Big Data and the Future of Privacy

Curated on Posted on by Stefaan Verhulst

John Podesta at the White House blog: “Last Friday, the President spoke to the American people, and the international community, about how to keep us safe from terrorism in a changing world while upholding America’s commitment to liberty and privacy that our values and Constitution require. Our national security challenges are real, but that is surely not the only space where changes in technology are altering the landscape and challenging conceptions of privacy.
That’s why in his speech, the President asked me to lead a comprehensive review of the way that “big data” will affect the way we live and work; the relationship between government and citizens; and how public and private sectors can spur innovation and maximize the opportunities and free flow of this information while minimizing the risks to privacy. I will be joined in this effort by Secretary of Commerce Penny Pritzker, Secretary of Energy Ernie Moniz, the President’s Science Advisor John Holdren, the President’s Economic Advisor Gene Sperling and other senior government officials.
I would like to explain a little bit more about the review, its scope, and what you can expect over the next 90 days.
We are undergoing a revolution in the way that information about our purchases, our conversations, our social networks, our movements, and even our physical identities are collected, stored, analyzed and used. The immense volume, diversity and potential value of data will have profound implications for privacy, the economy, and public policy. The working group will consider all those issues, and specifically how the present and future state of these technologies might motivate changes in our policies across a range of sectors.
When we complete our work, we expect to deliver to the President a report that anticipates future technological trends and frames the key questions that the collection, availability, and use of “big data” raise – both for our government, and the nation as a whole. It will help identify technological changes to watch, whether those technological changes are addressed by the U.S.’s current policy framework and highlight where further government action, funding, research and consideration may be required.
This is going to be a collaborative effort. The President’s Council of Advisors on Science and Technology (PCAST) will conduct a study to explore in-depth the technological dimensions of the intersection of big data and privacy, which will feed into this broader effort. Our working group will consult with industry, civil liberties groups, technologists, privacy experts, international partners, and other national and local government officials on the significance of and future for these technologies. Finally, we will be working with a number of think tanks, academic institutions, and other organizations around the country as they convene stakeholders to discuss these very issues and questions. Likewise, many abroad are analyzing and responding to the challenge and seizing the opportunity of big data. These discussions will help to inform our study.
While we don’t expect to answer all these questions, or produce a comprehensive new policy in 90 days, we expect this work to serve as the foundation for a robust and forward-looking plan of action. Check back on this blog for updates on how you can get involved in the debate and for status updates on our progress.”

Use big data and crowdsourcing to detect nuclear proliferation, says DSB

Curated on Posted on by Stefaan Verhulst

FierceGovernmentIT: “A changing set of counter-nuclear proliferation problems requires a paradigm shift in monitoring that should include big data analytics and crowdsourcing, says a report from the Defense Science Board.
Much has changed since the Cold War when it comes to ensuring that nuclear weapons are subject to international controls, meaning that monitoring in support of treaties covering declared capabilities should be only one part of overall U.S. monitoring efforts, says the board in a January report (.pdf).
There are challenges related to covert operations, such as testing calibrated to fall below detection thresholds, and non-traditional technologies that present ambiguous threat signatures. Knowledge about how to make nuclear weapons is widespread and in the hands of actors who will give the United States or its allies limited or no access….
The report recommends using a slew of technologies including radiation sensors, but also exploitation of digital sources of information.
“Data gathered from the cyber domain establishes a rich and exploitable source for determining activities of individuals, groups and organizations needed to participate in either the procurement or development of a nuclear device,” it says.
Big data analytics could be used to take advantage of the proliferation of potential data sources including commercial satellite imaging, social media and other online sources.
The report notes that the proliferation of readily available commercial satellite imagery has created concerns about the introduction of more noise than genuine signal. “On balance, however, it is the judgment from the task force that more information from remote sensing systems, both commercial and dedicated national assets, is better than less information,” it says.
In fact, the ready availability of commercial imagery should be an impetus of governmental ability to find weak signals “even within the most cluttered and noisy environments.”
Crowdsourcing also holds potential, although the report again notes that nuclear proliferation analysis by non-governmental entities “will constrain the ability of the United States to keep its options open in dealing with potential violations.” The distinction between gathering information and making political judgments “will erode.”
An effort by Georgetown University students (reported in the Washington Post in 2011) to use open source data analyzing the network of tunnels used in China to hide its missile and nuclear arsenal provides a proof-of-concept on how crowdsourcing can be used to augment limited analytical capacity, the report says – despite debate on the students’ work, which concluded that China’s arsenal could be many times larger than conventionally accepted…
For more:
– download the DSB report, “Assessment of Nuclear Monitoring and Verification Technologies” (.pdf)
– read the WaPo article on the Georgetown University crowdsourcing effort”

Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’

Curated on Posted on by Stefaan Verhulst

New Paper by Shelton, T., Poorthuis, A., Graham, M., and Zook, M. : “Digital social data are now practically ubiquitous, with increasingly large and interconnected databases leading researchers, politicians, and the private sector to focus on how such ‘big data’ can allow potentially unprecedented insights into our world. This paper investigates Twitter activity in the wake of Hurricane Sandy in order to demonstrate the complex relationship between the material world and its digital representations. Through documenting the various spatial patterns of Sandy-related tweeting both within the New York metropolitan region and across the United States, we make a series of broader conceptual and methodological interventions into the nascent geographic literature on big data. Rather than focus on how these massive databases are causing necessary and irreversible shifts in the ways that knowledge is produced, we instead find it more productive to ask how small subsets of big data, especially georeferenced social media information scraped from the internet, can reveal the geographies of a range of social processes and practices. Utilizing both qualitative and quantitative methods, we can uncover broad spatial patterns within this data, as well as understand how this data reflects the lived experiences of the people creating it. We also seek to fill a conceptual lacuna in studies of user-generated geographic information, which have often avoided any explicit theorizing of sociospatial relations, by employing Jessop et al’s TPSN framework. Through these interventions, we demonstrate that any analysis of user-generated geographic information must take into account the existence of more complex spatialities than the relatively simple spatial ontology implied by latitude and longitude coordinates.”

Safety Datapalooza Shows Power of Data.gov Communities

Curated on Posted on by Stefaan Verhulst

Lisa Nelson at DigitalGov: “The White House Office of Public Engagement held the first Safety Datapalooza illustrating the power of Data.gov communities. Federal Chief Technology Officer Todd Park and Deputy Secretary of Transportation John Porcari hosted the event, which touted the data available on Safety.Data.gov and the community of innovators using it to make effective tools for consumers.
The event showcased many of the  tools that have been produced as a result of  opening this safety data including:

  • PulsePoint, from the San Ramon Fire Protection District, a lifesaving mobile app that allows CPR-trained volunteers to be notified if someone nearby is in need of emergency assistance;
  • Commute and crime maps, from Trulia, allow home buyers to choose their new residence based on two important everyday factors; and
  • Hurricane App, from the American Red Cross, to monitor storm conditions, prepare your family and home, find help, and let others know you’re safe even if the power is out;

Safety data is far from alone in generating innovative ideas and gathering a community of developers and entrepreneurs, Data.gov currently has 16 different topically diverse communities on land and sea — the Cities and Oceans communities being two such examples. Data.gov’s communities are a virtual meeting spot for interested parties across government, academia and industry to come together and put the data to use. Data.gov enables a whole set of tools to make these communities come to life: apps, blogs, challenges, forums, ranking, rating and wikis.
For a summary of the Safety Datapalooza visit Transportation’s “Fast Lane” blog.”

New Book: Open Data Now

Curated on Posted on by Stefaan Verhulst

New book by Joel Gurin (The GovLab): “Open Data is the world’s greatest free resource–unprecedented access to thousands of databases–and it is one of the most revolutionary developments since the Information Age began. Combining two major trends–the exponential growth of digital data and the emerging culture of disclosure and transparency–Open Data gives you and your business full access to information that has never been available to the average person until now. Unlike most Big Data, Open Data is transparent, accessible, and reusable in ways that give it the power to transform business, government, and society.
Open Data Now is an essential guide to understanding all kinds of open databases–business, government, science, technology, retail, social media, and more–and using those resources to your best advantage. You’ll learn how to tap crowds for fast innovation, conduct research through open collaboration, and manage and market your business in a transparent marketplace.
Open Data is open for business–and the opportunities are as big and boundless as the Internet itself. This powerful, practical book shows you how to harness the power of Open Data in a variety of applications:

  • HOT STARTUPS: turn government data into profitable ventures
  • SAVVY MARKETING: understand how reputational data drives your brand
  • DATA-DRIVEN INVESTING: apply new tools for business analysis
  • CONSUMER IN FORMATION: connect with your customers using smart disclosure
  • GREEN BUSINESS: use data to bet on sustainable companies
  • FAST R&D: turn the online world into your research lab
  • NEW OPPORTUNITIES: explore open fields for new businesses

Whether you’re a marketing professional who wants to stay on top of what’s trending, a budding entrepreneur with a billion-dollar idea and limited resources, or a struggling business owner trying to stay competitive in a changing global market–or if you just want to understand the cutting edge of information technology–Open Data Now offers a wealth of big ideas, strategies, and techniques that wouldn’t have been possible before Open Data leveled the playing field.
The revolution is here and it’s now. It’s Open Data Now.”

