DATA – Page 549 – The Living Library

(Appropriate) Big Data for Climate Resilience?

Curated on September 13, 2013August 15, 2018 by Stefaan Verhulst

Amy Luers at the Stanford Social Innovation Review: “The answer to whether big data can help communities build resilience to climate change is yes—there are huge opportunities, but there are also risks.

Opportunities

Feedback: Strong negative feedback is core to resilience. A simple example is our body’s response to heat stress—sweating, which is a natural feedback to cool down our body. In social systems, feedbacks are also critical for maintaining functions under stress. For example, communication by affected communities after a hurricane provides feedback for how and where organizations and individuals can provide help. While this kind of feedback used to rely completely on traditional communication channels, now crowdsourcing and data mining projects, such as Ushahidi and Twitter Earthquake detector, enable faster and more-targeted relief.
Diversity: Big data is enhancing diversity in a number of ways. Consider public health systems. Health officials are increasingly relying on digital detection methods, such as Google Flu Trends or Flu Near You, to augment and diversify traditional disease surveillance.
Self-Organization: A central characteristic of resilient communities is the ability to self-organize. This characteristic must exist within a community (see the National Research Council Resilience Report), not something you can impose on it. However, social media and related data-mining tools (InfoAmazonia, Healthmap) can enhance situational awareness and facilitate collective action by helping people identify others with common interests, communicate with them, and coordinate efforts.

Risks

Eroding trust: Trust is well established as a core feature of community resilience. Yet the NSA PRISM escapade made it clear that big data projects are raising privacy concerns and possibly eroding trust. And it is not just an issue in government. For example, Target analyzes shopping patterns and can fairly accurately guess if someone in your family is pregnant (which is awkward if they know your daughter is pregnant before you do). When our trust in government, business, and communities weakens, it can decrease a society’s resilience to climate stress.
Mistaking correlation for causation: Data mining seeks meaning in patterns that are completely independent of theory (suggesting to some that theory is dead). This approach can lead to erroneous conclusions when correlation is mistakenly taken for causation. For example, one study demonstrated that data mining techniques could show a strong (however spurious) correlation between the changes in the S&P 500 stock index and butter production in Bangladesh. While interesting, a decision support system based on this correlation would likely prove misleading.
Failing to see the big picture: One of the biggest challenges with big data mining for building climate resilience is its overemphasis on the hyper-local and hyper-now. While this hyper-local, hyper-now information may be critical for business decisions, without a broader understanding of the longer-term and more-systemic dynamism of social and biophysical systems, big data provides no ability to understand future trends or anticipate vulnerabilities. We must not let our obsession with the here and now divert us from slower-changing variables such as declining groundwater, loss of biodiversity, and melting ice caps—all of which may silently define our future. A related challenge is the fact that big data mining tends to overlook the most vulnerable populations. We must not let the lure of the big data microscope on the “well-to-do” populations of the world make us blind to the less well of populations within cities and communities that have more limited access to smart phones and the Internet.”

