The Emerging Power of Big Data

Curated on Posted on by Stefaan Verhulst

New America Foundation Report on the Chicago experience of using big data: “Big data is transforming the commercial marketplace but it also has the potential to reshape government affairs and urban development.  In a new report from the Emerging Leaders Program at the Chicago Council of Global Affairs, Lincoln S. Ellis, a founding member of the World Economic Roundtable, and other authors from the Emerging Leaders Program, explore how big data can be used by mega-cities to meet the challenges they face in an age of resource constraints to improve the lives of their residents.
Using Chicago as a case study, the report examines how the explosion of data availability enables cities to do more with less—to improve government services, fund much needed transportation, provide better education, and guarantee public safety.  And do more with less is what many cities have had to do over the past five years because many cities have had to cut their budgets and reduce the number of public employees in the post-financial crisis economy.  It is also what they will need to continue to do in the future.
“Unfortunately, resource constraints are a consistent feature of the post-crisis global landscape,” argues Ellis.  “Happily, so too is the renaissance in productivity gains garnered by our ability to leverage technology and information to achieve our most important public purposes in a smarter and more efficient way.”
Click here to view the report as a PDF.”

Big Data, My Data

Curated on Posted on by Stefaan Verhulst

Jane Sarasohn-Kahn  at iHealthBeat: “The routine operation of modern health care systems produces an abundance of electronically stored data on an ongoing basis,” Sebastian Schneeweis writes in a recent New England Journal of Medicine Perspective.
Is this abundance of data a treasure trove for improving patient care and growing knowledge about effective treatments? Is that data trove a Pandora’s black box that can be mined by obscure third parties to benefit for-profit companies without rewarding those whose data are said to be the new currency of the economy? That is, patients themselves?
In this emerging world of data analytics in health care, there’s Big Data and there’s My Data (“small data”). Who most benefits from the use of My Data may not actually be the consumer.
Big focus on Big Data. Several reports published in the first half of 2014 talk about the promise and perils of Big Data in health care. The Federal Trade Commission’s study, titled “Data Brokers: A Call for Transparency and Accountability,” analyzed the business practices of nine “data brokers,” companies that buy and sell consumers’ personal information from a broad array of sources. Data brokers sell consumers’ information to buyers looking to use those data for marketing, managing financial risk or identifying people. There are health implications in all of these activities, and the use of such data generally is not covered by HIPAA. The report discusses the example of a data segment called “Smoker in Household,” which a company selling a new air filter for the home could use to target-market to an individual who might seek such a product. On the downside, without the consumers’ knowledge, the information could be used by a financial services company to identify the consumer as a bad health insurance risk.
“Big Data and Privacy: A Technological Perspective,” a report from the President’s Office of Science and Technology Policy, considers the growth of Big Data’s role in helping inform new ways to treat diseases and presents two scenarios of the “near future” of health care. The first, on personalized medicine, recognizes that not all patients are alike or respond identically to treatments. Data collected from a large number of similar patients (such as digital images, genomic information and granular responses to clinical trials) can be mined to develop a treatment with an optimal outcome for the patients. In this case, patients may have provided their data based on the promise of anonymity but would like to be informed if a useful treatment has been found. In the second scenario, detecting symptoms via mobile devices, people wishing to detect early signs of Alzheimer’s Disease in themselves use a mobile device connecting to a personal couch in the Internet cloud that supports and records activities of daily living: say, gait when walking, notes on conversations and physical navigation instructions. For both of these scenarios, the authors ask, “Can the information about individuals’ health be sold, without additional consent, to third parties? What if this is a stated condition of use of the app? Should information go to the individual’s personal physicians with their initial consent but not a subsequent confirmation?”
The World Privacy Foundation’s report, titled “The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future,” describes the growing market for developing indices on consumer behavior, identifying over a dozen health-related scores. Health scores include the Affordable Care Act Individual Health Risk Score, the FICO Medication Adherence Score, various frailty scores, personal health scores (from WebMD and OneHealth, whose default sharing setting is based on the user’s sharing setting with the RunKeeper mobile health app), Medicaid Resource Utilization Group Scores, the SF-36 survey on physical and mental health and complexity scores (such as the Aristotle score for congenital heart surgery). WPF presents a history of consumer scoring beginning with the FICO score for personal creditworthiness and recommends regulatory scrutiny on the new consumer scores for fairness, transparency and accessibility to consumers.
At the same time these three reports went to press, scores of news stories emerged discussing the Big Opportunities Big Data present. The June issue of CFO Magazine published a piece called “Big Data: Where the Money Is.” InformationWeek published “Health Care Dives Into Big Data,” Motley Fool wrote about “Big Data’s Big Future in Health Care” and WIRED called “Cloud Computing, Big Data and Health Care” the “trifecta.”
Well-timed on June 5, the Office of the National Coordinator for Health IT’s Roadmap for Interoperability was detailed in a white paper, titled “Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure.” The document envisions the long view for the U.S. health IT ecosystem enabling people to share and access health information, ensuring quality and safety in care delivery, managing population health, and leveraging Big Data and analytics. Notably, “Building Block #3” in this vision is ensuring privacy and security protections for health information. ONC will “support developers creating health tools for consumers to encourage responsible privacy and security practices and greater transparency about how they use personal health information.” Looking forward, ONC notes the need for “scaling trust across communities.”
Consumer trust: going, going, gone? In the stakeholder community of U.S. consumers, there is declining trust between people and the companies and government agencies with whom people deal. Only 47% of U.S. adults trust companies with whom they regularly do business to keep their personal information secure, according to a June 6 Gallup poll. Furthermore, 37% of people say this trust has decreased in the past year. Who’s most trusted to keep information secure? Banks and credit card companies come in first place, trusted by 39% of people, and health insurance companies come in second, trusted by 26% of people.
Trust is a basic requirement for health engagement. Health researchers need patients to share personal data to drive insights, knowledge and treatments back to the people who need them. PatientsLikeMe, the online social network, launched the Data for Good project to inspire people to share personal health information imploring people to “Donate your data for You. For Others. For Good.” For 10 years, patients have been sharing personal health information on the PatientsLikeMe site, which has developed trusted relationships with more than 250,000 community members…”

