing me at least £100….

These examples are not unusual. Many companies are nudging purely for their own profit and not in customers’ best interests. In a recent column in The New York Times, Robert Shiller called such behavior “phishing.” Mr. Shiller and George Akerlof, both Nobel-winning economists, have written a book on the subject, “Phishing for Phools.”

Some argue that phishing — or evil nudging — is more dangerous in government than in the private sector. The argument is that government is a monopoly with coercive power, while we have more choice in the private sector over which newspapers we read and which airlines we fly.

I think this distinction is overstated. In a democracy, if a government creates bad policies, it can be voted out of office. Competition in the private sector, however, can easily work to encourage phishing rather than stifle it.

One example is the mortgage industry in the early 2000s. Borrowers were encouraged to take out loans that they could not repay when real estate prices fell. Competition did not eliminate this practice, because it was hard for anyone to make money selling the advice “Don’t take that loan.”

As customers, we can help one another by resisting these come-ons. The more we turn down questionable offers like trip insurance and scrutinize “one month” trials, the less incentive companies will have to use such schemes. Conversely, if customers reward firms that act in our best interests, more such outfits will survive and flourish, and the options available to us will improve….(More)

Open Data as Open Educational Resources: Case studies of emerging practice

Curated on Posted on by Stefaan Verhulst

Book edited by Javiera Atenas and Leo Havemann: “…is the outcome of a collective effort that has its origins in the 5th Open Knowledge Open Education Working Group call, in which the idea of using Open Data in schools was mentioned. It occurred to us that Open Data and open educational resources seemed to us almost to exist in separate open worlds.

We decided to seek out evidence in the use of open data as OER, initially by conducting a bibliographical search. As we could not find published evidence, we decided to ask educators if they were in fact, using open data in this way, and wrote a post for this blog (with Ernesto Priego) explaining our perspective, called The 21st Century’s Raw Material: Using Open Data as Open Educational Resources. We ended the post with a link to an exploratory survey, the results of which indicated a need for more awareness of the existence and potential value of Open Data amongst educators…..

the case studies themselves. They have been provided by scholars and practitioners from different disciplines and countries, and they reflect different approaches to the use of open data. The first case study presents an approach to educating both teachers and students in the use of open data for civil monitoring via Scuola di OpenCoesione in Italy, and has been written by Chiara Ciociola and Luigi Reggi. The second case, by Tim Coughlan from the Open University, UK, showcases practical applications in the use of local and contextualised open data for the development of apps. The third case, written by Katie Shamash, Juan Pablo Alperin & Alessandra Bordini from Simon Fraser University, Canada, demonstrates how publishing students can engage, through data analysis, in very current debates around scholarly communications and be encouraged to publish their own findings. The fourth case by Alan Dix from Talis and University of Birmingham, UK, and Geoffrey Ellis from University of Konstanz, Germany, is unique because the data discussed in this case is self-produced, indeed ‘quantified self’ data, which was used with students as material for class discussion and, separately, as source data for another student’s dissertation project. Finally, the fifth case, presented by Virginia Power from University of the West of England, UK, examines strategies to develop data and statistical literacies in future librarians and knowledge managers, aiming to support and extend their theoretical understanding of the concept of the ‘knowledge society’ through the use of Open Data….(More)

The book can be downloaded here Open Data as Open Educational Resources

Does Open Data Need Journalism?

Curated on Posted on by Stefaan Verhulst

Paper by Jonathan Stoneman at Reuters Institute for Journalism: “The Open Data movement really came into being when President Obama issued his first policy paper, on his first day in office in January 2009. The US government opened up thousands of datasets to scrutiny by the public, by journalists, by policy-makers. Coders and developers were also invited to make the data useful to people and businesses in all manner of ways. Other governments across the globe followed suit, opening up data to their populations.

Opening data in this way has not resulted in genuine openness, save in a few isolated cases. In the USA and a few European countries, developers have created apps and websites which draw on Open Data, but these are not reaching a mass audience.

At the same time, journalists are not seen by government as the end users of these data. Data releases, even in the best cases, are uneven, and slow, and do not meet the needs of journalists. Although thousands of journalists have been learning and adopting the new skills of datajournalism they have tended to work with data obtained through Freedom of Information (FOI) legislation.

Stories which have resulted from datajournalists’ efforts have rarely been front page news; in many cases data-driven stories have ended up as lesser stories on inside pages, or as infographics, which relatively few people look at.

