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)”

Mobile data: Made to measure

Curated on Posted on by Stefaan Verhulst

Neil Savage in Nature: “For decades, doctors around the world have been using a simple test to measure the cardiovascular health of patients. They ask them to walk on a hard, flat surface and see how much distance they cover in six minutes. This test has been used to predict the survival rates of lung transplant candidates, to measure the progression of muscular dystrophy, and to assess overall cardiovascular fitness.

The walk test has been studied in many trials, but even the biggest rarely top a thousand participants. Yet when Euan Ashley launched a cardiovascular study in March 2015, he collected test results from 6,000 people in the first two weeks. “That’s a remarkable number,” says Ashley, a geneticist who heads Stanford University’s Center for Inherited Cardiovascular Disease. “We’re used to dealing with a few hundred patients, if we’re lucky.”

Numbers on that scale, he hopes, will tell him a lot more about the relationship between physical activity and heart health. The reason they can be achieved is that millions of people now have smartphones and fitness trackers with sensors that can record all sorts of physical activity. Health researchers are studying such devices to figure out what sort of data they can collect, how reliable those data are, and what they might learn when they analyse measurements of all sorts of day-to-day activities from many tens of thousands of people and apply big-data algorithms to the readings.

By July, more than 40,000 people in the United States had signed up to participate in Ashley’s study, which uses an iPhone application called MyHeart Counts. He expects the numbers to surge as the app becomes more widely available around the world. The study — designed by scientists, approved by institutional review boards, and requiring informed consent — asks participants to answer questions about their health and risk factors, and to use their phone’s motion sensors to collect data about their activities for seven days. They also do a six-minute walk test, and the phone measures the distance they cover. If their own doctors have ordered blood tests, users can enter information such as cholesterol or glucose measurements. Every three months, the app checks back to update their data.

Physicians know that physical activity is a strong predictor of long-term heart health, Ashley says. But it is less clear what kind of activity is best, or whether different groups of people do better with different types of exercise. MyHeart Counts may open a window on such questions. “We can start to look at subgroups and find differences,” he says.

“You can take pretty noisy data, but if you have enough of it, you can find a signal.”

It is the volume of the data that makes such studies possible. In traditional studies, there may not be enough data to find statistically significant results for such subgroups. And rare events may not occur in the smaller samples, or may produce a signal so weak that it is lost in statistical noise. Big data can overcome those problems, and if the data set is big enough, small errors can be smoothed out. “You can take pretty noisy data, but if you have enough of it, you can find a signal,” Ashley says….(More)”.

How big data and The Sims are helping us to build the cities of the future

Curated on Posted on by Stefaan Verhulst

The Next Web: “By 2050, the United Nations predicts that around 66 percent of the world’s population will be living in urban areas. It is expected that the greatest expansion will take place in developing regions such as Africa and Asia. Cities in these parts will be challenged to meet the needs of their residents, and provide sufficient housing, energy, waste disposal, healthcare, transportation, education and employment.

So, understanding how cities will grow – and how we can make them smarter and more sustainable along the way – is a high priority among researchers and governments the world over. We need to get to grips with the inner mechanisms of cities, if we’re to engineer them for the future. Fortunately, there are tools to help us do this. And even better, using them is a bit like playing SimCity….

Cities are complex systems. Increasingly, scientists studying cities have gone from thinking about “cities as machines”, to approaching “cities as organisms”. Viewing cities as complex, adaptive organisms – similar to natural systems like termite mounds or slime mould colonies – allows us to gain unique insights into their inner workings. …So, if cities are like organisms, it follows that we should examine them from the bottom-up, and seek to understand how unexpected large-scale phenomena emerge from individual-level interactions. Specifically, we can simulate how the behaviour of individual “agents” – whether they are people, households, or organisations – affect the urban environment, using a set of techniques known as “agent-based modelling”….These days, increases in computing power and the proliferation of big datagive agent-based modelling unprecedented power and scope. One of the most exciting developments is the potential to incorporate people’s thoughts and behaviours. In doing so, we can begin to model the impacts of people’s choices on present circumstances, and the future.