Selected Readings on Crowdsourcing Tasks and Peer Production

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 crowdsourcing was originally published in 2014.

Technological advances are creating a new paradigm by which institutions and organizations are increasingly outsourcing tasks to an open community, allocating specific needs to a flexible, willing and dispersed workforce. “Microtasking” platforms like Amazon’s Mechanical Turk are a burgeoning source of income for individuals who contribute their time, skills and knowledge on a per-task basis. In parallel, citizen science projects – task-based initiatives in which citizens of any background can help contribute to scientific research – like Galaxy Zoo are demonstrating the ability of lay and expert citizens alike to make small, useful contributions to aid large, complex undertakings. As governing institutions seek to do more with less, looking to the success of citizen science and microtasking initiatives could provide a blueprint for engaging citizens to help accomplish difficult, time-consuming objectives at little cost. Moreover, the incredible success of peer-production projects – best exemplified by Wikipedia – instills optimism regarding the public’s willingness and ability to complete relatively small tasks that feed into a greater whole and benefit the public good. You can learn more about this new wave of “collective intelligence” by following the MIT Center for Collective Intelligence and their annual Collective Intelligence Conference.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, 2006. http://bit.ly/1aaU7Yb.

  • In this book, Benkler “describes how patterns of information, knowledge, and cultural production are changing – and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves.”
  • In his discussion on Wikipedia – one of many paradigmatic examples of people collaborating without financial reward – he calls attention to the notable ongoing cooperation taking place among a diversity of individuals. He argues that, “The important point is that Wikipedia requires not only mechanical cooperation among people, but a commitment to a particular style of writing and describing concepts that is far from intuitive or natural to people. It requires self-discipline. It enforces the behavior it requires primarily through appeal to the common enterprise that the participants are engaged in…”

Brabham, Daren C. Using Crowdsourcing in Government. Collaborating Across Boundaries Series. IBM Center for The Business of Government, 2013. http://bit.ly/17gzBTA.

