Mozilla Location Service: crowdsourcing data to help devices find your location without GPS

Curated on Posted on by Stefaan Verhulst

“The Mozilla Location Service is an experimental pilot project to provide geolocation lookups based on publicly observable cell tower and WiFi access point information. Currently in its early stages, it already provides basic service coverage of select locations thanks to our early adopters and contributors.
A world map showing areas with location data. Map data provided by mapbox / OpenStreetMap.
While many commercial services exist in this space, there’s currently no large public service to provide this crucial part of any mobile ecosystem. Mobile phones with a weak GPS signal and laptops without GPS hardware can use this service to quickly identify their approximate location. Even though the underlying data is based on publicly accessible signals, geolocation data is by its very nature personal and privacy sensitive. Mozilla is committed to improving the privacy aspects for all participants of this service offering.
If you want to help us build our service, you can install our dedicated Android MozStumbler and enjoy competing against others on our leaderboard or choose to contribute anonymously. The service is evolving rapidly, so expect to see a more full featured experience soon. For an overview of the current experience, you can head over to the blog of Soledad Penadés, who wrote a far better introduction than we did.
We welcome any ideas or concerns about this project and would love to hear any feedback or experience you might have. Please contact us either on our dedicated mailing list or come talk to us in our IRC room #geo on Mozilla’s IRC server.
For more information please follow the links on our project page.”

When Nudges Fail: Slippery Defaults

Curated on Posted on by Stefaan Verhulst

New paper by Lauren E. Willis “Inspired by the success of “automatic enrollment” in increasing participation in defined contribution retirement savings plans, policymakers have put similar policy defaults in place in a variety of other contexts, from checking account overdraft coverage to home-mortgage escrows. Internet privacy appears poised to be the next arena. But how broadly applicable are the results obtained in the retirement savings context? Evidence from other contexts indicates two problems with this approach: the defaults put in place by the law are not always sticky, and the people who opt out may be those who would benefit the most from the default. Examining the new default for consumer checking account overdraft coverage reveals that firms can systematically undermine each of the mechanisms that might otherwise operate to make defaults sticky. Comparing the retirement-savings default to the overdraft default, four boundary conditions on the use of defaults as a policy tool are apparent: policy defaults will not be sticky when (1) motivated firms oppose them, (2) these firms have access to the consumer, (3) consumers find the decision environment confusing, and (4) consumer preferences are uncertain. Due to constitutional and institutional constraints, government regulation of the libertarian-paternalism variety is unlikely to be capable of overcoming these bounds. Therefore, policy defaults intended to protect individuals when firms have the motivation and means to move consumers out of the default are unlikely to be effective unless accompanied by substantive regulation. Moreover, the same is likely to be true of “nudges” more generally, when motivated firms oppose them.”

Selected Readings on Linked Data and the Semantic Web

Curated on Posted on by Andrew Young

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

Linked Data and the Semantic Web movement are seeking to make our growing body of digital knowledge and information more interconnected, searchable, machine-readable and useful. First introduced by the W3C, Sir Tim Berners-Lee, Christian Bizer and Tom Heath define Linked Data as “data published to the Web in such a way that it is machine-readable, its meaning is explicitly defined, it is linked to other external data sets, and can in turn be linked to from external datasets.” In other words, Linked Data and the Semantic Web seek to do for data what the Web did for documents. Additionally, the evolving capability of linking together different forms of data is fueling the potentially transformative rise of social machines – “processes in which the people do the creative work and the machine does the administration.”

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Alani, Harith, David Dupplaw, John Sheridan, Kieron O’Hara, John Darlington, Nigel Shadbolt, and Carol Tullo. “Unlocking the Potential of Public Sector Information with Semantic Web Technology,” 2007. http://bit.ly/17fMbCt.

