Google Maps is turning its over a billion users into editors

Curated on Posted on by Stefaan Verhulst

TechCrunch: “Google has begun to further tap into the power of the crowd in order to improve its Google Maps application, the company announced this morning. This is being done through the introduction of a number of features that will allow users to more easily share location details, as well as confirm edits suggested by others. Many users had already seen these changes rolling out, but today Google is making them official – an indication that the broader rollout is completing.

While Google says that it makes “millions” of updates to Maps every day, it’s still not enough to ensure that every location has the most accurate, detailed information. That’s why it’s turning to users to help it improve the mapping service.

The company has expanded users’ ability to both add missing places to Google Maps or correct business information through its Google Maps iOS and Android applications, as well as in Google Search. You may have already noticed options for “Suggest an Edit,” or “Add a Missing Place,” for example, which allow you to make your contributions.

Screen Shot 2016-07-21 at 10.32.19 AM

Those edits don’t immediately go live, however, as one user’s input alone isn’t enough to determine the edit’s accuracy….(More)”

Global Indicators of Regulatory Governance

Curated on Posted on by Stefaan Verhulst

Worldbank: “The Global Indicators of Regulatory Governance project is an initiative of the World Bank’sGlobal Indicators Group, which produces a range of datasets and benchmarking products on regulations and business activity around the world. These datasets include Doing Business,Enterprise Surveys, Enabling the Business of Agriculture and Women, Business and the Law.

The Global Indicators of Regulatory Governance project explores how governments interact with the public when shaping regulations that affect their business community. Concerned stakeholders could be professional associations, civic groups or foreign investors. The project charts how interested groups learn about new regulations being considered, and the extent to which they are able to engage with officials on the content. It also measures whether or not governments assess the possible impact of new regulations in their countries (including economic, social and environmental considerations) and whether those calculations form part of the public consultation. Finally, Global Indicators of Regulatory Governance capture two additional components of a predictable regulatory environment: the ability of stakeholders to challenge regulations, and the ability of people to access all the laws and regulations currently in force in one, consolidated place.

The project grew out of an increasing recognition of the importance of transparency and accountability in government actions. Citizen access to the government rulemaking process is central for the creation of a business environment in which investors make long-range plans and investments. Greater levels of consultation are also associated with a higher quality of regulation…(More) ( View project summary (PDF, 190KB)”

What Governments Can Learn From Airbnb And the Sharing Economy

Curated on Posted on by Stefaan Verhulst

Arun Sundararajan in Fortune: “….Despite some regulators’ fears, the sharing economy may not result in the decline of regulation but rather in its opposite, providing a basis upon which society can develop more rational, ethical, and participatory models of regulation. But what regulation looks like, as well as who actually creates and enforce the regulation, is also bound to change.

There are three emerging models – peer regulation, self-regulatory organizations, and data-driven delegation – that promise a regulatory future for the sharing economy best aligned with society’s interests. In the adapted book excerpt that follows, I explain how the third of these approaches, of delegating enforcement of regulations to companies that store critical data on consumers, can help mitigate some of the biases Airbnb guests may face, and why this is a superior alternative to the “open data” approach of transferring consumer information to cities and state regulators.

Consider a different problem — of collecting hotel occupancy taxes from hundreds of thousands of Airbnb hosts rather than from a handful of corporate hotel chains. The delegation of tax collection to Airbnb, something a growing number of cities are experimenting with, has a number of advantages. It is likely to yield higher tax revenues and greater compliance than a system where hosts are required to register directly with the government, which is something occasional hosts seem reluctant to do. It also sidesteps privacy concerns resulting from mandates that digital platforms like Airbnb turn over detailed user data to the government. There is also significant opportunity for the platform to build credibility as it starts to take on quasi governmental roles like this.

There is yet another advantage, and the one I believe will be the most significant in the long-run. It asks a platform to leverage its data to ensure compliance with a set of laws in a manner geared towards delegating responsibility to the platform. You might say that the task in question here — computing tax owed, collecting, and remitting it—is technologically trivial. True. But I like this structure because of the potential it represents. It could be a precursor for much more exciting delegated possibilities.