  • In this report, Brabham categorizes government crowdsourcing cases into a “four-part, problem-based typology, encouraging government leaders and public administrators to consider these open problem-solving techniques as a way to engage the public and tackle difficult policy and administrative tasks more effectively and efficiently using online communities.”
  • The proposed four-part typology describes the following types of crowdsourcing in government:
    • Knowledge Discovery and Management
    • Distributed Human Intelligence Tasking
    • Broadcast Search
    • Peer-Vetted Creative Production
  • In his discussion on Distributed Human Intelligence Tasking, Brabham argues that Amazon’s Mechanical Turk and other microtasking platforms could be useful in a number of governance scenarios, including:
    • Governments and scholars transcribing historical document scans
    • Public health departments translating health campaign materials into foreign languages to benefit constituents who do not speak the native language
    • Governments translating tax documents, school enrollment and immunization brochures, and other important materials into minority languages
    • Helping governments predict citizens’ behavior, “such as for predicting their use of public transit or other services or for predicting behaviors that could inform public health practitioners and environmental policy makers”

Boudreau, Kevin J., Patrick Gaule, Karim Lakhani, Christoph Reidl, Anita Williams Woolley. “From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 14-060. January 23, 2014. https://bit.ly/2QVmGUu.

  • In this working paper, the authors explore the “conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork” and “consider the the role of incentives versus social processes in catalyzing collaboration.”
  • The paper’s findings are based on an experiment involving 260 individuals randomly assigned to 52 teams working toward solutions to a complex problem.
  • The authors determined the level of effort in such collaborative undertakings are sensitive to cash incentives. However, collaboration among teams was driven more by the active participation of teammates, rather than any monetary reward.

Franzoni, Chiara, and Henry Sauermann. “Crowd Science: The Organization of Scientific Research in Open Collaborative Projects.” Research Policy (August 14, 2013). http://bit.ly/HihFyj.

  • In this paper, the authors explore the concept of crowd science, which they define based on two important features: “participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem solving algorithms are made openly available.” The rationale for their study and conceptual framework is the “growing attention from the scientific community, but also policy makers, funding agencies and managers who seek to evaluate its potential benefits and challenges. Based on the experiences of early crowd science projects, the opportunities are considerable.”
  • Based on the study of a number of crowd science projects – including governance-related initiatives like Patients Like Me – the authors identify a number of potential benefits in the following categories:
    • Knowledge-related benefits
    • Benefits from open participation
    • Benefits from the open disclosure of intermediate inputs
    • Motivational benefits
  • The authors also identify a number of challenges:
    • Organizational challenges
    • Matching projects and people
    • Division of labor and integration of contributions
    • Project leadership
    • Motivational challenges
    • Sustaining contributor involvement
    • Supporting a broader set of motivations
    • Reconciling conflicting motivations

Kittur, Aniket, Ed H. Chi, and Bongwon Suh. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 453–456. CHI ’08. New York, NY, USA: ACM, 2008. http://bit.ly/1a3Op48.

  • In this paper, the authors examine “[m]icro-task markets, such as Amazon’s Mechanical Turk, [which] offer a potential paradigm for engaging a large number of users for low time and monetary costs. [They] investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks.”
  • The authors conclude that in addition to providing a means for crowdsourcing small, clearly defined, often non-skill-intensive tasks, “Micro-task markets such as Amazon’s Mechanical Turk are promising platforms for conducting a variety of user study tasks, ranging from surveys to rapid prototyping to quantitative measures. Hundreds of users can be recruited for highly interactive tasks for marginal costs within a timeframe of days or even minutes. However, special care must be taken in the design of the task, especially for user measurements that are subjective or qualitative.”

Kittur, Aniket, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton. “The Future of Crowd Work.” In 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013), 2012. http://bit.ly/1c1GJD3.

  • In this paper, the authors discuss paid crowd work, which “offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale.” However, they caution that, “it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework.”
  • The authors argue that seven key challenges must be met to ensure that crowd work processes evolve and reach their full potential:
    • Designing workflows
    • Assigning tasks
    • Supporting hierarchical structure
    • Enabling real-time crowd work
    • Supporting synchronous collaboration
    • Controlling quality

Madison, Michael J. “Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo.” In Convening Cultural Commons, 2013. http://bit.ly/1ih9Xzm.