In this context, therefore, Open Data remains outside the mainstream of journalism, and out of the consciousness of the electorate, begging the question, “what are Open Data for?”, or as one developer put it – “if Open Data is the answer, what was the question?” Openness is seen as a badge of honour – scores of national governments have signed pledges to make data open, often repeating the same kind of idealistic official language as the previous announcement of a conversion to openness. But these acts are “top down”, and soon run out of momentum, becoming simply openness for its own sake. Looking at specific examples, the United States is the nearest to a success story: there is a rich ecosystem – made up of government departments, interest groups and NGOs, the media, civil society – which allows data driven projects the space to grow and the airtime to make an impact. (It probably helped that the media in the US were facing an existential challenge urgent enough to force them to embrace new, inexpensive, ways of carrying out investigative reporting).

Elsewhere data are making less impact on journalism. In the UK the new openness is being exploited by a small minority. Where data are made published on the data.gov.uk website they are frequently out of date, incomplete, or of limited new value, so where data do drive stories, these tend to be data released under FOI legislation, and the resulting stories take the form of statistics and/or infographics.

In developing countries where Open Data Portals have been launched with a fanfare – such as Kenya, and more recently Burkina Faso – there has been little uptake by coders, journalists, or citizens, and the number of fresh datasets being published drops to a trickle, and are soon well out of date. Small, apparently randomly selected datasets are soon outdated and inertia sets in.

The British Conservative Party, pledging greater openness in its 2010 manifesto, foresaw armies of “Armchair Auditors” who would comb through the data and present the government with ideas for greater efficiency in the use of public funds. Almost needless to say, these armies have never materialised, and thousands of datasets go unscrutinised by anybody. 2 In countries like Britain large amounts of data are being published but going (probably) unread and unscrutinised by anybody. At the same time, the journalists who want to make use of data are getting what they need through FOI, or even by gathering data themselves. Open Data is thus being bypassed, and could become an irrelevance. Yet, the media could be vital agents in the quest for the release of meaningful, relevant, timely data.

Governments seem in no hurry to expand the “comfort zone” from which they release the data which shows their policies at their most effective, and keeping to themselves data which paints a gloomier picture. Journalists seem likely to remain in their comfort zone, where they make use of FOI and traditional sources of information. For their part, journalists should push for better data and use it more, working in collaboration with open data activists. They need to change the habits of a lifetime and discuss their sources: revealing the source and quality of data used in a story would in itself be as much a part of the advocacy as of the actual reporting.

If Open Data are to be part of a new system of democratic accountability, they need to be more than a gesture of openness. Nor should Open Data remain largely the preserve of companies using them for commercial purposes. Governments should improve the quality and relevance of published data, making them genuinely useful for journalists and citizens alike….(More)”

Politics and the New Machine

Curated on Posted on by Stefaan Verhulst

Jill Lepore in the NewYorker on “What the turn from polls to data science means for democracy”: “…The modern public-opinion poll has been around since the Great Depression, when the response rate—the number of people who take a survey as a percentage of those who were asked—was more than ninety. The participation rate—the number of people who take a survey as a percentage of the population—is far lower. Election pollsters sample only a minuscule portion of the electorate, not uncommonly something on the order of a couple of thousand people out of the more than two hundred million Americans who are eligible to vote. The promise of this work is that the sample is exquisitely representative. But the lower the response rate the harder and more expensive it becomes to realize that promise, which requires both calling many more people and trying to correct for “non-response bias” by giving greater weight to the answers of people from demographic groups that are less likely to respond. Pollster.com’s Mark Blumenthal has recalled how, in the nineteen-eighties, when the response rate at the firm where he was working had fallen to about sixty per cent, people in his office said, “What will happen when it’s only twenty? We won’t be able to be in business!” A typical response rate is now in the single digits.

Meanwhile, polls are wielding greater influence over American elections than ever….

Still, data science can’t solve the biggest problem with polling, because that problem is neither methodological nor technological. It’s political. Pollsters rose to prominence by claiming that measuring public opinion is good for democracy. But what if it’s bad?

A “poll” used to mean the top of your head. Ophelia says of Polonius, “His beard as white as snow: All flaxen was his poll.” When voting involved assembling (all in favor of Smith stand here, all in favor of Jones over there), counting votes required counting heads; that is, counting polls. Eventually, a “poll” came to mean the count itself. By the nineteenth century, to vote was to go “to the polls,” where, more and more, voting was done on paper. Ballots were often printed in newspapers: you’d cut one out and bring it with you. With the turn to the secret ballot, beginning in the eighteen-eighties, the government began supplying the ballots, but newspapers kept printing them; they’d use them to conduct their own polls, called “straw polls.” Before the election, you’d cut out your ballot and mail it to the newspaper, which would make a prediction. Political parties conducted straw polls, too. That’s one of the ways the political machine worked….