For a couple of decades now, companies of different kinds have been mining the large sets of “data trails” customers provide through their digital interactions. This generates insights of business and social importance. One such effort we are all familiar with is credit card fraud detection. When an unusual pattern of activity is detected, you get a call from your bank’s security team. Sometimes your card is blocked temporarily. The enthusiasm of these digital security systems is sometimes a nuisance, but it stems from your credit card company using sophisticated machine learning techniques to identify patterns that prior experience has told it are associated with a stolen card. It saves billions of dollars in taxpayer and corporate funds by detecting and blocking fraudulent activity swiftly.

A more recent visible example of the power of mining large data sets of customer interaction came in 2008, when Google engineers announced that they could predict flu outbreaks using data collected from Google searches, and track the spread of flu outbreaks in real time, providing information that was well ahead of the information available using the Center for Disease Control’s (CDC) own tracking systems. The Google system’s performance deteriorated after a couple of years, but its impact on public perception of what might be possible using “big data” was immense.

It seems highly unlikely that such a system would have emerged if Google had been asked to hand over anonymized search data to the CDC. In fact, there would have probably been widespread public backlash to this on privacy grounds. Besides, the reason why this capability emerged organically from within Google is partly as a consequence of Google having one of the highest concentrations of computer science and machine learning talent in the world.

Similar approaches hold great promise as a regulatory approach for sharing economy platforms. Consider the issue of discriminatory practices. There has long been anecdotal evidence that some yellow cabs in New York discriminate against some nonwhite passengers. There have been similar concerns that such behavior may start to manifest on ridesharing platforms and in other peer-to-peer markets for accommodation and labor services.

For example, a 2014 study by Benjamin Edelman and Michael Luca of Harvard suggested that African American hosts might have lower pricing power than white hosts on Airbnb. While the study did not conclusively establish that the difference is due to guests discriminating against African American hosts, a follow-up study suggested that guests with “distinctively African American names” were less likely to receive favorable responses for their requests to Airbnb hosts. This research raises a red flag about the need for vigilance as the lines between personal and professional blur.

One solution would be to apply machine-learning techniques to be able to identify patterns associated with discriminatory behavior. No doubt, many platforms are already using such systems….(More)”

How Twitter gives scientists a window into human happiness and health

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “Since its public launch 10 years ago, Twitter has been used as a social networking platform among friends, an instant messaging service for smartphone users and a promotional tool for corporations and politicians.

But it’s also been an invaluable source of data for researchers and scientists – like myself – who want to study how humans feel and function within complex social systems.

By analyzing tweets, we’ve been able to observe and collect data on the social interactions of millions of people “in the wild,” outside of controlled laboratory experiments.

It’s enabled us to develop tools for monitoring the collective emotions of large populations, find the happiest places in the United States and much more.

So how, exactly, did Twitter become such a unique resource for computational social scientists? And what has it allowed us to discover?

Twitter’s biggest gift to researchers

On July 15, 2006, Twittr (as it was then known) publicly launched as a “mobile service that helps groups of friends bounce random thoughts around with SMS.” The ability to send free 140-character group texts drove many early adopters (myself included) to use the platform.

With time, the number of users exploded: from 20 million in 2009 to 200 million in 2012 and 310 million today. Rather than communicating directly with friends, users would simply tell their followers how they felt, respond to news positively or negatively, or crack jokes.

For researchers, Twitter’s biggest gift has been the provision of large quantities of open data. Twitter was one of the first major social networks to provide data samples through something called Application Programming Interfaces (APIs), which enable researchers to query Twitter for specific types of tweets (e.g., tweets that contain certain words), as well as information on users.

This led to an explosion of research projects exploiting this data. Today, a Google Scholar search for “Twitter” produces six million hits, compared with five million for “Facebook.” The difference is especially striking given that Facebook has roughly five times as many users as Twitter (and is two years older).

Twitter’s generous data policy undoubtedly led to some excellent free publicity for the company, as interesting scientific studies got picked up by the mainstream media.

Studying happiness and health

With traditional census data slow and expensive to collect, open data feeds like Twitter have the potential to provide a real-time window to see changes in large populations.