  • This paper explores a “case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place, Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis on the Internet. In the second place, Galaxy Zoo is a highly successful example of peer production, some times known as crowdsourcing…In the third place, is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies.”
  • Madison concludes that the success of Galaxy Zoo has not been the result of the “character of its information resources (scientific data) and rules regarding their usage,” but rather, the fact that the “community was guided from the outset by a vision of a specific organizational solution to a specific research problem in astronomy, initiated and governed, over time, by professional astronomers in collaboration with their expanding universe of volunteers.”

Malone, Thomas W., Robert Laubacher and Chrysanthos Dellarocas. “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” MIT Sloan Research Paper. February 3, 2009. https://bit.ly/2SPjxTP.

  • In this article, the authors describe and map the phenomenon of collective intelligence – also referred to as “radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics – which they broadly define as “groups of individuals doing things collectively that seem intelligent.”
  • The article is derived from the authors’ work at MIT’s Center for Collective Intelligence, where they gathered nearly 250 examples of Web-enabled collective intelligence. To map the building blocks or “genes” of collective intelligence, the authors used two pairs of related questions:
    • Who is performing the task? Why are they doing it?
    • What is being accomplished? How is it being done?
  • The authors concede that much work remains to be done “to identify all the different genes for collective intelligence, the conditions under which these genes are useful, and the constraints governing how they can be combined,” but they believe that their framework provides a useful start and gives managers and other institutional decisionmakers looking to take advantage of collective intelligence activities the ability to “systematically consider many possible combinations of answers to questions about Who, Why, What, and How.”

Mulgan, Geoff. “True Collective Intelligence? A Sketch of a Possible New Field.” Philosophy & Technology 27, no. 1. March 2014. http://bit.ly/1p3YSdd.

  • In this paper, Mulgan explores the concept of a collective intelligence, a “much talked about but…very underdeveloped” field.
  • With a particular focus on health knowledge, Mulgan “sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to possible intellectual barriers to progress.”
  • He concludes that the “central message that comes from observing real intelligence is that intelligence has to be for something,” and that “turning this simple insight – the stuff of so many science fiction stories – into new theories, new technologies and new applications looks set to be one of the most exciting prospects of the next few years and may help give shape to a new discipline that helps us to be collectively intelligent about our own collective intelligence.”

Sauermann, Henry and Chiara Franzoni. “Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation.” SSRN Working Papers Series. November 28, 2013. http://bit.ly/1o6YB7f.

  • In this paper, Sauremann and Franzoni explore the issue of interest-based motivation in crowd-based knowledge production – in particular the use of the crowd science platform Zooniverse – by drawing on “research in psychology to discuss important static and dynamic features of interest and deriv[ing] a number of research questions.”
  • The authors find that interest-based motivation is often tied to a “particular object (e.g., task, project, topic)” not based on a “general trait of the person or a general characteristic of the object.” As such, they find that “most members of the installed base of users on the platform do not sign up for multiple projects, and most of those who try out a project do not return.”
  • They conclude that “interest can be a powerful motivator of individuals’ contributions to crowd-based knowledge production…However, both the scope and sustainability of this interest appear to be rather limited for the large majority of contributors…At the same time, some individuals show a strong and more enduring interest to participate both within and across projects, and these contributors are ultimately responsible for much of what crowd science projects are able to accomplish.”

Schmitt-Sands, Catherine E. and Richard J. Smith. “Prospects for Online Crowdsourcing of Social Science Research Tasks: A Case Study Using Amazon Mechanical Turk.” SSRN Working Papers Series. January 9, 2014. http://bit.ly/1ugaYja.

  • In this paper, the authors describe an experiment involving the nascent use of Amazon’s Mechanical Turk as a social science research tool. “While researchers have used crowdsourcing to find research subjects or classify texts, [they] used Mechanical Turk to conduct a policy scan of local government websites.”
  • Schmitt-Sands and Smith found that “crowdsourcing worked well for conducting an online policy program and scan.” The microtasked workers were helpful in screening out local governments that either did not have websites or did not have the types of policies and services for which the researchers were looking. However, “if the task is complicated such that it requires ongoing supervision, then crowdsourcing is not the best solution.”

Shirky, Clay. Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press, 2008. https://bit.ly/2QysNif.

  • In this book, Shirky explores our current era in which, “For the first time in history, the tools for cooperating on a global scale are not solely in the hands of governments or institutions. The spread of the Internet and mobile phones are changing how people come together and get things done.”
  • Discussing Wikipedia’s “spontaneous division of labor,” Shirky argues that the process is like, “the process is more like creating a coral reef, the sum of millions of individual actions, than creating a car. And the key to creating those individual actions is to hand as much freedom as possible to the average user.”

