Emerging Technology From the arXiv: “Who are the most influential spreaders of information on a network? That’s a question that marketers, bloggers, news services and even governments would like answered. Not least because the answer could provide ways to promote products quickly, to boost the popularity of political parties above their rivals and to seed the rapid spread of news and opinions.
So it’s not surprising that network theorists have spent some time thinking about how best to identify these people and to check how the information they receive might spread around a network. Indeed, they’ve found a number of measures that spot so-called superspreaders, people who spread information, ideas or even disease more efficiently than anybody else.
But there’s a problem. Social networks are so complex that network scientists have never been able to test their ideas in the real world—it has always been too difficult to reconstruct the exact structure of Twitter or Facebook networks, for example. Instead, they’ve created models that mimic real networks in certain ways and tested their ideas on these instead.
But there is growing evidence that information does not spread through real networks in the same way as it does through these idealised ones. People tend to pass on information only when they are interested in a topic and when they are active, factors that are hard to take into account in a purely topological model of a network.
So the question of how to find the superspreaders remains open. That looks set to change thanks to the work of Sen Pei at Beihang University in Beijing and a few pals who have performed the first study of superspreaders on real networks.
These guys have studied the way information flows around various networks ranging from the Livejournal blogging network to the network of scientific publishing at the American Physical Society’s, as well as on subsets of the Twitter and Facebook networks. And they’ve discovered the key indicator that identifies superspreaders in these networks.
In the past, network scientists have developed a number of mathematical tests to measure the influence that individuals have on the spread of information through a network. For example, one measure is simply the number of connections a person has to other people in the network, a property known as their degree. The thinking is that the most highly connected people are the best at spreading information.
Another measure uses the famous PageRank algorithm that Google developed for ranking webpages. This works by ranking somebody more highly if they are connected to other highly ranked people.
Then there is ‘betweenness centrality’ , a measure of how many of the shortest paths across a network pass through a specific individual. The idea is that these people are more able to inject information into the network.
And finally there is a property of nodes in a network known as their k-core. This is determined by iteratively pruning the peripheries of a network to see what is left. The k-core is the step at which that node or person is pruned from the network. Obviously, the most highly connected survive this process the longest and have the highest k-core score..
The question that Sen and co set out to answer was which of these measures best picked out superspreaders of information in real networks.
They began with LiveJournal, a network of blogs in which individuals maintain lists of friends that represent social ties to other LiveJournal users. This network allows people to repost information from other blogs and to use a reference the links back to the original post. This allows Sen and co to recreate not only the network of social links between LiveJournal users but also the way in which information is spread between them.
Sen and co collected all of the blog posts from February 2010 to November 2011, a total of more than 56 million posts. Of these, some 600,000 contain links to other posts published by LiveJournal users.
The data reveals two important properties of information diffusion. First, only some 250,000 users are actively involved in spreading information. That’s a small fraction of the total.
More significantly, they found that information did not always diffuse across the social network. The found that information could spread between two LiveJournal users even though they have no social connection.
That’s probably because they find this information outside of the LiveJournal ecosystem, perhaps through web searches or via other networks. “Only 31.93% of the spreading posts can be attributed to the observable social links,” they say.
That’s in stark contrast to the assumptions behind many social network models. These simulate the way information flows by assuming that it travels directly through the network from one person to another, like a disease spread by physical contact.
The work of Sen and co suggests that influences outside the network are crucial too. In practice, information often spreads via several seemingly independent sources within the network at the same time. This has important implications for the way superspreaders can be spotted.
Sen and co say that a person’s degree– the number of other people he or her are connected to– is not as good a predictor of information diffusion as theorists have thought. “We find that the degree of the user is not a reliable predictor of influence in all circumstances,” they say.
What’s more, the Pagerank algorithm is often ineffective in this kind of network as well. “Contrary to common belief, although PageRank is effective in ranking web pages, there are many situations where it fails to locate superspreaders of information in reality,” they say….
Ref: arxiv.org/abs/1405.1790 : Searching For Superspreaders Of Information In Real-World Social Media”
Can Big Data Stop Wars Before They Happen?
Foreign Policy: “It has been almost two decades exactly since conflict prevention shot to the top of the peace-building agenda, as large-scale killings shifted from interstate wars to intrastate and intergroup conflicts. What could we have done to anticipate and prevent the 100 days of genocidal killing in Rwanda that began in April 1994 or the massacre of thousands of Bosnian Muslims at Srebrenica just over a year later? The international community recognized that conflict prevention could no longer be limited to diplomatic and military initiatives, but that it also requires earlier intervention to address the causes of violence between nonstate actors, including tribal, religious, economic, and resource-based tensions.
For years, even as it was pursued as doggedly as personnel and funding allowed, early intervention remained elusive, a kind of Holy Grail for peace-builders. This might finally be changing. The rise of data on social dynamics and what people think and feel — obtained through social media, SMS questionnaires, increasingly comprehensive satellite information, news-scraping apps, and more — has given the peace-building field hope of harnessing a new vision of the world. But to cash in on that hope, we first need to figure out how to understand all the numbers and charts and figures now available to us. Only then can we expect to predict and prevent events like the recent massacres in South Sudan or the ongoing violence in the Central African Republic.
