A report by the National Neighborhood Indicators Partnership (NNIP): “A large share of public decisions that shape the fundamental character of American life are made at the local level; for example, decisions about controlling crime, maintaining housing quality, targeting social services, revitalizing low-income neighborhoods, allocating health care, and deploying early childhood programs. Enormous benefits would be gained if a much larger share of these decisions were based on sound data and analysis.
In the mid-1990s, a movement began to address the need for data for local decisionmaking.Civic leaders in several cities funded local groups to start assembling neighborhood and address-level data from multiple local agencies. For the first time, it became possible to track changing neighborhood conditions, using a variety of indicators, year by year between censuses. These new data intermediaries pledged to use their data in practical ways to support policymaking and community building and give priority to the interests of distressed neighborhoods. Their theme was “democratizing data,” which in practice meant making the data accessible to residents and community groups (Sawicki and Craig 1996).
The initial groups that took on this work formed the National Neighborhood Indicators Partnership (NNIP) to further develop these capacities and spread them to other cities. By 2012, NNIP partners were established in 37 cities, and similar capacities were in development in a number of others. The Urban Institute (UI) serves as the secretariat for the network. This report documents a strategic planning process undertaken by NNIP in 2012 and early 2013. The network’s leadership and funders re-examined the NNIP model in the context of 15 years of local partner experiences and the dramatic changes in technology and policy approaches that have occurred over that period. The first three sections explain NNIP functions and institutional structures and examine the potential role for NNIP in advancing the community information field in today’s environment.”
Daren C. Brabham for IBM Center for The Business of Government: “The growing interest in “engaging the crowd” to identify or develop innovative solutions to public problems has been inspired by similar efforts in the commercial world. There, crowdsourcing has been successfully used to design innovative consumer products or solve complex scientific problems, ranging from custom-designed T-shirts to mapping genetic DNA strands.
The Obama administration, as well as many state and local governments, have been adapting these crowdsourcing techniques with some success. This report provides a strategic view of crowdsourcing and identifies four specific types:
- Type 1: Knowledge Discovery and Management. Collecting knowledge reported by an on-line community, such as the reporting of earth tremors or potholes to a central source.
- Type 2: Distributed Human Intelligence Tasking. Distributing “micro-tasks” that require human intelligence to solve, such as transcribing handwritten historical documents into electronic files.
- Type 3: Broadcast Search. Broadcasting a problem-solving challenge widely on the internet and providing an award for solution, such as NASA’s prize for an algorithm to predict solar flares
- Type 4: Peer-Vetted Creative Production. Creating peer-vetted solutions, where an on-line community both proposes possible solutions and is empowered to collectively choose among the solutions.
By understanding the different types, which require different approaches, public managers will have a better chance of success. Dr. Brabham focuses on the strategic design process rather than on the specific technical tools that can be used for crowdsourcing. He sets forth ten emerging best practices for implementing a crowdsourcing initiative.”
Jane Wakefield from BBC News: “In the future everything in a city, from the electricity grid, to the sewer pipes to roads, buildings and cars will be connected to the network. Buildings will turn off the lights for you, self-driving cars will find you that sought-after parking space, even the rubbish bins will be smart. But how do we get to this smarter future. Who will be monitoring and controlling the sensors that will increasingly be on every building, lamp-post and pipe in the city?…
There is another chapter in the smart city story – and this one is being written by citizens, who are using apps, DIY sensors, smartphones and the web to solve the city problems that matter to them.
Don’t Flush Me is a neat little DIY sensor and app which is single-handedly helping to solve one of New York’s biggest water issues.
Every time there is heavy rain in the city, raw sewage is pumped into the harbour, at a rate of 27 billion gallons each year.
Using an Arduino processor, a sensor which measures water levels in the sewer overflows and a smart phone app, Don’t Flush Me lets people know when it is ‘safe to flush’.
Meanwhile Egg, a community-led sensor network, is alerting people to an often hidden problem in our cities.
Researchers estimate that two million people die each year as a result of air pollution and as cities get more over-crowded, the problem is likely to get worse.
Egg is compiling data about air quality by selling cheap sensor which people put outside their homes where they collect readings of green gases, nitrogen oxide (NO2) and carbon monoxide (CO)….
The reality is that most smart city projects are currently pretty small scale – creating tech hubs or green areas of the city, experimenting with smart electricity grids or introducing electric buses or bike-sharing schemes.”