The University of Vermont’s Computational Story Lab was founded in 2006 and studies problems across applied mathematics, sociology and physics. Since 2008, the Story Lab has collected billions of tweets through Twitter’s “Gardenhose” feed, an API that streams a random sample of 10 percent of all public tweets in real time.

I spent three years at the Computational Story Lab and was lucky to be a part of many interesting studies using this data. For example, we developed a hedonometer that measures the happiness of the Twittersphere in real time. By focusing on geolocated tweets sent from smartphones, we were able to map the happiest places in the United States. Perhaps unsurprisingly, we found Hawaii to be the happiest state and wine-growing Napa the happiest city for 2013.

A map of 13 million geolocated U.S. tweets from 2013, colored by happiness, with red indicating happiness and blue indicating sadness. PLOS ONE, Author provided

These studies had deeper applications: Correlating Twitter word usage with demographics helped us understand underlying socioeconomic patterns in cities. For example, we could link word usage with health factors like obesity, so we built a lexicocalorimeter to measure the “caloric content” of social media posts. Tweets from a particular region that mentioned high-calorie foods increased the “caloric content” of that region, while tweets that mentioned exercise activities decreased our metric. We found that this simple measure correlates with other health and well-being metrics. In other words, tweets were able to give us a snapshot, at a specific moment in time, of the overall health of a city or region.

Using the richness of Twitter data, we’ve also been able to see people’s daily movement patterns in unprecedented detail. Understanding human mobility patterns, in turn, has the capacity to transform disease modeling, opening up the new field of digital epidemiology….(More)”

How technology disrupted the truth

Curated on Posted on by Stefaan Verhulst

 in The Guardian: “Social media has swallowed the news – threatening the funding of public-interest reporting and ushering in an era when everyone has their own facts. But the consequences go far beyond journalism…

When a fact begins to resemble whatever you feel is true, it becomes very difficult for anyone to tell the difference between facts that are true and “facts” that are not. The leave campaign was well aware of this – and took full advantage, safe in the knowledge that the Advertising Standards Authority has no power to police political claims. A few days after the vote, Arron Banks, Ukip’s largest donor and the main funder of the Leave.EU campaign, told the Guardian that his side knew all along that facts would not win the day. “It was taking an American-style media approach,” said Banks. “What they said early on was ‘Facts don’t work’, and that’s it. The remain campaign featured fact, fact, fact, fact, fact. It just doesn’t work. You have got to connect with people emotionally. It’s the Trump success.”
It was little surprise that some people were shocked after the result to discover that Brexit might have serious consequences and few of the promised benefits. When “facts don’t work” and voters don’t trust the media, everyone believes in their own “truth” – and the results, as we have just seen, can be devastating.

How did we end up here? And how do we fix it?

Twenty-five years after the first website went online, it is clear that we are living through a period of dizzying transition. For 500 years after Gutenberg, the dominant form of information was the printed page: knowledge was primarily delivered in a fixed format, one that encouraged readers to believe in stable and settled truths.

Now, we are caught in a series of confusing battles between opposing forces: between truth and falsehood, fact and rumour, kindness and cruelty; between the few and the many, the connected and the alienated; between the open platform of the web as its architects envisioned it and the gated enclosures of Facebook and other social networks; between an informed public and a misguided mob.

What is common to these struggles – and what makes their resolution an urgent matter – is that they all involve the diminishing status of truth. This does not mean that there are no truths. It simply means, as this year has made very clear, that we cannot agree on what those truths are, and when there is no consensus about the truth and no way to achieve it, chaos soon follows.

Increasingly, what counts as a fact is merely a view that someone feels to be true – and technology has made it very easy for these “facts” to circulate with a speed and reach that was unimaginable in the Gutenberg era (or even a decade ago). A dubious story about Cameron and a pig appears in a tabloid one morning, and by noon, it has flown around the world on social media and turned up in trusted news sources everywhere. This may seem like a small matter, but its consequences are enormous.