A growing number of initiatives have tried to make it across the bridge between data and understanding. They’ve ranged from small nonprofit shops of a few people to massive government-funded institutions, and they’ve been moving forward in fits and starts. Few of these initiatives have been successful in documenting incidents of violence actually averted or stopped. Sometimes that’s simply because violence or absence of it isn’t verifiable. The growing literature on big data and conflict prevention today is replete with caveats about “overpromising and underdelivering” and the persistent gap between early warning and early action. In the case of the Conflict Early Warning and Response Mechanism (CEWARN) system in central Africa — one of the earlier and most prominent attempts at early intervention — it is widely accepted that the project largely failed to use the data it retrieved for effective conflict management. It relied heavily on technology to produce large databases, while lacking the personnel to effectively analyze them or take meaningful early action.
To be sure, disappointments are to be expected when breaking new ground. But they don’t have to continue forever. This pioneering work demands not just data and technology expertise. Also critical is cross-discipline collaboration between the data experts and the conflict experts, who know intimately the social, political, and geographic terrain of different locations. What was once a clash of cultures over the value and meaning of metrics when it comes to complex human dynamics needs to morph into collaboration. This is still pretty rare, but if the past decade’s innovations are any prologue, we are hopefully headed in the right direction.
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Over the last three years, the U.S. Defense Department, the United Nations, and the CIA have all launched programs to parse the masses of public data now available, scraping and analyzing details from social media, blogs, market data, and myriad other sources to achieve variations of the same goal: anticipating when and where conflict might arise. The Defense Department’s Information Volume and Velocity program is designed to use “pattern recognition to detect trends in a sea of unstructured data” that would point to growing instability. The U.N.’s Global Pulse initiative’s stated goal is to track “human well-being and emerging vulnerabilities in real-time, in order to better protect populations from shocks.” The Open Source Indicators program at the CIA’s Intelligence Advanced Research Projects Activity aims to anticipate “political crises, disease outbreaks, economic instability, resource shortages, and natural disasters.” Each looks to the growing stream of public data to detect significant population-level changes.
Large institutions with deep pockets have always been at the forefront of efforts in the international security field to design systems for improving data-driven decision-making. They’ve followed the lead of large private-sector organizations where data and analytics rose to the top of the corporate agenda. (In that sector, the data revolution is promising “to transform the way many companies do business, delivering performance improvements not seen since the redesign of core processes in the 1990s,” as David Court, a director at consulting firm McKinsey, has put it.)
What really defines the recent data revolution in peace-building, however, is that it is transcending size and resource limitations. It is finding its way to small organizations operating at local levels and using knowledge and subject experts to parse information from the ground. It is transforming the way peace-builders do business, delivering data-led programs and evidence-based decision-making not seen since the field’s inception in the latter half of the 20th century.
One of the most famous recent examples is the 2013 Kenyan presidential election.
In March 2013, the world was watching and waiting to see whether the vote would produce more of the violence that had left at least 1,300 people dead and 600,000 homeless during and after 2010 elections. In the intervening years, a web of NGOs worked to set up early-warning and early-response mechanisms to defuse tribal rivalries, party passions, and rumor-mongering. Many of the projects were technology-based initiatives trying to leverage data sources in new ways — including a collaborative effort spearheaded and facilitated by a Kenyan nonprofit called Ushahidi (“witness” in Swahili) that designs open-source data collection and mapping software. The Umati (meaning “crowd”) project used an Ushahidi program to monitor media reports, tweets, and blog posts to detect rising tensions, frustration, calls to violence, and hate speech — and then sorted and categorized it all on one central platform. The information fed into election-monitoring maps built by the Ushahidi team, while mobile-phone provider Safaricom donated 50 million text messages to a local peace-building organization, Sisi ni Amani (“We are Peace”), so that it could act on the information by sending texts — which had been used to incite and fuel violence during the 2007 elections — aimed at preventing violence and quelling rumors.
The first challenges came around 10 a.m. on the opening day of voting. “Rowdy youth overpowered police at a polling station in Dandora Phase 4,” one of the informal settlements in Nairobi that had been a site of violence in 2007, wrote Neelam Verjee, programs manager at Sisi ni Amani. The young men were blocking others from voting, and “the situation was tense.”
Sisi ni Amani sent a text blast to its subscribers: “When we maintain peace, we will have joy & be happy to spend time with friends & family but violence spoils all these good things. Tudumishe amani [“Maintain the peace”] Phase 4.” Meanwhile, security officers, who had been called separately, arrived at the scene and took control of the polling station. Voting resumed with little violence. According to interviews collected by Sisi ni Amani after the vote, the message “was sent at the right time” and “helped to calm down the situation.”
In many ways, Kenya’s experience is the story of peace-building today: Data is changing the way professionals in the field think about anticipating events, planning interventions, and assessing what worked and what didn’t. But it also underscores the possibility that we might be edging closer to a time when peace-builders at every level and in all sectors — international, state, and local, governmental and not — will have mechanisms both to know about brewing violence and to save lives by acting on that knowledge.
Three important trends underlie the optimism. The first is the sheer amount of data that we’re generating. In 2012, humans plugged into digital devices managed to generate more data in a single year than over the course of world history — and that rate more than doubles every year. As of 2012, 2.4 billion people — 34 percent of the world’s population — had a direct Internet connection. The growth is most stunning in regions like the Middle East and Africa where conflict abounds; access has grown 2,634 percent and 3,607 percent, respectively, in the last decade.