For example, we might want to know how changes to the road layout might affect crime rates in certain areas. By modelling the activities of individuals who might try to commit a crime, we can see how altering the urban environment influences how people move around the city, the types of houses that they become aware of, and consequently which places have the greatest risk of becoming the targets of burglary.

To fully realise the goal of simulating cities in this way, models need a huge amount of data. For example, to model the daily flow of people around a city, we need to know what kinds of things people spend their time doing, where they do them, who they do them with, and what drives their behaviour.

Without good-quality, high-resolution data, we have no way of knowing whether our models are producing realistic results. Big data could offer researchers a wealth of information to meet these twin needs. The kinds of data that are exciting urban modellers include:

  • Electronic travel cards that tell us how people move around a city.
  • Twitter messages that provide insight into what people are doing and thinking.
  • The density of mobile telephones that hint at the presence of crowds.
  • Loyalty and credit-card transactions to understand consumer behaviour.
  • Participatory mapping of hitherto unknown urban spaces, such as Open Street Map.

These data can often be refined to the level of a single person. As a result, models of urban phenomena no longer need to rely on assumptions about the population as a whole – they can be tailored to capture the diversity of a city full of individuals, who often think and behave differently from one another….(More)

Crowdsourced pollution data via smartphones

Curated on Posted on by Stefaan Verhulst

Springwise: “Citizens in eleven cities in Europe were recently recruited to help crowdsource pollution measurements, as part of the large-scale research project iSPEX-EU. Participants used their smartphones, an app and a lens called a spectropolarimeter, to collect data about air quality across the continent, which will be used by iSPEX to make comprehensive maps.

The project ran for six weeks and saw thousands of measurements taken in Athens, Barcelona, Belgrade, Berlin, Copenhagen, London, Manchester, Milan, Rome, and Toulouse. To contribute, citizens registered their interest, downloaded the free app and were sent an iSPEX lens. Then, on a clear day they placed the lens over their smartphone camera and photographed the sky in multiple directions. The app registered the location and direction of each picture and measured the light spectrum and the polarization of the light.
From the data, iSPEX are able to calculate how much fine dust — known as aerosols — there is in the atmosphere in that place and create a map showing levels of air pollution across Europe. The crowdsourced data can be used to aid government research by filling in any blank spaces and ensuring that the official data is honest.

We’ve seen attempts at similar projects before, such asSmart Citizen, but iSPEX EU benefits from the flexibility and simplicity of its tools. Smartphones have been successfully harnessed as scientific apparatus, enabling researchers to crowdsource data about issues including cancer and tree disease….(More)”

Open government: a new paradigm in social change?

Curated on Posted on by Stefaan Verhulst

Rosie Williams: In a recent speech to the Australian and New Zealand School of Government (ANSOG) annual conference, technology journalist and academic Suelette Drefyus explained the growing ‘information asymmetry’ that characterises the current-day relationship between government and citizenry.

According to Dreyfus:

‘Big Data makes government very powerful in its relationship with the citizen. This is even more so with the rise of intelligent systems, software that increasingly trawls, matches and analyses that Big Data. And it is moving toward making more decisions once made by human beings.’

The role of technology in the delivery of government services gives much food for thought in terms of both its implications for potential good and the potential dangers it may pose. The concept of open government is an important one for the future of policy and democracy in Australia. Open government has at its core a recognition that the world has changed, that the ways people engage and who they engage with has transformed in ways that governments around the world must respond to in both technological and policy terms.

As described in the ANSOG speech, the change within government in how it uses technology is well underway, however in many regards we are at the very beginning of understanding and implementing the potential of data and technology in providing solutions to many of our shared problems. Australia’s pending membership of the Open Government Partnership is integral to how Australia responds to these challenges. Membership of the multi-lateral partnership requires the Australian government to create a National Action Plan based on consultation and demonstrate our credentials in the areas of Fiscal Transparency, Access to Information, Income and Asset Disclosure, and Citizen Engagement.