Three ways to think of the future…

Curated on September 13, 2013October 10, 2018 by Stefaan Verhulst

Geoff Mulgan’s blog: “Here I suggest three complementary ways of thinking about the future which provide partial protection against the pitfalls.
The shape of the future
First, create your own composite future by engaging with the trends. There are many methods available for mapping the future – from Foresight to scenarios to the Delphi method.
Behind all are implicit views about the shapes of change. Indeed any quantitative exploration of the future uses a common language of patterns (shown in this table above) which summarises the fact that some things will go up, some go down, some change suddenly and some not at all.
All of us have implicit or explicit assumptions about these. But it’s rare to interrogate them systematically and test whether our assumptions about what fits in which category are right.
Let’s start with the J shaped curves. Many of the long-term trends around physical phenomena look J-curved: rising carbon emissions, water useage and energy consumption have been exponential in shape over the centuries. As we know, physical constraints mean that these simply can’t go on – the J curves have to become S shaped sooner or later, or else crash. That is the ecological challenge of the 21^st century.
New revolutions
But there are other J curves, particularly the ones associated with digital technology. Moore’s Law and Metcalfe’s Law describe the dramatically expanding processing power of chips, and the growing connectedness of the world. Some hope that the sheer pace of technological progress will somehow solve the ecological challenges. That hope has more to do with culture than evidence. But these J curves are much faster than the physical ones – any factor that doubles every 18 months achieves stupendous rates of change over decades.
That’s why we can be pretty confident that digital technologies will continue to throw up new revolutions – whether around the Internet of Things, the quantified self, machine learning, robots, mass surveillance or new kinds of social movement. But what form these will take is much harder to predict, and most digital prediction has been unreliable – we have Youtube but not the Interactive TV many predicted (when did you last vote on how a drama should end?); relatively simple SMS and twitter spread much more than ISDN or fibre to the home. And plausible ideas like the long tail theory turned out to be largely wrong.
If the J curves are dramatic but unusual, much more of the world is shaped by straight line trends – like ageing or the rising price of disease that some predict will take costs of healthcare up towards 40 or 50% of GDP by late in the century, or incremental advances in fuel efficiency, or the likely relative growth of the Chinese economy.
Also important are the flat straight lines – the things that probably won’t change in the next decade or two: the continued existence of nation states not unlike those of the 19^th century? Air travel making use of fifty year old technologies?
Great imponderables
If the Js are the most challenging trends, the most interesting ones are the ‘U’s’- the examples of trends bending: like crime which went up for a century and then started going down, or world population that has been going up but could start going down in the later part of this century, or divorce rates which seem to have plateaued, or Chinese labour supply which is forecast to turn down in the 2020s.
No one knows if the apparently remorseless upward trends of obesity and depression will turn downwards. No one knows if the next generation in the West will be poorer than their parents. And no one knows if democratic politics will reinvent itself and restore trust. In every case, much depends on what we do. None of these trends is a fact of nature or an act of God.
That’s one reason why it’s good to immerse yourself in these trends and interrogate what shape they really are. Out of that interrogation we can build a rough mental model and generate our own hypotheses – ones not based on the latest fashion or bestseller but hopefully on a sense of what the data shows and in particular what’s happening to the deltas – the current rates of change of different phenomena.”

Open Access

Curated on September 12, 2013August 3, 2018 by Stefaan Verhulst

Reports by the UK’s House of Commons, Business, Innovation and Skills Committee: “Open access refers to the immediate, online availability of peer reviewed research articles, free at the point of access (i.e. without subscription charges or paywalls). Open access relates to scholarly articles and related outputs. Open data (which is a separate area of Government policy and outside the scope of this inquiry) refers to the availability of the underlying research data itself. At the heart of the open access movement is the principle that publicly funded research should be publicly accessible. Open access expanded rapidly in the late twentieth century with the growth of the internet and digitisation (the transcription of data into a digital form), as it became possible to disseminate research findings more widely, quickly and cheaply.
Whilst there is widespread agreement that the transition to open access is essential in order to improve access to knowledge, there is a lack of consensus about the best route to achieve it. To achieve open access at scale in the UK, there will need to be a shift away from the dominant subscription-based business model. Inevitably, this will involve a transitional period and considerable change within the scholarly publishing market.
For the UK to transition to open access, an effective, functioning and competitive market in scholarly communications will be vital. The evidence we saw over the course of this inquiry shows that this is currently far from the case, with journal subscription prices rising at rates that are unsustainable for UK universities and other subscribers. There is a significant risk that the Government’s current open access policy will inadvertently encourage and prolong the dysfunctional elements of the scholarly publishing market, which are a major barrier to access.
See Volume I and Volume II “