The growth of mobile-phone subscriptions, which allow their owners to be part of new data sources without a direct Internet connection, is also staggering. In 2013, there were almost as many cell-phone subscriptions in the world as there were people. In Africa, there were 63 subscriptions per 100 people, and there were 105 per 100 people in the Arab states.
The second trend has to do with our expanded capacity to collect and crunch data. Not only do we have more computing power enabling us to produce enormous new data sets — such as the Global Database of Events, Language, and Tone (GDELT) project, which tracks almost 300 million conflict-relevant events reported in the media between 1979 and today — but we are also developing more-sophisticated methodological approaches to using these data as raw material for conflict prediction. New machine-learning methodologies, which use algorithms to make predictions (like a spam filter, but much, much more advanced), can provide “substantial improvements in accuracy and performance” in anticipating violent outbreaks, according to Chris Perry, a data scientist at the International Peace Institute.
This brings us to the third trend: the nature of the data itself. When it comes to conflict prevention and peace-building, progress is not simply a question of “more” data, but also different data. For the first time, digital media — user-generated content and online social networks in particular — tell us not just what is going on, but also what people think about the things that are going on. Excitement in the peace-building field centers on the possibility that we can tap into data sets to understand, and preempt, the human sentiment that underlies violent conflict.
Realizing the full potential of these three trends means figuring out how to distinguish between the information, which abounds, and the insights, which are actionable. It is a distinction that is especially hard to make because it requires cross-discipline expertise that combines the wherewithal of data scientists with that of social scientists and the knowledge of technologists with the insights of conflict experts.
United States federal government use of crowdsourcing grows six-fold since 2011
at E Pluribus Unum: “Citizensourcing and open innovation can work in the public sector, just as crowdsourcing can in the private sector. Around the world, the use of prizes to spur innovation has been booming for years. The United States of America has been significantly scaling up its use of prizes and challenges to solving grand national challenges since January 2011, when, President Obama signed an updated version of the America COMPETES Act into law.
According to the third congressionally mandated report released by the Obama administration today (PDF/Text), the number of prizes and challenges conducted under the America COMPETES Act has increased by 50% since 2012, 85% since 2012, and nearly six-fold overall since 2011. 25 different federal agencies offered prizes under COMPETES in fiscal year 2013, with 87 prize competitions in total. The size of the prize purses has also grown as well, with 11 challenges over $100,000 in 2013. Nearly half of the prizes conducted in FY 2013 were focused on software, including applications, data visualization tools, and predictive algorithms. Challenge.gov, the award-winning online platform for crowdsourcing national challenges, now has tens of thousands of users who have participated in more than 300 public-sector prize competitions. Beyond the growth in prize numbers and amounts, Obama administration highlighted 4 trends in public-sector prize competitions:
- New models for public engagement and community building during competitions
- Growth software and information technology challenges, with nearly 50% of the total prizes in this category
- More emphasis on sustainability and “creating a post-competition path to success”
- Increased focus on identifying novel approaches to solving problems
The growth of open innovation in and by the public sector was directly enabled by Congress and the White House, working together for the common good. Congress reauthorized COMPETES in 2010 with an amendment to Section 105 of the act that added a Section 24 on “Prize Competitions,” providing all agencies with the authority to conduct prizes and challenges that only NASA and DARPA has previously enjoyed, and the White House Office of Science and Technology Policy (OSTP), which has been guiding its implementation and providing guidance on the use of challenges and prizes to promote open government.
“This progress is due to important steps that the Obama Administration has taken to make prizes a standard tool in every agency’s toolbox,” wrote Cristin Dorgelo, assistant director for grand challenges in OSTP, in a WhiteHouse.gov blog post on engaging citizen solvers with prizes:
In his September 2009 Strategy for American Innovation, President Obama called on all Federal agencies to increase their use of prizes to address some of our Nation’s most pressing challenges. Those efforts have expanded since the signing of the America COMPETES Reauthorization Act of 2010, which provided all agencies with expanded authority to pursue ambitious prizes with robust incentives.
To support these ongoing efforts, OSTP and the General Services Administration have trained over 1,200 agency staff through workshops, online resources, and an active community of practice. And NASA’s Center of Excellence for Collaborative Innovation (COECI) provides a full suite of prize implementation services, allowing agencies to experiment with these new methods before standing up their own capabilities.
Sun Microsystems co-founder Bill Joy famously once said that “No matter who you are, most of the smartest people work for someone else.” This rings true, in and outside of government. The idea of governments using prizes like this to inspire technological innovation, however, is not reliant on Web services and social media, born from the fertile mind of a Silicon Valley entrepreneur. As the introduction to the third White House prize report notes:
“One of the most famous scientific achievements in nautical history was spurred by a grand challenge issued in the 18th Century. The issue of safe, long distance sea travel in the Age of Sail was of such great importance that the British government offered a cash award of £20,000 pounds to anyone who could invent a way of precisely determining a ship’s longitude. The Longitude Prize, enacted by the British Parliament in 1714, would be worth some £30 million pounds today, but even by that measure the value of the marine chronometer invented by British clockmaker John Harrison might be a deal.”