Why the Nate Silvers of the World Don’t Know Everything

Curated on Posted on by Stefaan Verhulst

Felix Salmon in Wired: “This shift in US intelligence mirrors a definite pattern of the past 30 years, one that we can see across fields and institutions. It’s the rise of the quants—that is, the ascent to power of people whose native tongue is numbers and algorithms and systems rather than personal relationships or human intuition. Michael Lewis’ Moneyball vividly recounts how the quants took over baseball, as statistical analy­sis trumped traditional scouting and propelled the underfunded Oakland A’s to a division-winning 2002 season. More recently we’ve seen the rise of the quants in politics. Commentators who “trusted their gut” about Mitt Romney’s chances had their gut kicked by Nate Silver, the stats whiz who called the election days before­hand as a lock for Obama, down to the very last electoral vote in the very last state.
The reason the quants win is that they’re almost always right—at least at first. They find numerical patterns or invent ingenious algorithms that increase profits or solve problems in ways that no amount of subjective experience can match. But what happens after the quants win is not always the data-driven paradise that they and their boosters expected. The more a field is run by a system, the more that system creates incentives for everyone (employees, customers, competitors) to change their behavior in perverse ways—providing more of whatever the system is designed to measure and produce, whether that actually creates any value or not. It’s a problem that can’t be solved until the quants learn a little bit from the old-fashioned ways of thinking they’ve displaced.
No matter the discipline or industry, the rise of the quants tends to happen in four stages. Stage one is what you might call pre-disruption, and it’s generally best visible in hindsight. Think about quaint dating agencies in the days before the arrival of Match .com and all the other algorithm-powered online replacements. Or think about retail in the era before floor-space management analytics helped quantify exactly which goods ought to go where. For a live example, consider Hollywood, which, for all the money it spends on market research, is still run by a small group of lavishly compensated studio executives, all of whom are well aware that the first rule of Hollywood, as memorably summed up by screenwriter William Goldman, is “Nobody knows anything.” On its face, Hollywood is ripe for quantifi­cation—there’s a huge amount of data to be mined, considering that every movie and TV show can be classified along hundreds of different axes, from stars to genre to running time, and they can all be correlated to box office receipts and other measures of profitability.
Next comes stage two, disruption. In most industries, the rise of the quants is a recent phenomenon, but in the world of finance it began back in the 1980s. The unmistakable sign of this change was hard to miss: the point at which you started getting targeted and personalized offers for credit cards and other financial services based not on the relationship you had with your local bank manager but on what the bank’s algorithms deduced about your finances and creditworthiness. Pretty soon, when you went into a branch to inquire about a loan, all they could do was punch numbers into a computer and then give you the computer’s answer.
For a present-day example of disruption, think about politics. In the 2012 election, Obama’s old-fashioned campaign operatives didn’t disappear. But they gave money and freedom to a core group of technologists in Chicago—including Harper Reed, former CTO of the Chicago-based online retailer Threadless—and allowed them to make huge decisions about fund-raising and voter targeting. Whereas earlier campaigns had tried to target segments of the population defined by geography or demographic profile, Obama’s team made the campaign granular right down to the individual level. So if a mom in Cedar Rapids was on the fence about who to vote for, or whether to vote at all, then instead of buying yet another TV ad, the Obama campaign would message one of her Facebook friends and try the much more effective personal approach…
After disruption, though, there comes at least some version of stage three: over­shoot. The most common problem is that all these new systems—metrics, algo­rithms, automated decisionmaking processes—result in humans gaming the system in rational but often unpredictable ways. Sociologist Donald T. Campbell noted this dynamic back in the ’70s, when he articulated what’s come to be known as Campbell’s law: “The more any quantitative social indicator is used for social decision-making,” he wrote, “the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”…
Policing is a good example, as explained by Harvard sociologist Peter Moskos in his book Cop in the Hood: My Year Policing Baltimore’s Eastern District. Most cops have a pretty good idea of what they should be doing, if their goal is public safety: reducing crime, locking up kingpins, confiscating drugs. It involves foot patrols, deep investigations, and building good relations with the community. But under statistically driven regimes, individual officers have almost no incentive to actually do that stuff. Instead, they’re all too often judged on results—specifically, arrests. (Not even convictions, just arrests: If a suspect throws away his drugs while fleeing police, the police will chase and arrest him just to get the arrest, even when they know there’s no chance of a conviction.)…
It’s increasingly clear that for smart organizations, living by numbers alone simply won’t work. That’s why they arrive at stage four: synthesis—the practice of marrying quantitative insights with old-fashioned subjective experience. Nate Silver himself has written thoughtfully about examples of this in his book, The Signal and the Noise. He cites baseball, which in the post-Moneyball era adopted a “fusion approach” that leans on both statistics and scouting. Silver credits it with delivering the Boston Red Sox’s first World Series title in 86 years. Or consider weather forecasting: The National Weather Service employs meteorologists who, understanding the dynamics of weather systems, can improve forecasts by as much as 25 percent compared with computers alone. A similar synthesis holds in eco­nomic forecasting: Adding human judgment to statistical methods makes results roughly 15 percent more accurate. And it’s even true in chess: While the best computers can now easily beat the best humans, they can in turn be beaten by humans aided by computers….
That’s what a good synthesis of big data and human intuition tends to look like. As long as the humans are in control, and understand what it is they’re controlling, we’re fine. It’s when they become slaves to the numbers that trouble breaks out. So let’s celebrate the value of disruption by data—but let’s not forget that data isn’t everything.