Todd Sherer, Chief Executive Officer of The Michael J. Fox Foundation for Parkinson’s Research, in Forbes: “he problem is, we all still work in a system that feeds on secrecy and competition. It’s hard enough work just to dream up win/win collaborative structures; getting them off the ground can feel like pushing a boulder up a hill. Yet there is no doubt that the realities of today’s research environment — everything from the accumulation of big data to the ever-shrinking availability of funds — demand new models for collaboration. Call it “collaboration 2.0.”…I share a few recent examples in the hope of increasing the reach of these initiatives, inspiring others like them, and encouraging frank commentary on how they’re working.
The successes of collaborations in the traditional sense, coupled with advanced techniques such as genomic sequencing, have yielded masses of data. Consortia of clinical sites around the world are working together to collect and characterize data and biospecimens through standardized methods, leading to ever-larger pools — more like Great Lakes — of data. Study investigators draw their own conclusions, but there is so much more to discover than any individual lab has the bandwidth for….
A great way to grow engagement with resources you’re willing to share? Ask for it. Collaboration 2.0 casts a wide net. We dipped our toe in the crowdsourcing waters earlier this year with our Parkinson’s Data Challenge, which asked anyone interested to download a set of data that had been collected from PD patients and controls using smart phones. …
Cross-Disciplinary Collaboration 2.0
The more we uncover about the interconnectedness and complexity of the human system, the more proof we are gathering that findings and treatments for one disease may provide invaluable insights for others. We’ve seen some really intriguing crosstalk between the Parkinson’s and Alzheimer’s disease research communities recently…
The results should be: More ideas. More discovery. Better health.”
PSFK: “Because most of us only see the finished product when it comes to 3D printing projects – it’s easy to forget that things can, and do, go wrong when it comes to this miracle technology.
3D printing is constantly evolving, reaching exciting new heights, and touching every industry you can think of – but all this progress has left a trail of mangled plastic, and a devastated machines in it’s wake.
The Art of 3D Print Failure is a Flickr group that aims to document this failure, because after all, mistakes are how we learn, and how we make sure the same thing doesn’t happen the next time around. It can also prevent mistakes from happening to those who are new to 3D printing, before they even make them!”
Paper by Hector J. Levesque: “The science of AI is concerned with the study of intelligent forms of behaviour in computational terms. But what does it tell us when a good semblance of a behaviour can be achieved using cheap tricks that seem to have little to do with what we intuitively imagine intelligence to be? Are these intuitions wrong, and is intelligence really just a bag of tricks? Or are the philosophers right, and is a behavioural understanding of intelligence simply too weak? I think both of these are wrong. I suggest in the context of question-answering that what matters when it comes to the science of AI is not a good semblance of intelligent behaviour at all, but the behaviour itself, what it depends on, and how it can be achieved. I go on to discuss two major hurdles that I believe will need to be cleared.”
New paper by Sanmay Das, Allen Lavoie, and Malik Magdon-Ismail: “Our reliance on networked, collectively built information is a vulnerability when the quality or reliability of this information is poor. Wikipedia, one such collectively built information source, is often our first stop for information on all kinds of topics; its quality has stood up to many tests, and it prides itself on having a “Neutral Point of View”. Enforcement of neutrality is in the hands of comparatively few, powerful administrators. We find a surprisingly large number of editors who change their behavior and begin focusing more on a particular controversial topic once they are promoted to administrator status. The conscious and unconscious biases of these few, but powerful, administrators may be shaping the information on many of the most sensitive topics on Wikipedia; some may even be explicitly infiltrating the ranks of administrators in order to promote their own points of view. Neither prior history nor vote counts during an administrator’s election can identify those editors most likely to change their behavior in this suspicious manner. We find that an alternative measure, which gives more weight to influential voters, can successfully reject these suspicious candidates. This has important implications for how we harness collective intelligence: even if wisdom exists in a collective opinion (like a vote), that signal can be lost unless we carefully distinguish the true expert voter from the noisy or manipulative voter.”
Thesis by Hollie Russon Gilman: “Participatory Budgeting (PB) has expanded to over 1,500 municipalities worldwide since its inception in Porto Alege, Brazil in 1989 by the leftist Partido dos Trabalhadores (Workers’ Party). While PB has been adopted throughout the world, it has yet to take hold in the United States. This dissertation examines the introduction of PB to the United States with the first project in Chicago in 2009, and proceeds with an in-depth case study of the largest implementation of PB in the United States: Participatory Budgeting in New York City. I assess the outputs of PB in the United States including deliberations, governance, and participation. I argue that PB produces better outcomes than the status quo budget process in New York City, while also transforming how those who participate understand themselves as citizens, constituents, Council members, civil society leaders and community stakeholders. However, there are serious challenges to participation, including high costs of engagement, process exhaustion, and perils of scalability. I devise a framework for assessment called “citizenly politics,” focusing on: 1) designing participation 2) deliberation 3) participation and 4) potential for institutionalization. I argue that while the material results PB produces are relatively modest, including more innovative projects, PB delivers more substantial non-material or existential results. Existential citizenly rewards include: greater civic knowledge, strengthened relationships with elected officials, and greater community inclusion. Overall, PB provides a viable and informative democratic innovation for strengthening civic engagement within the United States that can be streamlined and adopted to scale.”