Centuries later, the Internet, World Wide Web, mobile devices and social media offer the best platforms in history for this kind of approach to solving grand challenges and catalyzing civic innovation, helping public officials and businesses find new ways to solve old problem. When a new idea, technology or methodology that challenges and improves upon existing processes and systems, it can improve the lives of citizens or the function of the society that they live within….”
Thanks-for-Ungluing launches!
Blog from Unglue.it: “Great books deserve to be read by all of us, and we ought to be supporting the people who create these books. “Thanks for Ungluing” gives readers, authors, libraries and publishers a new way to build, sustain, and nourish the books we love.
“Thanks for Ungluing” books are Creative Commons licensed and free to download. You don’t need to register or anything. But when you download, the creators can ask for your support. You can pay what you want. You can just scroll down and download the book. But when that book has become your friend, your advisor, your confidante, you’ll probably want to show your support and tell all your friends.
We have some amazing creators participating in this launch….”
Findings of the Big Data and Privacy Working Group Review
John Podesta at the White House Blog: “Over the past several days, severe storms have battered Arkansas, Oklahoma, Mississippi and other states. Dozens of people have been killed and entire neighborhoods turned to rubble and debris as tornadoes have touched down across the region. Natural disasters like these present a host of challenges for first responders. How many people are affected, injured, or dead? Where can they find food, shelter, and medical attention? What critical infrastructure might have been damaged?
Drawing on open government data sources, including Census demographics and NOAA weather data, along with their own demographic databases, Esri, a geospatial technology company, has created a real-time map showing where the twisters have been spotted and how the storm systems are moving. They have also used these data to show how many people live in the affected area, and summarize potential impacts from the storms. It’s a powerful tool for emergency services and communities. And it’s driven by big data technology.
In January, President Obama asked me to lead a wide-ranging review of “big data” and privacy—to explore how these technologies are changing our economy, our government, and our society, and to consider their implications for our personal privacy. Together with Secretary of Commerce Penny Pritzker, Secretary of Energy Ernest Moniz, the President’s Science Advisor John Holdren, the President’s Economic Advisor Jeff Zients, and other senior officials, our review sought to understand what is genuinely new and different about big data and to consider how best to encourage the potential of these technologies while minimizing risks to privacy and core American values.
Over the course of 90 days, we met with academic researchers and privacy advocates, with regulators and the technology industry, with advertisers and civil rights groups. The President’s Council of Advisors for Science and Technology conducted a parallel study of the technological trends underpinning big data. The White House Office of Science and Technology Policy jointly organized three university conferences at MIT, NYU, and U.C. Berkeley. We issued a formal Request for Information seeking public comment, and hosted a survey to generate even more public input.
Today, we presented our findings to the President. We knew better than to try to answer every question about big data in three months. But we are able to draw important conclusions and make concrete recommendations for Administration attention and policy development in a few key areas.
There are a few technological trends that bear drawing out. The declining cost of collection, storage, and processing of data, combined with new sources of data like sensors, cameras, and geospatial technologies, mean that we live in a world of near-ubiquitous data collection. All this data is being crunched at a speed that is increasingly approaching real-time, meaning that big data algorithms could soon have immediate effects on decisions being made about our lives.
The big data revolution presents incredible opportunities in virtually every sector of the economy and every corner of society.
Big data is saving lives. Infections are dangerous—even deadly—for many babies born prematurely. By collecting and analyzing millions of data points from a NICU, one study was able to identify factors, like slight increases in body temperature and heart rate, that serve as early warning signs an infection may be taking root—subtle changes that even the most experienced doctors wouldn’t have noticed on their own.
Big data is making the economy work better. Jet engines and delivery trucks now come outfitted with sensors that continuously monitor hundreds of data points and send automatic alerts when maintenance is needed. Utility companies are starting to use big data to predict periods of peak electric demand, adjusting the grid to be more efficient and potentially averting brown-outs.
Big data is making government work better and saving taxpayer dollars. The Centers for Medicare and Medicaid Services have begun using predictive analytics—a big data technique—to flag likely instances of reimbursement fraud before claims are paid. The Fraud Prevention System helps identify the highest-risk health care providers for waste, fraud, and abuse in real time and has already stopped, prevented, or identified $115 million in fraudulent payments.
But big data raises serious questions, too, about how we protect our privacy and other values in a world where data collection is increasingly ubiquitous and where analysis is conducted at speeds approaching real time. In particular, our review raised the question of whether the “notice and consent” framework, in which a user grants permission for a service to collect and use information about them, still allows us to meaningfully control our privacy as data about us is increasingly used and reused in ways that could not have been anticipated when it was collected.
Big data raises other concerns, as well. One significant finding of our review was the potential for big data analytics to lead to discriminatory outcomes and to circumvent longstanding civil rights protections in housing, employment, credit, and the consumer marketplace.
No matter how quickly technology advances, it remains within our power to ensure that we both encourage innovation and protect our values through law, policy, and the practices we encourage in the public and private sector. To that end, we make six actionable policy recommendations in our report to the President:
Advance the Consumer Privacy Bill of Rights. Consumers deserve clear, understandable, reasonable standards for how their personal information is used in the big data era. We recommend the Department of Commerce take appropriate consultative steps to seek stakeholder and public comment on what changes, if any, are needed to the Consumer Privacy Bill of Rights, first proposed by the President in 2012, and to prepare draft legislative text for consideration by stakeholders and submission by the President to Congress.