In the digital age, it is easier than ever to publish false information, which is quickly shared and taken to be true. “The Truth”, as Peter Chippindale and Chris Horrie wrote in Stick It Up Your Punter!, their history of the Sun newspaper, is a “bald statement which every newspaper prints at its peril”. There are usually several conflicting truths on any given subject, but in the era of the printing press, words on a page nailed things down, whether they turned out to be true or not. The information felt like the truth, at least until the next day brought another update or a correction, and we all shared a common set of facts.

This settled “truth” was usually handed down from above: an established truth, often fixed in place by an establishment. This arrangement was not without flaws: too much of the press often exhibited a bias towards the status quo and a deference to authority, and it was prohibitively difficult for ordinary people to challenge the power of the press. Now, people distrust much of what is presented as fact – particularly if the facts in question are uncomfortable, or out of sync with their own views – and while some of that distrust is misplaced, some of it is not.

In the digital age, it is easier than ever to publish false information, which is quickly shared and taken to be true – as we often see in emergency situations, when news is breaking in real time. To pick one example among many, during the November 2015 Paris terror attacks, rumours quickly spread on social media that the Louvre and Pompidou Centre had been hit, and that François Hollande had suffered a stroke. Trusted news organisations are needed to debunk such tall tales.

Sometimes rumours like these spread out of panic, sometimes out of malice, and sometimes deliberate manipulation, in which a corporation or regime pays people to convey their message. Whatever the motive, falsehoods and facts now spread the same way, through what academics call an “information cascade”. As the legal scholar and online-harassment expert Danielle Citron describes it, “people forward on what others think, even if the information is false, misleading or incomplete, because they think they have learned something valuable.” This cycle repeats itself, and before you know it, the cascade has unstoppable momentum. You share a friend’s post on Facebook, perhaps to show kinship or agreement or that you’re “in the know”, and thus you increase the visibility of their pot to others.
Algorithms such as the one that powers Facebook’s news feed are designed to give us more of what they think we want – which means that the version of the world we encounter every day in our own personal stream has been invisibly curated to reinforce our pre-existing beliefs. When Eli Pariser, the co-founder of Upworthy, coined the term “filter bubble” in 2011, he was talking about how the personalised web – and in particular Google’s personalised search function, which means that no two people’s Google searches are the same – means that we are less likely to be exposed to information that challenges us or broadens our worldview, and less likely to encounter facts that disprove false information that others have shared.

Pariser’s plea, at the time, was that those running social media platforms should ensure that “their algorithms prioritise countervailing views and news that’s important, not just the stuff that’s most popular or most self-validating”. But in less than five years, thanks to the incredible power of a few social platforms, the filter bubble that Pariser described has become much more extreme.

On the day after the EU referendum, in a Facebook post, the British internet activist and mySociety founder, Tom Steinberg, provided a vivid illustration of the power of the filter bubble – and the serious civic consequences for a world where information flows largely through social networks:

I am actively searching through Facebook for people celebrating the Brexit leave victory, but the filter bubble is SO strong, and extends SO far into things like Facebook’s custom search that I can’t find anyone who is happy *despite the fact that over half the country is clearly jubilant today* and despite the fact that I’m *actively* looking to hear what they are saying.

This echo-chamber problem is now SO severe and SO chronic that I can only beg any friends I have who actually work for Facebook and other major social media and technology to urgently tell their leaders that to not act on this problem now is tantamount to actively supporting and funding the tearing apart of the fabric of our societies … We’re getting countries where one half just doesn’t know anything at all about the other.

But asking technology companies to “do something” about the filter bubble presumes that this is a problem that can be easily fixed – rather than one baked into the very idea of social networks that are designed to give you what you and your friends want to see….(More)”

Data as a Means, Not an End: A Brief Case Study

Curated on Posted on by Stefaan Verhulst

Tracie Neuhaus & Jarasa Kanok  in the Stanford Social Innovation Review: “In 2014, City Year—the well-known national education nonprofit that leverages young adults in national service to help students and schools succeed—was outgrowing the methods it used for collecting, managing, and using performance data. As the organization established its strategy for long-term impact, leaders identified a business problem: The current system for data collection and use would need to evolve to address the more-complex challenges the organization was undertaking. Staff throughout the organization were citing pain points one might expect, including onerous manual data collection, and long lag times to get much-needed data and reports on student attendance, grades, and academic and social-emotional assessments. After digging deeper, leaders realized they couldn’t fix the organization’s challenges with technology or improved methods without first addressing more fundamental issues. They saw City Year lacked a common “language” for the data it collected and used. Staff varied widely in their levels of data literacy, as did the scope of data-sharing agreements with the 27 urban school districts where City Year was working at the time. What’s more, its evaluation group had gradually become a default clearinghouse for a wide variety of service requests from across the organization that the group was neither designed nor staffed to address. The situation was much more complex than it appeared.