The Tech Intellectuals

Curated on September 11, 2013August 3, 2018 by Stefaan Verhulst

New Essay by Henry Farrell in Democracy: “A quarter of a century ago, Russell Jacoby lamented the demise of the public intellectual. The cause of death was an improvement in material conditions. Public intellectuals—Dwight Macdonald, I.F. Stone, and their like—once had little choice but to be independent. They had difficulty getting permanent well-paying jobs. However, as universities began to expand, they offered new opportunities to erstwhile unemployables. The academy demanded a high price. Intellectuals had to turn away from the public and toward the practiced obscurities of academic research and prose. In Jacoby’s description, these intellectuals “no longer need[ed] or want[ed] a larger public…. Campuses [were] their homes; colleagues their audience; monographs and specialized journals their media.”
Over the last decade, conditions have changed again. New possibilities are opening up for public intellectuals. Internet-fueled media such as blogs have made it much easier for aspiring intellectuals to publish their opinions. They have fostered the creation of new intellectual outlets (Jacobin, The New Inquiry, The Los Angeles Review of Books), and helped revitalize some old ones too (The Baffler, Dissent). Finally, and not least, they have provided the meat for a new set of arguments about how communications technology is reshaping society.
These debates have created opportunities for an emergent breed of professional argument-crafters: technology intellectuals. Like their predecessors of the 1950s and ’60s, they often make a living without having to work for a university. Indeed, the professoriate is being left behind. Traditional academic disciplines (except for law, which has a magpie-like fascination with new and shiny things) have had a hard time keeping up. New technologies, to traditionalists, are suspect: They are difficult to pin down within traditional academic boundaries, and they look a little too fashionable to senior academics, who are often nervous that their fields might somehow become publicly relevant.
Many of these new public intellectuals are more or less self-made. Others are scholars (often with uncomfortable relationships with the academy, such as Clay Shirky, an unorthodox professor who is skeptical that the traditional university model can survive). Others still are entrepreneurs, like technology and media writer and podcaster Jeff Jarvis, working the angles between public argument and emerging business models….
Different incentives would lead to different debates. In a better world, technology intellectuals might think more seriously about the relationship between technological change and economic inequality. Many technology intellectuals think of the culture of Silicon Valley as inherently egalitarian, yet economist James Galbraith argues that income inequality in the United States “has been driven by capital gains and stock options, mostly in the tech sector.”
They might think more seriously about how technology is changing politics. Current debates are still dominated by pointless arguments between enthusiasts who believe the Internet is a model for a radically better democracy, and skeptics who claim it is the dictator’s best friend.
Finally, they might pay more attention to the burgeoning relationship between technology companies and the U.S. government. Technology intellectuals like to think that a powerful technology sector can enhance personal freedom and constrain the excesses of government. Instead, we are now seeing how a powerful technology sector may enable government excesses. Without big semi-monopolies like Facebook, Google, and Microsoft to hoover up personal information, surveillance would be far more difficult for the U.S. government.
Debating these issues would require a more diverse group of technology intellectuals. The current crop are not diverse in some immediately obvious ways—there are few women, few nonwhites, and few non-English speakers who have ascended to the peak of attention. Yet there is also far less intellectual diversity than there ought to be. The core assumptions of public debates over technology get less attention than they need and deserve.”

Frontiers in Massive Data Analysis

Curated on September 11, 2013August 3, 2018 by Stefaan Verhulst

New report from the National Academy of Sciences: “Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data.
Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale–terabytes and petabytes–is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge–from computer science, statistics, machine learning, and application disciplines–that must be brought to bear to make useful inferences from massive data.”

Understanding the impact of releasing and re-using open government data

Curated on September 10, 2013August 3, 2018 by Stefaan Verhulst

New Report by the European Public Sector Information Platform: “While there has been a proliferation of open data portals and data re-using tools and applications of tremendous speed in the last decade, research and understanding about the impact of opening up public sector information and open government data (OGD hereinafter) has been lacking behind.
Until now, there have been some research efforts to structure the concept of the impact of OGD suggesting various theories of change, their measuring methodologies or in some cases, concrete calculations as to what financial benefits opening government data brings on a table. For instance, the European Commission conducted a study on pricing of public sector information, which attempted evaluating direct and indirect economic impact of opening public data and identified key indicators to monitor the effects of open data portals. Also, Open Data Research Network issued a background report in April 2012 suggesting a general framework of key indicators to measure the impact of open data initiatives both on a provision and re-use stages.
Building on the research efforts up to date, this report will reflect upon the main types of impacts OGD may have and will also present key measuring frameworks to observe the change OGD initiatives may bring about.”