Ever since Gallup, two things have been called polls: surveys of opinions and forecasts of election results. (Plenty of other surveys, of course, don’t measure opinions but instead concern status and behavior: Do you own a house? Have you seen a doctor in the past month?) It’s not a bad idea to reserve the term “polls” for the kind meant to produce election forecasts. When Gallup started out, he was skeptical about using a survey to forecast an election: “Such a test is by no means perfect, because a preelection survey must not only measure public opinion in respect to candidates but must also predict just what groups of people will actually take the trouble to cast their ballots.” Also, he didn’t think that predicting elections constituted a public good: “While such forecasts provide an interesting and legitimate activity, they probably serve no great social purpose.” Then why do it? Gallup conducted polls only to prove the accuracy of his surveys, there being no other way to demonstrate it. The polls themselves, he thought, were pointless…

If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan, lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism….

Data science may well turn out to be as flawed as public-opinion polling. But a stage in the development of any new tool is to imagine that you’ve perfected it, in order to ponder its consequences. I asked Hilton to suppose that there existed a flawless tool for measuring public opinion, accurately and instantly, a tool available to voters and politicians alike. Imagine that you’re a member of Congress, I said, and you’re about to head into the House to vote on an act—let’s call it the Smeadwell-Nutley Act. As you do, you use an app called iThePublic to learn the opinions of your constituents. You oppose Smeadwell-Nutley; your constituents are seventy-nine per cent in favor of it. Your constituents will instantly know how you’ve voted, and many have set up an account with Crowdpac to make automatic campaign donations. If you vote against the proposed legislation, your constituents will stop giving money to your reëlection campaign. If, contrary to your convictions but in line with your iThePublic, you vote for Smeadwell-Nutley, would that be democracy? …(More)”

 

How Satellite Data and Artificial Intelligence could help us understand poverty better

Curated on Posted on by Stefaan Verhulst

Maya Craig at Fast Company: “Governments and development organizations currently measure poverty levels by conducting door-to-door surveys. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth.

The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide. A recent study conducted by the organization found that more than 50 countries lack legitimate poverty estimates, which limits the ability of the development community to support the world’s poorest populations.

“Data depravation is a serious issue, especially in many of the countries where we need it most,” says David Newhouse, senior economist at the World Bank. “This technology has the potential to help us get that data more frequently and at a finer level of detail than is currently possible.”

The announcement is the latest in an emerging industry of AI analysis of satellite photos. A growing number of investors and entrepreneurs are betting that the convergence of these fields will have far-reaching impacts on business, policy, resource management and disaster response.

Wall Street’s biggest hedge-fund businesses have begun using the technology to improve investment strategies. The Pew Charitable Trust employs the method to monitor oceans for illegal fishing activities. And startups like San Francisco-based Mavrx use similar analytics to optimize crop harvest.

The commercial earth-imaging satellite market, valued at $2.7 billion in 2014, is predicted to grow by 14% each year through the decade, according to a recent report.

As recently as two years ago, there were just four commercial earth imaging satellites operated in the U.S., and government contracts accounted for about 70% of imagery sales. By 2020, there will be hundreds of private-sector “smallsats” in orbit capturing imagery that will be easily accessible online. Companies like Skybox Imaging and Planet Labs have the first of these smallsats already active, with plans for more.

The images generated by these companies will be among the world’s largest data sets. And recent breakthroughs in AI research have made it possible to analyze these images to inform decision-making…(More)”

Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government

Curated on Posted on by Stefaan Verhulst

Paper by Jan Whittington et al: “Cities hold considerable information, including details about the daily lives of residents and employees, maps of critical infrastructure, and records of the officials’ internal deliberations. Cities are beginning to realize that this data has economic and other value: If done wisely, the responsible release of city information can also release greater efficiency and innovation in the public and private sector. New services are cropping up that leverage open city data to great effect.

Meanwhile, activist groups and individual residents are placing increasing pressure on state and local government to be more transparent and accountable, even as others sound an alarm over the privacy issues that inevitably attend greater data promiscuity. This takes the form of political pressure to release more information, as well as increased requests for information under the many public records acts across the country.

The result of these forces is that cities are beginning to open their data as never before. It turns out there is surprisingly little research to date into the important and growing area of municipal open data. This article is among the first sustained, cross-disciplinary assessments of an open municipal government system. We are a team of researchers in law, computer science, information science, and urban studies. We have worked hand-in-hand with the City of Seattle, Washington for the better part of a year to understand its current procedures from each disciplinary perspective. Based on this empirical work, we generate a set of recommendations to help the city manage risk latent in opening its data….(More)”

Using Crowdsourcing to Track the Next Viral Disease Outbreak

Curated on Posted on by Stefaan Verhulst

The TakeAway: “Last year’s Ebola outbreak in West Africa killed more than 11,000 people. The pandemic may be diminished, but public health officials think that another major outbreak of infectious disease is fast-approaching, and they’re busy preparing for it.