Pass National Data Breach Legislation. Big data technologies make it possible to store significantly more data, and further derive intimate insights into a person’s character, habits, preferences, and activities. That makes the potential impacts of data breaches at businesses or other organizations even more serious. A patchwork of state laws currently governs requirements for reporting data breaches. Congress should pass legislation that provides for a single national data breach standard, along the lines of the Administration’s 2011 Cybersecurity legislative proposal.
Extend Privacy Protections to non-U.S. Persons. Privacy is a worldwide value that should be reflected in how the federal government handles personally identifiable information about non-U.S. citizens. The Office of Management and Budget should work with departments and agencies to apply the Privacy Act of 1974 to non-U.S. persons where practicable, or to establish alternative privacy policies that apply appropriate and meaningful protections to personal information regardless of a person’s nationality.
Ensure Data Collected on Students in School is used for Educational Purposes. Big data and other technological innovations, including new online course platforms that provide students real time feedback, promise to transform education by personalizing learning. At the same time, the federal government must ensure educational data linked to individual students gathered in school is used for educational purposes, and protect students against their data being shared or used inappropriately.
Expand Technical Expertise to Stop Discrimination. The detailed personal profiles held about many consumers, combined with automated, algorithm-driven decision-making, could lead—intentionally or inadvertently—to discriminatory outcomes, or what some are already calling “digital redlining.” The federal government’s lead civil rights and consumer protection agencies should expand their technical expertise to be able to identify practices and outcomes facilitated by big data analytics that have a discriminatory impact on protected classes, and develop a plan for investigating and resolving violations of law.
Amend the Electronic Communications Privacy Act. The laws that govern protections afforded to our communications were written before email, the internet, and cloud computing came into wide use. Congress should amend ECPA to ensure the standard of protection for online, digital content is consistent with that afforded in the physical world—including by removing archaic distinctions between email left unread or over a certain age.
We also identify several broader areas ripe for further study, debate, and public engagement that, collectively, we hope will spark a national conversation about how to harness big data for the public good. We conclude that we must find a way to preserve our privacy values in both the domestic and international marketplace. We urgently need to build capacity in the federal government to identify and prevent new modes of discrimination that could be enabled by big data. We must ensure that law enforcement agencies using big data technologies do so responsibly, and that our fundamental privacy rights remain protected. Finally, we recognize that data is a valuable public resource, and call for continuing the Administration’s efforts to open more government data sources and make investments in research and technology.
While big data presents new challenges, it also presents immense opportunities to improve lives, the United States is perhaps better suited to lead this conversation than any other nation on earth. Our innovative spirit, technological know-how, and deep commitment to values of privacy, fairness, non-discrimination, and self-determination will help us harness the benefits of the big data revolution and encourage the free flow of information while working with our international partners to protect personal privacy. This review is but one piece of that effort, and we hope it spurs a conversation about big data across the country and around the world.
Read the Big Data Report.
See the fact sheet from today’s announcement.“
Mapping the Intersection Between Social Media and Open Spaces in California
Stamen Design: “Last month, Stamen launched parks.stamen.com, a project we created in partnership with the Electric Roadrunner Lab, with the goal of revealing the diversity of social media activity that happens inside parks and other open spaces in California. If you haven’t already looked at the site, please go visit it now! Find your favorite park, or the parks that are nearest to you, or just stroll between random parks using the wander button. For more background about the goals of the project, read Eric’s blog post: A Conversation About California Parks.
In this post I’d like to describe some of the algorithms we use to collect the social media data that feeds the park pages. Currently we collect data from four social media platforms: Twitter, Foursquare, Flickr, and Instagram. We chose these because they all have public APIs (Application Programming Interfaces) that are easy to work with, and we expect they will provide a view into the different facets of each park, and the diverse communities who enjoy these parks. Each social media service creates its own unique geographies, and its own way of representing these parks. For example, the kinds of photos you upload to Instagram might be different from the photos you upload to Flickr. The way you describe experiences using Twitter might be different from the moments you document by checking into Foursquare. In the future we may add more feeds, but for now there’s a lot we can learn from these four.
Through the course of collecting data from these social network services, I also found that each service’s public API imposes certain constraints on our queries, producing their own intricate patterns. Thus, the quirks of how each API was written results in distinct and fascinating geometries. Also, since we are only interested in parks for this project, the process of culling non-park-related content further produces unusual and interesting patterns. Rural areas have large parks that cover huge areas, while cities have lots of (relatively) tiny parks, which creates its own challenges for how we query the APIs.
Broadly, we followed a similar approach for all the social media services. First, we grab the geocoded data from the APIs. This ignores any media that don’t have a latitude and longitude associated with them. In Foursquare, almost all checkins have a latitude and longitude, and for Flickr and Instagram most photos have a location associated with them. However, for Twitter, only around 1% of all tweets have geographic coordinates. But as we will see, even 1% still results in a whole lot of tweets!
After grabbing the social media data, we intersect it with the outlines of parks and open spaces in California, using polygons from the California Protected Areas Database maintained by GreenInfo Network. Everything that doesn’t intersect one of these parks, we throw away. The following maps represent the data as it looks before the filtering process.
But enough talking, let’s look at some maps!”