  • This paper explores the potential of using Semantic Web technology to increase the value of public sector information already in existence.
  • The authors note that, while “[g]overnments often hold very rich data and whilst much of this information is published and available for re-use by others, it is often trapped by poor data structures, locked up in legacy data formats or in fragmented databases. One of the great benefits that Semantic Web (SW) technology offers is facilitating the large scale integration and sharing of distributed data sources.”
  • They also argue that Linked Data and the Semantic Web are growing in use and visibility in other sectors, but government has been slower to adapt: “The adoption of Semantic Web technology to allow for more efficient use of data in order to add value is becoming more common where efficiency and value-added are important parameters, for example in business and science. However, in the field of government there are other parameters to be taken into account (e.g. confidentiality), and the cost-benefit analysis is more complex.” In spite of that complexity, the authors’ work “was intended to show that SW technology could be valuable in the governmental context.”

Berners-Lee, Tim, James Hendler, and Ora Lassila. “The Semantic Web.” Scientific American 284, no. 5 (2001): 28–37. http://bit.ly/Hhp9AZ.

  • In this article, Sir Tim Berners-Lee, James Hendler and Ora Lassila introduce the Semantic Web, “a new form of Web content that is meaningful to computers [and] will unleash a revolution of new possibilities.”
  • The authors argue that the evolution of linked data and the Semantic Web “lets anyone express new concepts that they invent with minimal effort. Its unifying logical language will enable these concepts to be progressively linked into a universal Web. This structure will open up the knowledge and workings of humankind to meaningful analysis by software agents, providing a new class of tools by which we can live, work and learn together.”

Bizer, Christian, Tom Heath, and Tim Berners-Lee. “Linked Data – The Story So Far.” International Journal on Semantic Web and Information Systems (IJSWIS) 5, no. 3 (2009): 1–22. http://bit.ly/HedpPO.

  • In this paper, the authors take stock of Linked Data’s challenges, potential and successes close to a decade after its introduction. They build their argument for increasingly linked data by referring to the incredible value creation of the Web: “Despite the inarguable benefits the Web provides, until recently the same principles that enabled the Web of documents to flourish have not been applied to data.”
  • The authors expect that “Linked Data will enable a significant evolutionary step in leading the Web to its full potential” if a number of research challenges can be adequately addressed, both technical, like interaction paradigms and data fusion; and non-technical, like licensing, quality and privacy.

Ding, Li, Dominic Difranzo, Sarah Magidson, Deborah L. Mcguinness, and Jim Hendler. Data-Gov Wiki: Towards Linked Government Data, n.d. http://bit.ly/1h3ATHz.

  • In this paper, the authors “investigate the role of Semantic Web technologies in converting, enhancing and using linked government data” in the context of Data-gov Wiki, a project that attempts to integrate datasets found at Data.gov into the Linking Open Data (LOD) cloud.
  • The paper features discussion and “practical strategies” based on four key issue areas: Making Government Data Linkable, Linking Government Data, Supporting the Use of Linked Government Data and Preserving Knowledge Provenance.

Kalampokis, Evangelos, Michael Hausenblas, and Konstantinos Tarabanis. “Combining Social and Government Open Data for Participatory Decision-Making.” In Electronic Participation, edited by Efthimios Tambouris, Ann Macintosh, and Hans de Bruijn, 36–47. Lecture Notes in Computer Science 6847. Springer Berlin Heidelberg, 2011. http://bit.ly/17hsj4a.

  • This paper presents a proposed data architecture for “supporting participatory decision-making based on the integration and analysis of social and government data.” The authors believe that their approach will “(i) allow decision makers to understand and predict public opinion and reaction about specific decisions; and (ii) enable citizens to inadvertently contribute in decision-making.”
  • The proposed approach, “based on the use of the linked data paradigm,” draws on subjective social data and objective government data in two phases: Data Collection and Filtering and Data Analysis. “The aim of the former phase is to narrow social data based on criteria such as the topic of the decision and the target group that is affected by the decision. The aim of the latter phase is to predict public opinion and reactions using independent variables related to both subjective social and objective government data.”

Rady, Kaiser. Publishing the Public Sector Legal Information in the Era of the Semantic Web. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 2012. http://bit.ly/17fMiOp.

  • Following an EU directive calling for the release of public sector information by member states, this study examines the “uniqueness” of creating and publishing primary legal source documents on the web and highlights “the most recent technological strategy used to structure, link and publish data online (the Semantic Web).”
  • Rady argues for public sector legal information to be published as “open-linked-data in line with the new approach for the web.” He believes that if data is created and published in this form, “the data will be more independent from devices and applications and could be considered as a component of [a] big information system. That because, it will be well-structured, classified and has the ability to be used and utilized in various combinations to satisfy specific user requirements.”