With significant technology roadmap decisions looming, City Year engaged with us to help it develop its data strategy. Together we came to realize that these symptoms were reflective of a single issue, one that exists in many organizations: City Year’s focus on data wasn’t targeted to address the very different kinds of decisions that each staff member—from the front office to the front lines—needed to make. …

Many of us in the social sector have probably seen elements of this dynamic. Many organizations create impact reports designed to satisfy external demands from donors, but these reports have little relevance to the operational or strategic choices the organizations face every day, much less address harder-to-measure, system-level outcomes. As a result, over time and in the face of constrained resources, measurement is relegated to a compliance activity, disconnected from identifying and collecting the information that directly enables individuals within the organization to drive impact. Gathering data becomes an end in itself, rather than a means of enabling ground-level work and learning how to improve the organization’s impact.

Overcoming this all-too-common “measurement drift” requires that we challenge the underlying orthodoxies that drive it and reorient measurement activities around one simple premise: Data should support better decision-making. This enables organizations to not only shed a significant burden of unproductive activity, but also drive themselves to new heights of performance.

In the case of City Year, leaders realized that to really take advantage of existing technology platforms, they needed a broader mindset shift….(More)”

Research in the Crowdsourcing Age, a Case Study

Curated on Posted on by Stefaan Verhulst

Report by  (Pew): “How scholars, companies and workers are using Mechanical Turk, a ‘gig economy’ platform, for tasks computers can’t handle

How Mechanical Turk WorksDigital age platforms are providing researchers the ability to outsource portions of their work – not just to increasingly intelligent machines, but also to a relatively low-cost online labor force comprised of humans. These so-called “online outsourcing” services help employers connect with a global pool of free-agent workers who are willing to complete a variety of specialized or repetitive tasks.

Because it provides access to large numbers of workers at relatively low cost, online outsourcing holds a particular appeal for academics and nonprofit research organizations – many of whom have limited resources compared with corporate America. For instance, Pew Research Center has experimented with using these services to perform tasks such as classifying documents and collecting website URLs. And a Google search of scholarly academic literature shows that more than 800 studies – ranging from medical research to social science – were published using data from one such platform, Amazon’s Mechanical Turk, in 2015 alone.1

The rise of these platforms has also generated considerable commentary about the so-called “gig economy” and the possible impact it will have on traditional notions about the nature of work, the structure of compensation and the “social contract” between firms and workers. Pew Research Center recently explored some of the policy and employment implications of these new platforms in a national survey of Americans.

Proponents say this technology-driven innovation can offer employers – whether companies or academics – the ability to control costs by relying on a global workforce that is available 24 hours a day to perform relatively inexpensive tasks. They also argue that these arrangements offer workers the flexibility to work when and where they want to. On the other hand, some critics worry this type of arrangement does not give employees the same type of protections offered in more traditional work environments – while others have raised concerns about the quality and consistency of data collected in this manner.

A recent report from the World Bank found that the online outsourcing industry generated roughly $2 billion in 2013 and involved 48 million registered workers (though only 10% of them were considered “active”). By 2020, the report predicted, the industry will generate between $15 billion and $25 billion.

Amazon’s Mechanical Turk is one of the largest outsourcing platforms in the United States and has become particularly popular in the social science research community as a way to conduct inexpensive surveys and experiments. The platform has also become an emblem of the way that the internet enables new businesses and social structures to arise.

In light of its widespread use by the research community and overall prominence within the emerging world of online outsourcing, Pew Research Center conducted a detailed case study examining the Mechanical Turk platform in late 2015 and early 2016. The study utilizes three different research methodologies to examine various aspects of the Mechanical Turk ecosystem. These include human content analysis of the platform, a canvassing of Mechanical Turk workers and an analysis of third party data.