What are the implications of the National Action Plan for policy consultation formulation, implementation and evaluation? In relative terms, Australia’s history with open government is fairly recent. Policies on open data have seen the roll out of data.gov.au – a repository of data published by government agencies and made available for re-use in efforts such as the author’s own financial transparency site OpenAus.

In this way citizen activity and government come together for the purposes of achieving open government. These efforts express a new paradigm in government and activism where the responsibility for solving the problems of democracy are shared between government and the people as opposed to the government ‘solving’ the problems of a passive, receptive citizenry.

As the famous whistle-blowers have shown, citizens are no longer passive but this new capability also requires a consciousness of the responsibilities and accountability that go along with the powers newly developed by citizen activists through technological change.

The opening of data and communication channels in the formulation of public policy provides a way forward to create both a better informed citizenry and also better informed policy evaluation. When new standards of transparency are applied to wicked problems what shortcomings does this highlight?

This question was tested with my recent request for a basic fact missing from relevant government research and reviews but key to social issues of homelessness and domestic violence….(More)”

New traffic app and disaster prevention technology road tested

Curated on Posted on by Stefaan Verhulst

Psych.org: “A new smartphone traffic app tested by citizens in Dublin, Ireland allows users to give feedback on traffic incidents, enabling traffic management centres to respond quicker when collisions and other incidents happen around the city. The ‘CrowdAlert’ app, which is now available for download, is one of the key components utilised in the EU-funded INSIGHT project and a good example of how smartphones and social networks can be harnessed to improve public services and safety.

‘We are witnessing an explosion in the quantity, quality, and variety of available information, fuelled in large part by advances in sensor networking, the availability of low-cost sensor-enabled devices and by the widespread adoption of powerful smart-phones,’ explains  coordinator professor Dimitrios Gunopulos from the National and Kapodistrian University of Athens. ‘These revolutionary technologies are driving the development and adoption of applications where mobile devices are used for continuous data sensing and analysis.’

The project also developed a novel citywide real-time traffic monitoring tool, the ‘INSIGHT System’, which was tested in real conditions in the Dublin City control room, along with nationwide disaster monitoring technologies. The INSIGHT system was shown to provide early warnings to experts at situation centres, enabling them to monitor situations in real-time, including disasters with potentially nation-wide impacts such as severe weather conditions, floods and subsequent knock-on events such as fires and power outages.

The project’s results will be of interest to public services, which have until now lacked the necessary infrastructure for handling and integrating miscellaneous data streams, including data from static and mobile sensors as well as information coming from social network sources, in real-time. Providing cities with the ability to manage emergency situations with enhanced capabilities will also open up new markets for network technologies….(More)”

The Human Face of Big Data

Curated on Posted on by Stefaan Verhulst

A film by Sandy Smolan [56 minutes]: “Big Data is defined as the real time collection, analyses, and visualization of vast amounts of information. In the hands of Data Scientists this raw information is fueling a revolution which many people believe may have as big an impact on humanity going forward as the Internet has over the past two decades. Its enable us to sense, measure, and understand aspects of our existence in ways never before possible.

The Human Face of Big Data captures an extraordinary revolution sweeping, almost invisibly, through business, academia, government, healthcare, and everyday life. It’s already enabling us to provide a healthier life for our children. To provide our seniors with independence while keeping them safe. To help us conserve precious resources like water and energy. To alert us to tiny changes in our health, weeks or years before we develop a life—threatening illness. To peer into our own individual genetic makeup. To create new forms of life. And soon, as many predict, to re—engineer our own species. And we’ve barely scratched the surface…

This massive gathering and analyzing of data in real time is allowing us to address some of humanities biggest challenges. Yet, as Edward Snowden and the release of the NSA documents has shown, the accessibility of all this data can come at a steep price….(More)”

New Human Need Index fills a data void to help those in need

Curated on Posted on by Stefaan Verhulst

Scott W. Allard at Brookings: “My 2009 book, “Out of Reach,” examined why it can be hard for poor families to get help from the safety net. One critical barrier is the lack of information about local program resources and nonprofit social service organizations. Good information is key to finding help, but also to important if we are to target resources effectively and assess if program investments were successful.