Shadbolt, Nigel, Kieron O’Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, and m.c. schraefel. “Linked Open Government Data: Lessons from Data.gov.uk.” IEEE Intelligent Systems 27, no. 3 (May 2012): 16–24. http://bit.ly/1cgdH6R.

  • In this paper, the authors view Open Government Data (OGD) as an “opportunity and a challenge for the LDW [Linked Data Web]. The opportunity is to grow by linking with PSI [Public Sector Information] – real-world, useful information with good provenance. The challenge is to manage the sudden influx of heterogeneous data, often with minimal semantics and structure, tailored to highly specific task contexts.
  • As the linking of OGD continues, the authors argue that, “Releasing OGD is not solely a technical problem, although it presents technical challenges. OGD is not a rigid government IT specification, but it demands productive dialogue between data providers, users, and developers. We should expect a ‘perpetual beta,’ in which best practice, technical development, innovative use of data, and citizen-centric politics combine to drive data-release programs.”
  • Despite challenges, the authors believe that, “Integrating OGD onto the LDW will vastly increase the scope and richness of the LDW. A reciprocal benefit is that the LDW will provide additional resources and context to enrich OGD. Here, we see the network effect in action, with resources mutually adding value to one another.”

Vitale, Michael, Anni Rowland-Campbell, Valentina Cardo, and Peter Thompson. “The Implications of Government as a ‘Social Machine’ for Making and Implementing Market-based Policy.” Intersticia, September 2013. http://bit.ly/HhMzqD.

  • This report from the Australia and New Zealand School of Government (ANZSOG) explores the concept of government as a social machine. The authors draw on the definition of a social machine proposed by Sir Nigel Shadbolt et al. – a system where “human and computational intelligence coalesce in order to achieve a given purpose” – to describe a “new approach to the relationship between citizens and government, facilitated by technological systems which are increasingly becoming intuitive, intelligent and ‘social.'”
  • The authors argue that beyond providing more and varied data to government, the evolving concept of government as a social machine as the potential to alter power dynamics, address the growing lack of trust in public institutions and facilitate greater public involvement in policy-making.

Big Data

Curated on Posted on by Stefaan Verhulst

Special Report on Big Data by Volta – A newsletter on Science, Technology and Society in Europe:  “Locating crime spots, or the next outbreak of a contagious disease, Big Data promises benefits for society as well as business. But more means messier. Do policy-makers know how to use this scale of data-driven decision-making in an effective way for their citizens and ensure their privacy?90% of the world’s data have been created in the last two years. Every minute, more than 100 million new emails are created, 72 hours of new video are uploaded to YouTube and Google processes more than 2 million searches. Nowadays, almost everyone walks around with a small computer in their pocket, uses the internet on a daily basis and shares photos and information with their friends, family and networks. The digital exhaust we leave behind every day contributes to an enormous amount of data produced, and at the same time leaves electronic traces that contain a great deal of personal information….
Until recently, traditional technology and analysis techniques have not been able to handle this quantity and type of data. But recent technological developments have enabled us to collect, store and process data in new ways. There seems to be no limitations, either to the volume of data or technology for storing and analyzing them. Big Data can map a driver’s sitting position to identify a car thief, it can use Google searches to predict outbreaks of the H1N1 flu virus, it can data-mine Twitter to predict the price of rice or use mobile phone top-ups to describe unemployment in Asia.
The word ‘data’ means ‘given’ in Latin. It commonly refers to a description of something that can be recorded and analyzed. While there is no clear definition of the concept of ‘Big Data’, it usually refers to the processing of huge amounts and new types of data that have not been possible with traditional tools.

‘The new development is not necessarily that there are so much more data. It’s rather that data is available to us in a new way.’