The Postmodernity of Big Data

Curated on Posted on by Stefaan Verhulst

Essay by in the New Inquiry: “Big Data fascinates because its presence has always been with us in nature. Each tree, drop of rain, and the path of each grain of sand, both responds to and creates millions of data points, even on a short journey. Nature is the original algorithm, the most efficient and powerful. Mathematicians since the ancients have looked to it for inspiration; techno-capitalists now look to unlock its mysteries for private gain. Playing God has become all the more brisk and profitable thanks to cloud computing.
But beyond economic motivations for Big Data’s rise, are there also epistemological ones? Has Big Data come to try to fill the vacuum of certainty left by postmodernism? Does data science address the insecurities of the postmodern thought?
It turns out that trying to explain Big Data is like trying to explain postmodernism. Neither can be summarized effectively in a phrase, despite their champions’ efforts. Broad epistemological developments are compressed into cursory, ex post facto descriptions. Attempts to define Big Data, such as IBM’s marketing copy, which promises “insights gleaned” from “enterprise data warehouses that implement massively parallel processing,” “real-time scalability” and “parsing structured and unstructured sources,” focus on its implementation at the expense of its substance, decontextualizing it entirely . Similarly, definitions of postmodernism, like art critic Thomas McEvilley’s claim that it is “a renunciation that involves recognition of the relativity of the self—of one’s habit systems, their tininess, silliness, and arbitrariness” are accurate but abstract to the point of vagueness….
Big Data might come to be understood as Big Postmodernism: the period in which the influx of unstructured, non-teleological, non-narrative inputs ceased to destabilize the existing order but was instead finally mastered processed by sufficiently complex, distributed, and pluralized algorithmic regime. If Big Data has a skepticism built in, how this is different from the skepticism of postmodernism is perhaps impossible to yet comprehend”.

Big Data Becomes a Mirror

Curated on Posted on by Stefaan Verhulst

Book Review of ‘Uncharted,’ by Erez Aiden and Jean-Baptiste Michel in the New York Times: “Why do English speakers say “drove” rather than “drived”?

As graduate students at the Harvard Program for Evolutionary Dynamics about eight years ago, Erez Aiden and Jean-Baptiste Michel pondered the matter and decided that something like natural selection might be at work. In English, the “-ed” past-tense ending of Proto-Germanic, like a superior life form, drove out the Proto-Indo-European system of indicating tenses by vowel changes. Only the small class of verbs we know as irregular managed to resist.

To test this evolutionary premise, Mr. Aiden and Mr. Michel wound up inventing something they call culturomics, the use of huge amounts of digital information to track changes in language, culture and history. Their quest is the subject of “Uncharted: Big Data as a Lens on Human Culture,” an entertaining tour of the authors’ big-data adventure, whose implications they wildly oversell….

Invigorated by the great verb chase, Mr. Aiden and Mr. Michel went hunting for bigger game. Given a large enough storehouse of words and a fine filter, would it be possible to see cultural change at the micro level, to follow minute fluctuations in human thought processes and activities? Tiny factoids, multiplied endlessly, might assume imposing dimensions.