The Right Colors Make Data Easier To Read
Sharon Lin And Jeffrey Heer at HBR Blog: “What is the color of money? Of love? Of the ocean? In the United States, most people respond that money is green, love is red and the ocean is blue. Many concepts evoke related colors — whether due to physical appearance, common metaphors, or cultural conventions. When colors are paired with the concepts that evoke them, we call these “semantically resonant color choices.”
Artists and designers regularly use semantically resonant colors in their work. And in the research we conducted with Julie Fortuna, Chinmay Kulkarni, and Maureen Stone, we found they can be remarkably important to data visualization.
Consider these charts of (fictional) fruit sales:
The only difference between the charts is the color assignment. The left-hand chart uses colors from a default palette. The right-hand chart has been assigned semantically resonant colors. (In this case, the assignment was computed automatically using an algorithm that analyzes the colors in relevant images retrieved from Google Image Search using queries for each data category name.)
Now, try answering some questions about the data in each of these charts. Which fruit had higher sales: blueberries or tangerines? How about peaches versus apples? Which chart do you find easier to read?…
To make effective visualization color choices, you need to take a number of factors into consideration. To name just two: All the colors need to be suitably different from one another, for instance, so that readers can tell them apart – what’s called “discriminability.” You also need to consider what the colors look like to the color blind — roughly 8% of the U.S. male population! Could the colors be distinguished from one another if they were reprinted in black and white?
One easy way to assign semantically resonant colors is to use colors from an existing color palette that has been carefully designed for visualization applications (ColorBrewer offers some options) but assign the colors to data values in a way that best matches concept color associations. This is the basis of our own algorithm, which acquires images for each concept and then analyzes them to learn concept color associations. However, keep in mind that color associations may vary across cultures. For example, in the United States and many western cultures, luck is often associated with green (four-leaf clovers), while red can be considered a color of danger. However, in China, luck is traditionally symbolized with the color red.
…
Semantically resonant colors can reinforce perception of a wide range of data categories. We believe similar gains would likely be seen for other forms of visualizations like maps, scatterplots, and line charts. So when designing visualizations for presentation or analysis, consider color choice and ask yourself how well the colors resonate with the underlying data.”
The Open Data 500: Putting Research Into Action
TheGovLab Blog: “On April 8, the GovLab made two significant announcements. At an open data event in Washington, DC, I was pleased to announce the official launch of the Open Data 500, our study of 500 companies that use open government data as a key business resource. We also announced that the GovLab is now planning a series of Open Data Roundtables to bring together government agencies with the businesses that use their data – and that five federal agencies have agreed to participate. Video of the event, which was hosted by the Center for Data Innovation, is available here.
The Open Data 500, funded by the John S. and James L. Knight Foundation, is the first comprehensive study of U.S.-based companies that rely on open government data. Our website at OpenData500.com includes searchable, sortable information on 500 of these companies. Our data about them comes from responses to a survey we’ve sent to all the companies (190 have responded) and what we’ve been able to learn from research using public information. Anyone can now explore this website, read about specific companies or groups of companies, or download our data to analyze it. The website features an interactive tool on the home page, the Open Data Compass, that shows the connections between government agencies and different categories of companies visually.
We began work on the Open Data 500 study last fall with three goals. First, we wanted to collect information that will ultimately help calculate the economic value of open data – an important question for policymakers and others. Second, we wanted to present examples of open data companies to inspire others to use this important government resource in new ways. And third – and perhaps most important – we’ve hoped that our work will be a first step in creating a dialogue between the government agencies that provide open data and the companies that use it.
That dialogue is critically important to make government open data more accessible and useful. While open government data is a huge potential resource, and federal agencies are working to make it more available, it’s too often trapped in legacy systems that make the data difficult to find and to use. To solve this problem, we plan to connect agencies to their clients in the business community and help them work together to find and liberate the most valuable datasets.
We now plan to convene and facilitate a series of Open Data Roundtables – a new approach to bringing businesses and government agencies together. In these Roundtables, which will be informed by the Open Data 500 study, companies and the agencies that provide their data will come together in structured, results-oriented meetings that we will facilitate. We hope to help figure out what can be done to make the most valuable datasets more available and usable quickly.
We’ve been gratified by the immediate positive response to our plan from several federal agencies. The Department of Commerce has committed to help plan and participate in the first of our Roundtables, now being scheduled for May. By the time we announced our launch on April 8, the Departments of Labor, Transportation, and Treasury had also signed up. And at the end of the launch event, the Deputy Chief Information Officer of the USDA publicly committed her agency to participate as well…”
“Government Entrepreneur” is Not an Oxymoron
Mitchell Weiss in Harvard Business Review Blog: “Entrepreneurship almost always involves pushing against the status quo to capture opportunities and create value. So it shouldn’t be surprising when a new business model, such as ridesharing, disrupts existing systems and causes friction between entrepreneurs and local government officials, right?
But imagine if the road that led to the Seattle City Council ridesharing hearings this month — with rulings that sharply curtail UberX, Lyft, and Sidecar’s operations there — had been a vastly different one. Imagine that public leaders had conceived and built a platform to provide this new, shared model of transit. Or at the very least, that instead of having a revolution of the current transit regime done to Seattle public leaders, it was done with them. Amidst the acrimony, it seems hard to imagine that public leaders could envision and operate such a platform, or that private innovators could work with them more collaboratively on it — but it’s not impossible. What would it take? Answer: more public entrepreneurs.