The first goal of this research was to understand who uses the Mechanical Turk platform for research or business purposes, why they use it and who completes the work assignments posted there. To evaluate these issues, Pew Research Center performed a content analysis of the tasks posted on the site during the week of Dec. 7-11, 2015.

A second goal was to examine the demographics and experiences of the workers who complete the tasks appearing on the site. This is relevant not just to fellow researchers that might be interested in using the platform, but as a snapshot of one set of “gig economy” workers. To address these questions, Pew Research Center administered a nonprobability online survey of Turkers from Feb. 9-25, 2016, by posting a task on Mechanical Turk that rewarded workers for answering questions about their demographics and work habits. The sample of 3,370 workers contains any number of interesting findings, but it has its limits. This canvassing emerges from an opt-in sample of those who were active on MTurk during this particular period, who saw our survey and who had the time and interest to respond. It does not represent all active Turkers in this period or, more broadly, all workers on MTurk.

Finally, this report uses data collected by the online tool mturk-tracker, which is run by Dr. Panagiotis G. Ipeirotis of the New York University Stern School of Business, to examine the amount of activity occurring on the site. The mturk-tracker data are publically available online, though the insights presented here have not been previously published elsewhere….(More)”

Why we no longer trust the experts

Curated on Posted on by Stefaan Verhulst

Gillian Tett in the Financial Times: “Last week, I decided to take a gaggle of kids for an end-of-school-year lunch in a New York neighbourhood that I did not know well. I duly began looking for a suitable restaurant. A decade ago, I would have done that by turning to a restaurant guide. In the world I grew up in, it was normal to seek advice from the “experts”.

But in Manhattan last week, it did not occur to me to consult Fodor’s. Instead, I typed what I needed into my cellphone, scrolled through a long list of online restaurant recommendations, including comments from people who had eaten in them — and picked one.

Yes, it was a leap of faith; those restaurant reviews might have been fake. But there were enough voices for me to feel able to trust the wisdom of the cyber crowds — and, as it happened, our lunch choice was very good.

This is a trivial example of a much bigger change that is under way, and one that has some thought-provoking implications in the wake of the Brexit vote. Before the referendum, British citizens were subjected to a blitz of advice about the potential costs of Brexit from “experts”: economists, central bankers, the International Monetary Fund and world leaders, among others. Indeed, the central strategy of the government (and other “Remainers”) appeared to revolve around wheeling out these experts, with their solemn speeches and statistics….

I suspect that it indicates something else: that citizens of the cyber world no longer have much faith in anything that experts say, not just in the political sphere but in numerous others too. At a time when we increasingly rely on crowd-sourced advice rather than official experts to choose a restaurant, healthcare and holidays, it seems strange to expect voters to listen to official experts when it comes to politics.

In our everyday lives, we are moving from a system based around vertical axes of trust, where we trust people who seem to have more authority than we do, to one predicated on horizontal axes of trust: we take advice from our peer group.

You can see this clearly if you look at the surveys conducted by groups such as the Pew Research Center. These show that faith in institutions such as the government, big business and the media has crumbled in recent years; indeed, almost the only institution in the US that has bucked the trend is the military.

What is even more interesting to look at, however, are the areas where trust remains high. In an annual survey conducted by the Edelman public relations firm, people in 20 countries are asked who they trust. They show rising confidence in the “a person like me” category, and surprisingly high trust in digital technology. We live in a world where we increasingly trust our Facebook friends and the Twitter crowd more than we do the IMF or the prime minister.

In some senses, this is good news. Relying on horizontal axes of trust should mean more democracy and empowerment for ordinary citizens. But the problem of this new world is that people can fall prey to social fads and tribalism — or groupthink…..