By chance, Google Books, the megaproject to digitize every page of every book ever printed — all 130 million of them — was starting to roll just as the authors were looking for their next target of inquiry.

Meetings were held, deals were struck and the authors got to it. In 2010, working with Google, they perfected the Ngram Viewer, which takes its name from the computer-science term for a word or phrase. This “robot historian,” as they call it, can search the 30 million volumes already digitized by Google Books and instantly generate a usage-frequency timeline for any word, phrase, date or name, a sort of stock-market graph illustrating the ups and downs of cultural shares over time.

Mr. Aiden, now director of the Center for Genome Architecture at Rice University, and Mr. Michel, who went on to start the data-science company Quantified Labs, play the Ngram Viewer (books.google.com/ngrams) like a Wurlitzer…

The Ngram Viewer delivers the what and the when but not the why. Take the case of specific years. All years get attention as they approach, peak when they arrive, then taper off as succeeding years occupy the attention of the public. Mentions of the year 1872 had declined by half in 1896, a slow fade that took 23 years. The year 1973 completed the same trajectory in less than half the time.

“What caused that change?” the authors ask. “We don’t know. For now, all we have are the naked correlations: what we uncover when we look at collective memory through the digital lens of our new scope.” Someone else is going to have to do the heavy lifting.”

Are Smart Cities Empty Hype?

Curated on Posted on by Stefaan Verhulst

Irving Wladawsky-Berger in the Wall Street Journal: “A couple of weeks ago I participated in an online debate sponsored by The Economist around the question: Are Smart Cities Empty Hype? Defending the motion was Anthony Townsend, research director at the Institute for the Future and adjunct faculty member at NYU’s Wagner School of Public Service. I took the opposite side, arguing the case against the motion.
The debate consisted of three phases spread out over roughly 10 days. We each first stated our respective positions in our opening statements, followed a few days later by our rebuttals, and then finally our closing statements.  It was moderated by Ludwig Siegele, online business and finance editor at The Economist. Throughout the process, people were invited to vote on the motion, as well as to post their own comments.
The debate was inspired, I believe, by The Multiplexed Metropolis, an article Mr. Siegele published in the September 7 issue of The Economist which explored the impact of Big Data on cities. He wrote that the vast amounts of data generated by the many social interactions taking place in cities might lead to a kind of second electrification, transforming 21st century cities much as electricity did in the past. “Enthusiasts think that data services can change cities in this century as much as electricity did in the last one,” he noted. “They are a long way from proving their case.”
In my opening statement, I said that I strongly believe that digital technologies and the many data services they are enabling will make cities smarter and help transform them over time. My position is not surprising, given my affiliations with NYU’s Center for Urban Science and Progress (CUSP) and Imperial College’s Digital City Exchange, as well as my past involvements with IBM’s Smarter Cities and with Citigroup’s Citi for Cities initiatives. But, I totally understand why so many– almost half of those voting and quite a few who left comments–feel that smart cities are mostly hype. The case for smart cities is indeed far from proven.
Cities are the most complex social organisms created by humans. Just about every aspect of human endeavor is part of the mix of cities, and they all interact with each other leading to a highly dynamic system of systems. Moreover, each city has its own unique style and character. As is generally the case with transformative changes to highly complex systems, the evolution toward smart cities will likely take quite a bit longer than we anticipate, but the eventual impact will probably be more transformative than we can currently envision.
Electrification, for example, started in the U.S., Britain and other advanced nations around the 1880s and took decades to deploy and truly transform cities. The hype around smart cities that I worry the most about is underestimating their complexity and the amount of research, experimentation, and plain hard work that it will take to realize the promise. Smart cities projects are still in their very early stages. Some will work and some will fail. We have much to learn. Highly complex systems need time to evolve.
Commenting on the opening statements, Mr. Siegele noted: “Despite the motion being Are smart cities empty hype?, both sides have focused on whether these should be implemented top-down or bottom-up. Most will probably agree that digital technology can make cities smarter–meaning more liveable, more efficient, more sustainable and perhaps even more democratic.  But the big question is how to get there and how smart cities will be governed.”…

Selected Readings on Data Visualization

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data visualization was originally published in 2013.