The idea of ”public entrepreneurship” may sound to you like it belongs on a list of oxymorons right alongside “government intelligence.” But it doesn’t. Public entrepreneurs around the world are improving our lives, inventing entirely new ways to serve the public. They are using sensors to detect potholes; word pedometers to help students learn; harnessing behavioral economics to encourage organ donation; crowdsourcing patent review; and transforming Medellin, Colombia with cable cars. They are coding in civic hackathons and competing in the Bloomberg challenge. They are partnering with an Office of New Urban Mechanics in Boston or in Philadelphia, co-developing products in San Francisco’s Entrepreneurship-in-Residence program, or deploying some of the more than $430 million invested into civic-tech in the last two years.
There is, however, a big problem with public entrepreneurs: there just aren’t enough of them. Without more public entrepreneurship, it’s hard to imagine meeting our public challenges or making the most of private innovation. One might argue that bungled healthcare website roll-outs or internet spying are evidence of too much activity on the part of public leaders, but I would argue that what they really show is too little entrepreneurial skill and judgment.
The solution to creating more public entrepreneurs is straightforward: train them. But, by and large, we don’t. Consider Howard Stevenson’s definition of entrepreneurship: “the pursuit of opportunity without regard to resources currently controlled.” We could teach that approach to people heading towards the public sector. But now consider the following list of terms: “acknowledgement of multiple constituencies,” “risk reduction,” “formal planning,” “coordination,” “efficiency measures,” “clearly defined responsibility,” and “organizational culture.” It reads like a list of the kinds of concepts we would want a new public official to know; like it might be drawn from an interview evaluation form or graduate school syllabus. In fact, it’s from Stevenson’s list of pressures that pull managers away from entrepreneurship and towards administration. Of course, that’s not all bad. We must have more great public administrators. But with all our challenges and amidst all the dynamism, we are going to need more than analysts and strategists in the public sector, we need inventors and builders, too.
Public entrepreneurship is not simply innovation in the public sector (though it makes use of innovation), and it’s not just policy reform (though it can help drive reform). Public entrepreneurs build something from nothing with resources — be they financial capital or human talent or new rules — they didn’t command. In Boston, I worked with many amazing public managers and a handful of outstanding public entrepreneurs. Chris Osgood and Nigel Jacob brought the country’s first major-city mobile 311 app to life, and they are public entrepreneurs. They created Citizens Connect in 2009 by bringing together iPhones on loan together with a local coder and the most under-tapped resource in the public sector: the public. They transformed the way basic neighborhood issues are reported and responded to (20% of all constituent cases in Boston are reported over smartphones now), and their model is now accessible to 40 towns in Massachusetts and cities across the country. The Mayor’s team in Boston that started-up the One Fund in the days after the Marathon bombings were public entrepreneurs. We built the organization from PayPal and a Post Office Box, and it went on to channel $61 million from donors to victims and survivors in just 75 days. It still operates today….
It’s worth noting that public entrepreneurship, perhaps newly buzzworthy, is not actually new. Elinor Ostrom (44 years before her Nobel Prize) observed public entrepreneurs inventing new models in the 1960s. Back when Ronald Reagan was president, Peter Drucker wrote that it was entrepreneurship that would keep public service “flexible and self-renewing.” And almost two decades have passed since David Osborne and Ted Gaebler’s “Reinventing Government” (the then handbook for public officials) carried the promising subtitle: “How the Entrepreneurial Spirit is Transforming the Public Sector”. Public entrepreneurship, though not nearly as widespread as its private complement, or perhaps as fashionable as its “social” counterpart (focussed on non-profits and their ecosystem), has been around for a while and so have those who practiced it.
But still today, we mostly train future public leaders to be public administrators. We school them in performance management and leave them too inclined to run from risk instead of managing it. And we communicate often, explicitly or not, to private entrepreneurs that government officials are failures and dinosaurs. It’s easy to see how that road led to Seattle this month, but hard see how it empowers public officials to take on the enormous challenges that still lie ahead of us, or how it enables the public to help them.”
But imagine if the road that led to the Seattle City Council ridesharing hearings this month — with rulings that sharply curtail UberX, Lyft, and Sidecar’s operations there — had been a vastly different one. Imagine that public leaders had conceived and built a platform to provide this new, shared model of transit. Or at the very least, that instead of having a revolution of the current transit regime done to Seattle public leaders, it was done with them. Amidst the acrimony, it seems hard to imagine that public leaders could envision and operate such a platform, or that private innovators could work with them more collaboratively on it — but it’s not impossible. What would it take? Answer: more public entrepreneurs.
The idea of ”public entrepreneurship” may sound to you like it belongs on a list of oxymorons right alongside “government intelligence.” But it doesn’t. Public entrepreneurs around the world are improving our lives, inventing entirely new ways to serve the public. They are using sensors to detect potholes; word pedometers to help students learn; harnessing behavioral economics to encourage organ donation; crowdsourcing patent review; and transforming Medellin, Colombia with cable cars. They are coding in civic hackathons and competing in the Bloomberg challenge. They are partnering with an Office of New Urban Mechanics in Boston or in Philadelphia, co-developing products in San Francisco’s Entrepreneurship-in-Residence program, or deploying some of the more than $430 million invested into civic-tech in the last two years.