Open data for accountable governance: Is data literacy the key to citizen engagement?

Curated on September 9, 2013May 29, 2019 by Stefaan Verhulst

Camilla Monckton at UNDP’s Voices of Eurasia blog: “How can technology connect citizens with governments, and how can we foster, harness, and sustain the citizen engagement that is so essential to anti-corruption efforts?
UNDP has worked on a number of projects that use technology to make it easier for citizens to report corruption to authorities:

Serbia’s SMS corruption reporting in the health sector
Montenegro’s ‘be responsible app’
Kosovo’s online corruption reporting site kallxo.com

These projects are showing some promising results, and provide insights into how a more participatory, interactive government could develop.
At the heart of the projects is the ability to use citizen generated data to identify and report problems for governments to address….

Wanted: Citizen experts

As Kenneth Cukier, The Economist’s Data Editor, has discussed, data literacy will become the new computer literacy. Big data is still nascent and it is impossible to predict exactly how it will affect society as a whole. What we do know is that it is here to stay and data literacy will be integral to our lives.
It is essential that we understand how to interact with big data and the possibilities it holds.
Data literacy needs to be integrated into the education system. Educating non-experts to analyze data is critical to enabling broad participation in this new data age.
As technology advances, key government functions become automated, and government data sharing increases, newer ways for citizens to engage will multiply.
Technology changes rapidly, but the human mind and societal habits cannot. After years of closed government and bureaucratic inefficiency, adaptation of a new approach to governance will take time and education.
We need to bring up a generation that sees being involved in government decisions as normal, and that views participatory government as a right, not an ‘innovative’ service extended by governments.

What now?

In the meantime, while data literacy lies in the hands of a few, we must continue to connect those who have the technological skills with citizen experts seeking to change their communities for the better – as has been done in many a Social Innovation Camps recently (in Montenegro, Ukraine and Armenia at Mardamej and Mardamej Relaoded and across the region at Hurilab).
The social innovation camp and hackathon models are an increasingly debated topic (covered by Susannah Vila, David Eaves, Alex Howard and Clay Johnson).
On the whole, evaluations are leading to newer models that focus on greater integration of mentorship to increase sustainability – which I readily support. However, I do have one comment:
Social innovation camps are often criticized for a lack of sustainability – a claim based on the limited number of apps that go beyond the prototype phase. I find a certain sense of irony in this, for isn’t this what innovation is about: Opening oneself up to the risk of failure in the hope of striking something great?
In the words of Vinod Khosla:

“No failure means no risk, which means nothing new.”

As more data is released, the opportunity for new apps and new ways for citizen interaction will multiply and, who knows, someone might come along and transform government just as TripAdvisor transformed the travel industry.”

Fighting for Reliable Evidence

Curated on September 6, 2013November 1, 2018 by Stefaan Verhulst

New book by Judy Gueron and Howard Rolston: “Once primarily used in medical clinical trials, random assignment experimentation is now accepted among social scientists across a broad range of disciplines. The technique has been used in social experiments to evaluate a variety of programs, from microfinance and welfare reform to housing vouchers and teaching methods. How did randomized experiments move beyond medicine and into the social sciences, and can they be used effectively to evaluate complex social problems? Fighting for Reliable Evidence provides an absorbing historical account of the characters and controversies that have propelled the wider use of random assignment in social policy research over the past forty years.
Drawing from their extensive experience evaluating welfare reform programs, noted scholar practitioners Judith M. Gueron and Howard Rolston portray randomized experiments as a vital research tool to assess the impact of social policy. In a random assignment experiment, participants are sorted into either a treatment group that participates in a particular program, or a control group that does not. Because the groups are randomly selected, they do not differ from one another systematically. Therefore any subsequent differences between the groups can be attributed to the influence of the program or policy. The theory is elegant and persuasive, but many scholars worry that such an experiment is too difficult or expensive to implement in the real world. Can a control group be truly insulated from the treatment policy? Would staffers comply with the random allocation of participants? Would the findings matter?”