Data visualization is a response to the ever-increasing amount of  information in the world. With big data, informatics and predictive analytics, we have an unprecedented opportunity to revolutionize policy-making. Yet data by itself can be overwhelming. New tools and techniques for visualizing information can help policymakers clearly articulate insights drawn from data. Moreover, the rise of open data is enabling those outside of government to create informative and visually arresting representations of public information that can be used to support decision-making by those inside or outside governing institutions.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Duke, D.J., K.W. Brodlie, D.A. Duce and I. Herman. “Do You See What I Mean? [Data Visualization].” IEEE Computer Graphics and Applications 25, no. 3 (2005): 6–9. http://bit.ly/1aeU6yA.

  • In this paper, the authors argue that a more systematic ontology for data visualization to ensure the successful communication of meaning. “Visualization begins when someone has data that they wish to explore and interpret; the data are encoded as input to a visualization system, which may in its turn interact with other systems to produce a representation. This is communicated back to the user(s), who have to assess this against their goals and knowledge, possibly leading to further cycles of activity. Each phase of this process involves communication between two parties. For this to succeed, those parties must share a common language with an agreed meaning.”
  • That authors “believe that now is the right time to consider an ontology for visualization,” and “as visualization move from just a private enterprise involving data and tools owned by a research team into a public activity using shared data repositories, computational grids, and distributed collaboration…[m]eaning becomes a shared responsibility and resource. Through the Semantic Web, there is both the means and motivation to develop a shared picture of what we see when we turn and look within our own field.”

Friendly, Michael. “A Brief History of Data Visualization.” In Handbook of Data Visualization, 15–56. Springer Handbooks Comp.Statistics. Springer Berlin Heidelberg, 2008. http://bit.ly/17fM1e9.

  • In this paper, Friendly explores the “deep roots” of modern data visualization. “These roots reach into the histories of the earliest map making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.”
  • Just as the general the visualization of data is far from a new practice, Friendly shows that the graphical representation of government information has a similarly long history. “The collection, organization and dissemination of official government statistics on population, trade and commerce, social, moral and political issues became widespread in most of the countries of Europe from about 1825 to 1870. Reports containing data graphics were published with some regularity in France, Germany, Hungary and Finland, and with tabular displays in Sweden, Holland, Italy and elsewhere.”

Graves, Alvaro and James Hendler. “Visualization Tools for Open Government Data.” In Proceedings of the 14th Annual International Conference on Digital Government Research, 136–145. Dg.o ’13. New York, NY, USA: ACM, 2013. http://bit.ly/1eNSoXQ.

  • In this paper, the authors argue that, “there is a gap between current Open Data initiatives and an important part of the stakeholders of the Open Government Data Ecosystem.” As it stands, “there is an important portion of the population who could benefit from the use of OGD but who cannot do so because they cannot perform the essential operations needed to collect, process, merge, and make sense of the data. The reasons behind these problems are multiple, the most critical one being a fundamental lack of expertise and technical knowledge. We propose the use of visualizations to alleviate this situation. Visualizations provide a simple mechanism to understand and communicate large amounts of data.”
  • The authors also describe a prototype of a tool to create visualizations based on OGD with the following capabilities:
    • Facilitate visualization creation
    • Exploratory mechanisms
    • Viralization and sharing
    • Repurpose of visualizations

Hidalgo, César A. “Graphical Statistical Methods for the Representation of the Human Development Index and Its Components.” United Nations Development Programme Human Development Reports, September 2010. http://bit.ly/166TKur.

  • In this paper for the United Nations Human Development Programme, Hidalgo argues that “graphical statistical methods could be used to help communicate complex data and concepts through universal cognitive channels that are heretofore underused in the development literature.”
  • To support his argument, representations are provided that “show how graphical methods can be used to (i) compare changes in the level of development experienced by countries (ii) make it easier to understand how these changes are tied to each one of the components of the Human Development Index (iii) understand the evolution of the distribution of countries according to HDI and its components and (iv) teach and create awareness about human development by using iconographic representations that can be used to graphically narrate the story of countries and regions.”

Stowers, Genie. “The Use of Data Visualization in Government.” IBM Center for The Business of Government, Using Technology Series, 2013. http://bit.ly/1aame9K.

  • This report seeks “to help public sector managers understand one of the more important areas of data analysis today — data visualization. Data visualizations are more sophisticated, fuller graphic designs than the traditional spreadsheet charts, usually with more than two variables and, typically, incorporating interactive features.”
  • Stowers also offers numerous examples of “visualizations that include geographical and health data, or population and time data, or financial data represented in both absolute and relative terms — and each communicates more than simply the data that underpin it. In addition to these many examples of visualizations, the report discusses the history of this technique, and describes tools that can be used to create visualizations from many different kinds of data sets.”