The notion of Big Data is kind of misleading, argues Robindra Prabhu, a project manager at the Norwegian Board of Technology. “The new development is not necessarily that there are so much more data. It’s rather that data is available to us in a new way. The digitalization of society gives us access to both ‘traditional’, structured data – like the content of a database or register – and unstructured data, for example the content in a text, pictures and videos. Information designed to be read by humans is now also readable by machines. And this development makes a whole new world of  data gathering and analysis available. Big Data is exciting not just because of the amount and variety of data out there, but that we can process data about so much more than before.”

Open data: Unlocking innovation and performance with liquid information

Curated on Posted on by Stefaan Verhulst

New report by McKinsey Global Institute:“Open data—machine-readable information, particularly government data, that’s made available to others—has generated a great deal of excitement around the world for its potential to empower citizens, change how government works, and improve the delivery of public services. It may also generate significant economic value, according to a new McKinsey report.1 Our research suggests that seven sectors alone could generate more than $3 trillion a year in additional value as a result of open data, which is already giving rise to hundreds of entrepreneurial businesses and helping established companies to segment markets, define new products and services, and improve the efficiency and effectiveness of operations.

Although the open-data phenomenon is in its early days, we see a clear potential to unlock significant economic value by applying advanced analytics to both open and proprietary knowledge. Open data can become an instrument for breaking down information gaps across industries, allowing companies to share benchmarks and spread best practices that raise productivity. Blended with proprietary data sets, it can propel innovation and help organizations replace traditional and intuitive decision-making approaches with data-driven ones. Open-data analytics can also help uncover consumer preferences, allowing companies to improve new products and to uncover anomalies and needless variations. That can lead to leaner, more reliable processes.
However, investments in technology and expertise are required to use the data effectively. And there is much work to be done by governments, companies, and consumers to craft policies that protect privacy and intellectual property, as well as establish standards to speed the flow of data that is not only open but also “liquid.” After all, consumers have serious privacy concerns, and companies are reluctant to share proprietary information—even when anonymity is assured—for fear of losing competitive advantage…
See also Executive Summary and Full Report”

Making government simpler is complicated

Curated on Posted on by Stefaan Verhulst

Mike Konczal in The Washington Post: “Here’s something a politician would never say: “I’m in favor of complex regulations.” But what would the opposite mean? What would it mean to have “simple” regulations?

There are two definitions of “simple” that have come to dominate liberal conversations about government. One is the idea that we should make use of “nudges” in regulation. The other is the idea that we should avoid “kludges.” As it turns out, however, these two definitions conflict with each other —and the battle between them will dominate conversations about the state in the years ahead.

The case for “nudges”

The first definition of a “simple” regulation is one emphasized in Cass Sunstein’s recent book titled Simpler: The Future of Government (also see here). A simple policy is one that simply “nudges” people into one choice or another using a variety of default rules, disclosure requirements, and other market structures. Think, for instance, of rules that require fast-food restaurants to post calories on their menus, or a mortgage that has certain terms clearly marked in disclosures.

These sorts of regulations are deemed “choice preserving.” Consumers are still allowed to buy unhealthy fast-food meals or sign up for mortgages they can’t reasonably afford. The regulations are just there to inform people about their choices. These rules are designed to keep the market “free,” where all possibilities are ultimately possible, although there are rules to encourage certain outcomes.
In his book, however, Sunstein adds that there’s another very different way to understand the term “simple.” What most people mean when they think of simple regulations is a rule that is “simple to follow.” Usually a rule is simple to follow because it outright excludes certain possibilities and thus ensures others. Which means, by definition, it limits certain choices.

The case against “kludges”
This second definition of simple plays a key role in political scientist Steve Teles’ excellent recent essay, “Kludgeocracy in America.” For Teles, a “kludge” is a “clumsy but temporarily effective” fix for a policy problem. (The term comes from computer science.) These kludges tend to pile up over time, making government cumbersome and inefficient overall.
Teles focuses on several ways that kludges are introduced into policy, with a particularly sharp focus on overlapping jurisdictions and the related mess of federal and state overlap in programs. But, without specifically invoking it, he also suggests that a reliance on “nudge” regulations can lead to more kludges.
After all, non-kludge policy proposal is one that will be simple to follow and will clearly cause a certain outcome, with an obvious causality chain. This is in contrast to a web of “nudges” and incentives designed to try and guide certain outcomes.