Either way, nobody is going to put this genie back into the bottle. So we all need to think about what creates the bonds of “trust” in today’s world. And recognise that the 20th-century model of politics, with its reverence for experts and fixed parties, may eventually seem as outdated as restaurant guides. We live in volatile time…(More)”

Civic Data Initiatives

Curated on Posted on by Stefaan Verhulst

Burak Arikan at Medium: “Big data is the term used to define the perpetual and massive data gathered by corporations and governments on consumers and citizens. When the subject of data is not necessarily individuals but governments and companies themselves, we can call it civic data, and when systematically generated in large amounts, civic big data. Increasingly, a new generation of initiatives are generating and organizing structured data on particular societal issues from human rights violations, to auditing government budgets, from labor crimes to climate justice.

These civic data initiatives diverge from the traditional civil society organizations in their outcomes,that they don’t just publish their research as reports, but also open it to the public as a database.Civic data initiatives are quite different in their data work than international non-governmental organizations such as UN, OECD, World Bank and other similar bodies. Such organizations track social, economical, political conditions of countries and concentrate upon producing general statistical data, whereas civic data initiatives aim to produce actionable data on issues that impact individuals directly. The change in the GDP value of a country is useless for people struggling for free transportation in their city. Incarceration rate of a country does not help the struggle of the imprisoned journalists. Corruption indicators may serve as a parameter in a country’s credit score, but does not help to resolve monopolization created with public procurement. Carbon emission statistics do not prevent the energy deals between corrupt governments that destroy the nature in their region.

Partnership / financing network of oil refinery and thermal power plants around SOCAR (The State Oil Company of Azerbaijan). Data mapping by Network of Dispossession.

Needless to say, civic data initiatives also differ from governmental institutions, which are reluctant to share any more that they are legally obligated to. Many governments in the world simply dump scanned hardcopies of documents on official websites instead of releasing machine-readable data, which prevents systematic auditing of government activities.Civic data initiatives, on the other hand, make it a priority to structure and release their data in formats that are both accessible and queryable.

Civic data initiatives also deviate from general purpose information commons such as Wikipedia. Because they consistently engage with problems, closely watch a particular societal issue, make frequent updates,even record from the field to generate and organize highly granular data about the matter….

Several civic data initiatives generate data on variety of issues at different geographies, scopes, and scales. The non-exhaustive list below have information on founders, data sources, and financial support. It is sorted according to each initiative’s founding year. Please send your suggestions to contact at graphcommons.com. See more detailed information and updates on the spreadsheet of civic data initiatives.

Open Secrets tracks data about the money flow in the US government, so it becomes more accessible for journalists, researchers, and advocates.Founded as a non-profit in 1983 by Center for Responsive Politics, gets support from variety of institutions.

PolitiFact is a fact-checking website that rates the accuracy of claims by elected officials and others who speak up in American politics. Uses on-the-record interviews as its data source. Founded in 2007 as a non-profit organization by Tampa Bay Times. Supported by Democracy Fund, Bill &Melinda Gates Foundation, John S. and James L. Knight Foundation, FordFoundation, Knight Foundation, Craigslist Charitable Fund, and the CollinsCenter for Public Policy…..

La Fabrique de La loi (The Law Factory) maps issues of local-regional socio-economic development, public investments, and ecology in France.Started in 2014, the project builds a database by tracking bills from government sources, provides a search engine as well as an API. The partners of the project are CEE Sciences Po, médialab Sciences Po, RegardsCitoyens, and Density Design.

Mapping Media Freedom identifies threats, violations and limitations faced by members of the press throughout European Union member states,candidates for entry and neighbouring countries. Initiated by Index onCensorship and European Commission in 2004, the project…(More)”

Transparency and the open society: Practical lessons for effective policy

Curated on Posted on by Stefaan Verhulst

Book by Roger Taylor and Tim Kelsey: “Greater transparency is increasingly seen as the answer to a wide range of social issues by governments, NGOs and businesses around the world. However, evidence of its impact is mixed. Using case studies from around the world including India, Tanzania, the UK and US, Transparency and the open society surveys the adoption of transparency globally, providing an essential framework for assessing its likely performance as a policy and the steps that can be taken to make it more effective. It addresses the role of transparency in the context of growing use by governments and businesses of surveillance and database driven decision making. The book is written for anyone involved in the use of transparency whether campaigning from outside or working inside government or business to develop policies….(More)”