Boston public radio station WGBH recently partnered with The GroundTruth Project and NOVA Next on a series called “Next Outbreak.” As part of the series, they reported on an innovative global online monitoring system called HealthMap, which uses the power of the internet and crowdsourcing to detect and track emerging infectious diseases, and also more common ailments like the flu.

Researchers at Boston Children’s Hospital are the ones behind HealthMap (see below), and they use it to tap into tens of thousands of sources of online data, including social media, news reports, and blogs to curate information about outbreaks. Dr. John Brownstein, chief innovation officer at Boston Children’s Hospital and co-founder of HealthMap, says that smarter data collection can help to quickly detect and track emerging infectious diseases, fatal or not.

“Traditional public health is really slowed down by the communication process: People get sick, they’re seen by healthcare providers, they get laboratory confirmed, information flows up the channels to state and local health [agencies], national governments, and then to places like the WHO,” says Dr. Brownstein. “Each one of those stages can take days, weeks, or even months, and that’s the problem if you’re thinking about a virus that can spread around the world in a matter of days.”

The HealthMap team looks at a variety of communication channels to undo the existing hierarchy of health information.

“We make everyone a stakeholder when it comes to data about outbreaks, including consumers,” says Dr. Brownstein. “There are a suite of different tools that public health officials have at their disposal. What we’re trying to do is think about how to communicate and empower individuals to really understand what the risks are, what the true information is about a disease event, and what they can do to protect themselves and their families. It’s all about trying to demystify outbreaks.”

In addition to the map itself, the HealthMap team has a number of interactive tools that individuals can both use and contribute to. Dr. Brownstein hopes these resources will enable the public to care more about disease outbreaks that may be happening around them—it’s a way to put the “public” back in “public health,” he says.

“We have a app called Outbreaks Near Me that allows people to know about what disease outbreaks are happening in their neighborhood,” Dr. Brownstein says. “Flu Near You is a an app that people use to self report on symptoms; Vaccine Finder is a tool that allows people to know what vaccines are available to them and their community.”

In addition to developing their own app, the HealthMap has partnered with existing tech firms like Uber to spread the word about public health.

“We worked closely with Uber last year and actually put nurses in Uber cars and delivered vaccines to people,” Dr. Brownstein says. “The closest vaccine location might still be only a block away for people, but people are still hesitant to get it done.”…(More)”

How smartphones are solving one of China’s biggest mysteries

Curated on Posted on by Stefaan Verhulst

Ana Swanson at the Washington Post: “For decades, China has been engaged in a building boom of a scale that is hard to wrap your mind around. In the last three decades, 260 million people have moved from the countryside to Chinese cities — equivalent to around 80 percent of the population of the U.S. To make room for all of those people, the size of China’s built-up urban areas nearly quintupled between 1984 and 2010.

Much of that development has benefited people’s lives, but some has not. In a breathless rush to boost growth and development, some urban areas have built vast, unused real estate projects — China’s infamous “ghost cities.” These eerie, shining developments are complete except for one thing: people to live in them.

China’s ghost cities have sparked a lot of debate over the last few years. Some argue that the developments are evidence of the waste in top-down planning, or the result of too much cheap funding for businesses. Some blame the lack of other good places for average people to invest their money, or the desire of local officials to make a quick buck — land sales generate a lot of revenue for China’s local governments.

Others say the idea of ghost cities has been overblown. They espouse a “build it and they will come” philosophy, pointing out that, with time, some ghost cities fill up and turn into vibrant communities.

It’s been hard to evaluate these claims, since most of the research on ghost cities has been anecdotal. Even the most rigorous research methods leave a lot to be desired — for example, investment research firms sending poor junior employees out to remote locations to count how many lights are turned on in buildings at night.

Now new research from Baidu, one of China’s biggest technology companies, provides one of the first systematic looks at Chinese ghost cities. Researchers from Baidu’s Big Data Lab and Peking University in Beijing used the kind of location data gathered by mobile phones and GPS receivers to track how people moved in and out suspected ghost cities, in real time and on a national scale, over a period of six months. You can see the interactive project here.

Google has been blocked in China for years, and Baidu dominates the market in terms of search, mobile maps and other offerings. That gave the researchers a huge data base to work with —  770 million users, a hefty chunk of China’s 1.36 billion people.

To identify potential ghost cities, the researchers created an algorithm that identifies urban areas with a relatively spare population. They define a ghost city as an urban region with a population of fewer than 5,000 people per square kilometer – about half the density recommended by the Chinese Ministry of Housing and Urban-Rural Development….(More)”

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