Why “nudges” aren’t always simpler
The distinction between the two is clear if we take a specific example core to both definitions: retirement security.
For Teles, “one of the often overlooked benefits of the Social Security program… is that recipients automatically have taxes taken out of their paychecks, and, then without much effort on their part, checks begin to appear upon retirement. It’s simple and direct. By contrast, 401(k) retirement accounts… require enormous investments of time, effort, and stress to manage responsibly.”

Yet 401(k)s are the ultimately fantasy laboratory for nudge enthusiasts. A whole cottage industry has grown up around figuring out ways to default people into certain contributions, on designing the architecture of choices of investments, and trying to effortlessly and painlessly guide people into certain savings.
Each approach emphasizes different things. If you want to focus your energy on making people better consumers and market participations, expanding our government’s resources and energy into 401(k)s is a good choice. If you want to focus on providing retirement security directly, expanding Social Security is a better choice.
The first is “simple” in that it doesn’t exclude any possibility but encourages market choices. The second is “simple” in that it is easy to follow, and the result is simple as well: a certain amount of security in old age is provided directly. This second approach understands the government as playing a role in stopping certain outcomes, and providing for the opposite of those outcomes, directly….

Why it’s hard to create “simple” regulations
Like all supposed binaries this is really a continuum. Taxes, for instance, sit somewhere in the middle of the two definitions of “simple.” They tend to preserve the market as it is but raise (or lower) the price of certain goods, influencing choices.
And reforms and regulations are often most effective when there’s a combination of these two types of “simple” rules.
Consider an important new paper, “Regulating Consumer Financial Products: Evidence from Credit Cards,” by Sumit Agarwal, Souphala Chomsisengphet, Neale Mahoney and Johannes Stroebel. The authors analyze the CARD Act of 2009, which regulated credit cards. They found that the nudge-type disclosure rules “increased the number of account holders making the 36-month payment value by 0.5 percentage points.” However, more direct regulations on fees had an even bigger effect, saving U.S. consumers $20.8 billion per year with no notable reduction in credit access…..
The balance between these two approaches of making regulations simple will be front and center as liberals debate the future of government, whether they’re trying to pull back on the “submerged state” or consider the implications for privacy. The debate over the best way for government to be simple is still far from over.”

Are We Puppets in a Wired World?

Curated on Posted on by Stefaan Verhulst

Sue Halpern in The New York Review of Books: “Also not obvious was how the Web would evolve, though its open architecture virtually assured that it would. The original Web, the Web of static homepages, documents laden with “hot links,” and electronic storefronts, segued into Web 2.0, which, by providing the means for people without technical knowledge to easily share information, recast the Internet as a global social forum with sites like Facebook, Twitter, FourSquare, and Instagram.
Once that happened, people began to make aspects of their private lives public, letting others know, for example, when they were shopping at H+M and dining at Olive Garden, letting others know what they thought of the selection at that particular branch of H+M and the waitstaff at that Olive Garden, then modeling their new jeans for all to see and sharing pictures of their antipasti and lobster ravioli—to say nothing of sharing pictures of their girlfriends, babies, and drunken classmates, or chronicling life as a high-paid escort, or worrying about skin lesions or seeking a cure for insomnia or rating professors, and on and on.
The social Web celebrated, rewarded, routinized, and normalized this kind of living out loud, all the while anesthetizing many of its participants. Although they likely knew that these disclosures were funding the new information economy, they didn’t especially care…
The assumption that decisions made by machines that have assessed reams of real-world information are more accurate than those made by people, with their foibles and prejudices, may be correct generally and wrong in the particular; and for those unfortunate souls who might never commit another crime even if the algorithm says they will, there is little recourse. In any case, computers are not “neutral”; algorithms reflect the biases of their creators, which is to say that prediction cedes an awful lot of power to the algorithm creators, who are human after all. Some of the time, too, proprietary algorithms, like the ones used by Google and Twitter and Facebook, are intentionally biased to produce results that benefit the company, not the user, and some of the time algorithms can be gamed. (There is an entire industry devoted to “optimizing” Google searches, for example.)
But the real bias inherent in algorithms is that they are, by nature, reductive. They are intended to sift through complicated, seemingly discrete information and make some sort of sense of it, which is the definition of reductive.”
Books reviewed:

From open data to open democracy

Curated on Posted on by Stefaan Verhulst

Article by : “Such debates further underscore the complexities of open data and where it might lead. While open data may be viewed by some inside and outside government as a technically-focused and largely incremental project based upon information formatting and accessibility (with the degree of openness subject to a myriad of security and confidentiality provisions), such an approach greatly limits its potential. Indeed, the growing ubiquity of mobile and smart devices, the advent of open source operating systems and social media platforms, and the growing commitment by governments themselves to expansive public engagement objectives, all suggest a widening scope.
Yet, what will incentivize the typical citizen to access open data and to partake in collective efforts to create public value? It is here where our digital culture may well fall short, emphasizing individualized service and convenience at the expense of civic responsibility and community-mindedness. For one American academic, this “citizenship deficit” erodes democratic legitimacy and renders our politics more polarized and less discursive. For other observers in Europe, notions of the digital divide are giving rise to new “data divides.”
The politics and practicalities of data privacy often bring further confusion. While privacy advocates call for greater protection and a culture of data activism among Internet users themselves, the networked ethos of online communities and commercialization fuels speed and sharing, often with little understanding of the ramifications of doing so. Differences between consumerism and citizenship are subtle yet profoundly important, while increasingly blurred and overlooked.
A key conundrum provincially and federally, within the Westminster confines of parliamentary democracy, is that open data is being hatched mainly from within the executive branch, whereas the legislative branch watches and withers. In devising genuine democratic openness, politicians and their parties must do more than post expenses online: they must become partners and advocates for renewal. A lesson of open source technology, however, is that systemic change demands an informed and engaged civil society, disgruntled with the status quo but also determined to act anew.
Most often, such actions are highly localized, even in a virtual world, giving rise to the purpose and meaning of smarter and more intelligent communities. And in Canada it bears noting that we see communities both large and small embracing open data and other forms of online experimentation such as participatory budgeting. It is often within small but connected communities where a virtuous cycle of online and in-person identities and actions can deepen and impact decision-making most directly.
How, then, do we reconcile traditional notions of top-down political federalism and national leadership with this bottom-up approach to community engagement and democratic renewal? Shifting from open data to open democracy is likely to be an uneven, diverse, and at times messy affair. Better this way than attempting to ordain top-down change in a centralized and standardized manner.”

Our Privacy Problem is a Democracy Problem in Disguise

Curated on Posted on by Stefaan Verhulst

Evgeny Morozov in MIT Technology Review: “Intellectually, at least, it’s clear what needs to be done: we must confront the question not only in the economic and legal dimensions but also in a political one, linking the future of privacy with the future of democracy in a way that refuses to reduce privacy either to markets or to laws. What does this philosophical insight mean in practice?

First, we must politicize the debate about privacy and information sharing. Articulating the existence—and the profound political consequences—of the invisible barbed wire would be a good start. We must scrutinize data-intensive problem solving and expose its occasionally antidemocratic character. At times we should accept more risk, imperfection, improvisation, and inefficiency in the name of keeping the democratic spirit alive.
Second, we must learn how to sabotage the system—perhaps by refusing to self-track at all. If refusing to record our calorie intake or our whereabouts is the only way to get policy makers to address the structural causes of problems like obesity or climate change—and not just tinker with their symptoms through nudging—information boycotts might be justifiable. Refusing to make money off your own data might be as political an act as refusing to drive a car or eat meat. Privacy can then reëmerge as a political instrument for keeping the spirit of democracy alive: we want private spaces because we still believe in our ability to reflect on what ails the world and find a way to fix it, and we’d rather not surrender this capacity to algorithms and feedback loops.
Third, we need more provocative digital services. It’s not enough for a website to prompt us to decide who should see our data. Instead it should reawaken our own imaginations. Designed right, sites would not nudge citizens to either guard or share their private information but would reveal the hidden political dimensions to various acts of information sharing. We don’t want an electronic butler—we want an electronic provocateur. Instead of yet another app that could tell us how much money we can save by monitoring our exercise routine, we need an app that can tell us how many people are likely to lose health insurance if the insurance industry has as much data as the NSA, most of it contributed by consumers like us. Eventually we might discern such dimensions on our own, without any technological prompts.
Finally, we have to abandon fixed preconceptions about how our digital services work and interconnect. Otherwise, we’ll fall victim to the same logic that has constrained the imagination of so many well-­meaning privacy advocates who think that defending the “right to privacy”—not fighting to preserve democracy—is what should drive public policy. While many Internet activists would surely argue otherwise, what happens to the Internet is of only secondary importance. Just as with privacy, it’s the fate of democracy itself that should be our primary goal.