Release from the U.S. Department of the Interior, U.S. Geological Survey: “The mapping crowd-sourcing program, known as The National Map Corps (TNMCorps), encourages citizens to collect structures data by adding new features, removing obsolete points, and correcting existing data for The National Map database. Structures being mapped in the project include schools, hospitals, post offices, police stations and other important public buildings.
Since the start of the project in 2012, more than 780 volunteers have made in excess of 13,000 contributions. In addition to basic editing, a second volunteer peer review process greatly enhances the quality of data provided back to The National Map. A few months ago, volunteers in 35 states were actively involved. This final release of states opens up the entire country for volunteer structures enhancement.
To show appreciation of our volunteer’s efforts, The National Map Corps has instituted a recognition program that awards “virtual” badges to volunteers. The badges consist of a series of antique surveying instruments ranging from the Order of the Surveyor’s Chain (25 – 50 points) to the Theodolite Assemblage (2000+ points). Additionally, volunteers are publically acclaimed (with permission) via Twitter, Facebook and Google+….
Tools on TNMCorps website explain how a volunteer can edit any area, regardless of their familiarity with the selected structures, and becoming a volunteer for TNMCorps is easy; go to The National Map Corps website to learn more and to sign up as a volunteer. If you have access to the Internet and are willing to dedicate some time to editing map data, we hope you will consider participating!”
Samuel Arbesman, senior scholar at the Ewing Marion Kauffman Foundation and the author of “The Half-Life of Facts” in the Washington Post: “Big data holds the promise of harnessing huge amounts of information to help us better understand the world. But when talking about big data, there’s a tendency to fall into hyperbole. It is what compels contrarians to write such tweets as “Big Data, n.: the belief that any sufficiently large pile of s— contains a pony.” Let’s deflate the hype.
1. “Big data” has a clear definition.
The term “big data” has been in circulation since at least the 1990s, when it is believed to have originated in Silicon Valley. IBM offers a seemingly simple definition: Big data is characterized by the four V’s of volume, variety, velocity and veracity. But the term is thrown around so often, in so many contexts — science, marketing, politics, sports — that its meaning has become vague and ambiguous….
2. Big data is new.
By many accounts, big data exploded onto the scene quite recently. “If wonks were fashionistas, big data would be this season’s hot new color,” a Reuters report quipped last year. In a May 2011 report, the McKinsey Global Institute declared big data “the next frontier for innovation, competition, and productivity.”
It’s true that today we can mine massive amounts of data — textual, social, scientific and otherwise — using complex algorithms and computer power. But big data has been around for a long time. It’s just that exhaustive datasets were more exhausting to compile and study in the days when “computer” meant a person who performed calculations….
3. Big data is revolutionary.
In their new book, “Big Data: A Revolution That Will Transform How We Live, Work, and Think,”Viktor Mayer-Schonberger and Kenneth Cukier compare “the current data deluge” to the transformation brought about by the Gutenberg printing press.
If you want more precise advertising directed toward you, then yes, big data is revolutionary. Generally, though, it’s likely to have a modest and gradual impact on our lives….
4. Bigger data is better.
In science, some admittedly mind-blowing big-data analyses are being done. In business, companies are being told to “embrace big data before your competitors do.” But big data is not automatically better.
Really big datasets can be a mess. Unless researchers and analysts can reduce the number of variables and make the data more manageable, they get quantity without a whole lot of quality. Give me some quality medium data over bad big data any day…
5. Big data means the end of scientific theories.
Chris Anderson argued in a 2008 Wired essay that big data renders the scientific method obsolete: Throw enough data at an advanced machine-learning technique, and all the correlations and relationships will simply jump out. We’ll understand everything.
But you can’t just go fishing for correlations and hope they will explain the world. If you’re not careful, you’ll end up with spurious correlations. Even more important, to contend with the “why” of things, we still need ideas, hypotheses and theories. If you don’t have good questions, your results can be silly and meaningless.
Having more data won’t substitute for thinking hard, recognizing anomalies and exploring deep truths.”