There is, however, a big problem with public entrepreneurs: there just aren’t enough of them. Without more public entrepreneurship, it’s hard to imagine meeting our public challenges or making the most of private innovation. One might argue that bungled healthcare website roll-outs or internet spying are evidence of too much activity on the part of public leaders, but I would argue that what they really show is too little entrepreneurial skill and judgment.
The solution to creating more public entrepreneurs is straightforward: train them. But, by and large, we don’t. Consider Howard Stevenson’s definition of entrepreneurship: “the pursuit of opportunity without regard to resources currently controlled.” We could teach that approach to people heading towards the public sector. But now consider the following list of terms: “acknowledgement of multiple constituencies,” “risk reduction,” “formal planning,” “coordination,” “efficiency measures,” “clearly defined responsibility,” and “organizational culture.” It reads like a list of the kinds of concepts we would want a new public official to know; like it might be drawn from an interview evaluation form or graduate school syllabus. In fact, it’s from Stevenson’s list of pressures that pull managers away from entrepreneurship and towards administration. Of course, that’s not all bad. We must have more great public administrators. But with all our challenges and amidst all the dynamism, we are going to need more than analysts and strategists in the public sector, we need inventors and builders, too.
Public entrepreneurship is not simply innovation in the public sector (though it makes use of innovation), and it’s not just policy reform (though it can help drive reform). Public entrepreneurs build something from nothing with resources — be they financial capital or human talent or new rules — they didn’t command. In Boston, I worked with many amazing public managers and a handful of outstanding public entrepreneurs. Chris Osgood and Nigel Jacob brought the country’s first major-city mobile 311 app to life, and they are public entrepreneurs. They created Citizens Connect in 2009 by bringing together iPhones on loan together with a local coder and the most under-tapped resource in the public sector: the public. They transformed the way basic neighborhood issues are reported and responded to (20% of all constituent cases in Boston are reported over smartphones now), and their model is now accessible to 40 towns in Massachusetts and cities across the country. The Mayor’s team in Boston that started-up the One Fund in the days after the Marathon bombings were public entrepreneurs. We built the organization from PayPal and a Post Office Box, and it went on to channel $61 million from donors to victims and survivors in just 75 days. It still operates today….
It’s worth noting that public entrepreneurship, perhaps newly buzzworthy, is not actually new. Elinor Ostrom (44 years before her Nobel Prize) observed public entrepreneurs inventing new models in the 1960s. Back when Ronald Reagan was president, Peter Drucker wrote that it was entrepreneurship that would keep public service “flexible and self-renewing.” And almost two decades have passed since David Osborne and Ted Gaebler’s “Reinventing Government” (the then handbook for public officials) carried the promising subtitle: “How the Entrepreneurial Spirit is Transforming the Public Sector”. Public entrepreneurship, though not nearly as widespread as its private complement, or perhaps as fashionable as its “social” counterpart (focussed on non-profits and their ecosystem), has been around for a while and so have those who practiced it.
But still today, we mostly train future public leaders to be public administrators. We school them in performance management and leave them too inclined to run from risk instead of managing it. And we communicate often, explicitly or not, to private entrepreneurs that government officials are failures and dinosaurs. It’s easy to see how that road led to Seattle this month, but hard see how it empowers public officials to take on the enormous challenges that still lie ahead of us, or how it enables the public to help them.”
Climate Data Initiative Launches with Strong Public and Private Sector Commitments
John Podesta and Dr. John P. Holdren at the White House blog: “…today, delivering on a commitment in the President’s Climate Action Plan, we are launching the Climate Data Initiative, an ambitious new effort bringing together extensive open government data and design competitions with commitments from the private and philanthropic sectors to develop data-driven planning and resilience tools for local communities. This effort will help give communities across America the information and tools they need to plan for current and future climate impacts.
The Climate Data Initiative builds on the success of the Obama Administration’s ongoing efforts to unleash the power of open government data. Since data.gov, the central site to find U.S. government data resources, launched in 2009, the Federal government has released troves of valuable data that were previously hard to access in areas such as health, energy, education, public safety, and global development. Today these data are being used by entrepreneurs, researchers, tech innovators, and others to create countless new applications, tools, services, and businesses.
Data from NOAA, NASA, the U.S. Geological Survey, the Department of Defense, and other Federal agencies will be featured on climate.data.gov, a new section within data.gov that opens for business today. The first batch of climate data being made available will focus on coastal flooding and sea level rise. NOAA and NASA will also be announcing an innovation challenge calling on researchers and developers to create data-driven simulations to help plan for the future and to educate the public about the vulnerability of their own communities to sea level rise and flood events.
These and other Federal efforts will be amplified by a number of ambitious private commitments. For example, Esri, the company that produces the ArcGIS software used by thousands of city and regional planning experts, will be partnering with 12 cities across the country to create free and open “maps and apps” to help state and local governments plan for climate change impacts. Google will donate one petabyte—that’s 1,000 terabytes—of cloud storage for climate data, as well as 50 million hours of high-performance computing with the Google Earth Engine platform. The company is challenging the global innovation community to build a high-resolution global terrain model to help communities build resilience to anticipated climate impacts in decades to come. And the World Bank will release a new field guide for the Open Data for Resilience Initiative, which is working in more than 20 countries to map millions of buildings and urban infrastructure….”