GitHub and Government

Curated on Posted on by Stefaan Verhulst

New site: “Make government better, together. Stories of open source, open data, and open government.
This site is an open source effort to showcase best practices of open sourcing government. See something that you think could be better? Want to submit your own story? Simply fork the project and submit a pull request.
…

Ready to get started on GitHub? Here are some ideas that are easy to get your feet wet with.

Feedback Repository

GitHub’s about connecting with developers. Whether you’re an API publishing pro, or just getting started, creating a “feedback” repository can go a long way to connect your organization with the community. Get feedback from current and potential data consumers by creating a specific repository for them to contribute ideas and suggestions for types of data or other information they’d like to see opened. Here’s how:

  1. Create a new repository
    • Choose your organization as the Owner
    • Name the repository “feedback” or similar
    • Click the checkbox to automatically create a README.md file
  2. Set up your Readme
    • Click README.md within your newly created repository
    • Click Edit
    • Introduce yourself, describe why you’ve joined GitHub, what you’re hoping to do and what you’d like to learn from the development community. Encourage them to leave feedback through issues on the repository.

Sample text for your README.md:

# City of Gotham Feedback
We've just joined GitHub and want to know what data would be interesting to our development community?
Leave us comments via issues!

Open source a Dataset

Open sourcing a dataset can be as simple as uploading a .csv to GitHub and letting people know about it. Rather than publishing data as a zip file on your website or an FTP server, you can add the files through the GitHub.com web interface, or via the GitHub for Windows or GitHub for Mac native clients. Create a new repository to store your datasets – in many cases, it’s as easy as drag, drop, sync.
GitHub can host any file type (although open, non-binary files like .csvs tend to work best). Plus, GitHub supports rendering certain open data formats interactively such as the popular geospacial .geojson format. Once uploaded, citizens can view the files, and can even open issues or submit pull requests with proposed fixes.

Explore Open Source Civic Apps

There are many open source applications freely available on GitHub that were built just for government. Check them out, and see if it fits a need. Here are some examples:

  • Adopt-a – This open source web app was created for the City of Boston in 2011 by Code for America fellows. It allows residents to “adopt” a hydrant and make sure it’s clear of snow in the winter so that emergency crews can locate them when needed. It has since been adopted in Chicago (for sidewalks), Seattle (for storm drains), and Honolulu (for tsunami sirens).
  • StreetMix – Another creation of Code for America fellows (2013) this website, www.streetmix.net, allows anyone to create street sections in a way that is not only beautiful but educational, too. No downloading, no installing, no paying – make and save your creations right at the website. Great for internal or public community planning meetings.
  • We The People – We The People, the White House’s petitions application hosted at petitions.whitehouse.gov is a Drupal module to allow citizens to submit and digitally sign petitions.

Open source something small

Chances are you’ve got something small you can open source. Check in with your web or new media team, and see if they’ve got something they’ve been dying to share or blog about, no matter how small. It can be snippet of analytics code, or maybe a small script used internally. It doesn’t even have to be code.
Post your website’s privacy policy, comment moderation policy, or terms of service and let the community weigh in before your next edit. No matter how small it is, getting your first open source project going is a great first step.

Improve an existing project

Does you agency use an existing open source project to conduct its own business? Open an issue on the project’s repository with a feature request or a bug you spot. Better yet, fork the project, and submit your improvements. Even if it’s one or two lines of code, such examples are great to blog about to showcase your efforts.
Don’t forget, this site is an open source project, too. Making an needed edit is another great way to get started.”