As I prepared data for the book in 2005, my research team struggled to compile useful information about services and programs in the three major metro areas at the center of the study. We grappled with out-of-date print directories, incomplete online listings, bad addresses, disconnected phone numbers, and inaccurate information about the availability of services. It wasn’t clear families experiencing hardship could easily find the help they needed. It also wasn’t clear how potential volunteers or donors could know where to direct their energies, or whether communities could know whether they were deploying adequate and relevant safety net resources. In the book’s conclusion, however, I was optimistic things would get better. A mix of emerging technology, big data systems, and a generation of young entrepreneurs would certainly close these information gaps over the next several years.

Recently, I embarked upon an effort to again identify the social service organizations operating in one of the book’s original study sites. To my surprise, the work was much harder this time around. Print directories are artifacts of the past. Online referral tools provided only spotty coverage. Addresses and service information can still be quite out of date. In many local communities, it felt as if there was less information available now than a decade ago.

Lack of data about local safety net programs, particularly nonprofit organizations, has long been a problem for scholars, community advocates, nonprofit leaders, and philanthropists. Data about providers and populations served are expensive to collect, update, and disseminate. There are no easy ways to monetize data resources or find regular revenue streams to support data work. There are legal obstacles and important concerns about confidentiality. Many organizations don’t have the resources to do much analytic or learning work.

The result is striking. We spend tens of billions of dollars on social services for low-income households each year, but we have only the vaguest ideas of where those dollars go, what impact they have, and where unmet needs exist.

Into this information void steps the Salvation Army and the Lilly Family School of Philanthropy at Indiana University with a possible path forward. Working together and with an advisory board of scholars, the Salvation Army and the Lilly School have created a real-time Human Needs Index drawn from service provision tracking systems maintained by more than 7,000 Salvation Army sites nationwide. The index provides useful insight into consumption of an array of emergency services (e.g., food, shelter, clothing) at a given place and point in time across the entire country…(More)”

Statistical objectivity is a cloak spun from political yarn

Curated on Posted on by Stefaan Verhulst

Angus Deaton at the Financial Times: “The word data means things that are “given”: baseline truths, not things that are manufactured, invented, tailored or spun. Especially not by politics or politicians. Yet this absolutist view can be a poor guide to using the numbers well. Statistics are far from politics-free; indeed, politics is encoded in their genes. This is ultimately a good thing.

We like to deal with facts, not factoids. We are scandalised when politicians try to censor numbers or twist them, and most statistical offices have protocols designed to prevent such abuse. Headline statistics often seem simple but typically have many moving parts. A clock has two hands and 12 numerals yet underneath there may be thousands of springs, cogs and wheels. Politics is not only about telling the time, or whether the clock is slow or fast, but also about how to design the cogs and wheels. Down in the works, even where the decisions are delegated to bureaucrats and statisticians, there is room for politics to masquerade as science. A veneer of apolitical objectivity can be an effective disguise for a political programme.

Just occasionally, however, the mask drops and the design of the cogs and wheels moves into the glare of frontline politics. Consumer price indexes are leading examples of this. Britain’s first consumer price index was based on spending patterns from 1904. Long before the second world war, these weights were grotesquely outdated. During the war, the cabinet was worried about a wage-price spiral and the Treasury committed to hold the now-irrelevant index below the astonishingly precise value of 201.5 (1914=100) through a policy of food subsidies. It would, for example, respond to an increase in the price of eggs by lowering the price of sugar. Reform of the index would have jeopardised the government’s ability to control it and was too politically risky. The index was not updated until 1947….

These examples show the role of politics needs to be understood, and built in to any careful interpretation of the data. We must always work from multiple sources, and look deep into the cogs and wheels. James Scott, the political scientist, noted that statistics are how the state sees. The state decides what it needs to see and how to see it. That politics infuses every part of this is a testament to the importance of the numbers; lives depend on what they show.

For global poverty or hunger statistics, there is no state and no one’s material wellbeing depends on them. Politicians are less likely to interfere with such data, but this also removes a vital source of monitoring and accountability. Politics is a danger to good data; but without politics data are unlikely to be good, or at least not for long….(More)”