Silvertown, Jonathan. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24, no. 9 (September 2009): 467–471. http://bit.ly/1iha6CR.

  • This article discusses the move from “Science for the people,” a slogan adopted by activists in the 1970s to “’Science by the people,’ which is “a more inclusive aim, and is becoming a distinctly 21st century phenomenon.”
  • Silvertown identifies three factors that are responsible for the explosion of activity in citizen science, each of which could be similarly related to the crowdsourcing of skills by governing institutions:
    • “First is the existence of easily available technical tools for disseminating information about products and gathering data from the public.
    • A second factor driving the growth of citizen science is the increasing realisation among professional scientists that the public represent a free source of labour, skills, computational power and even finance.
    • Third, citizen science is likely to benefit from the condition that research funders such as the National Science Foundation in the USA and the Natural Environment Research Council in the UK now impose upon every grantholder to undertake project-related science outreach. This is outreach as a form of public accountability.”

Szkuta, Katarzyna, Roberto Pizzicannella, David Osimo. “Collaborative approaches to public sector innovation: A scoping study.” Telecommunications Policy. 2014. http://bit.ly/1oBg9GY.

  • In this article, the authors explore cases where government collaboratively delivers online public services, with a focus on success factors and “incentives for services providers, citizens as users and public administration.”
  • The authors focus on six types of collaborative governance projects:
    • Services initiated by government built on government data;
    • Services initiated by government and making use of citizens’ data;
    • Services initiated by civil society built on open government data;
    • Collaborative e-government services; and
    • Services run by civil society and based on citizen data.
  • The cases explored “are all designed in the way that effectively harnesses the citizens’ potential. Services susceptible to collaboration are those that require computing efforts, i.e. many non-complicated tasks (e.g. citizen science projects – Zooniverse) or citizens’ free time in general (e.g. time banks). Those services also profit from unique citizens’ skills and their propensity to share their competencies.”

Why Governments Should Adopt a Digital Engagement Strategy

Curated on Posted on by Stefaan Verhulst

Lindsay Crudele at StateTech: “Government agencies increasingly value digital engagement as a way to transform a complaint-based relationship into one of positive, proactive constituent empowerment. An engaged community is a stronger one.
Creating a culture of participatory government, as we strive to do in Boston, requires a data-driven infrastructure supported by IT solutions. Data management and analytics solutions translate a huge stream of social media data, drive conversations and creative crowdsourcing, and support transparency.
More than 50 departments across Boston host public conversations using a multichannel, multidisciplinary portfolio of accounts. We integrate these using an enterprise digital engagement management tool that connects and organizes them to break down silos and boost collaboration. Moreover, the technology provides a lens into ways to expedite workflow and improve service delivery.

A Vital Link in Times of Need

Committed and creative daily engagement builds trusting collaboration that, in turn, is vital in an inevitable crisis. As we saw during the tragic events of the 2013 Boston Marathon bombings and recent major weather events, rapid response through digital media clarifies the situation, provides information about safety and manages constituent expectations.
Boston’s enterprise model supports coordinated external communication and organized monitoring, intake and response. This provides a superadmin with access to all accounts for governance and the ability to easily amplify central messaging across a range of cultivated communities. These communities will later serve in recovery efforts.
The conversations must be seeded by a keen, creative and data-driven content strategy. For an agency to determine the correct strategy for the organization and the community it serves, a growing crop of social analytics tools can provide efficient insight into performance factors: type of content, deployment schedule, sentiment, service-based response time and team performance, to name a few. For example, in February, the city of Boston learned that tweets from our mayor with video saw 300 percent higher engagement than those without.
These insights can inform resource deployment, eliminating guesswork to more directly reach constituents by their preferred methods. Being truly present in a conversation demonstrates care and awareness and builds trust. This increased positivity can be measured through sentiment analysis, including change over time, and should be monitored for fluctuation.
During a major event, engagement managers may see activity reach new peaks in volume. IT solutions can interpret Big Data and bring a large-scale digital conversation back into perspective, identifying public safety alerts and emerging trends, needs and community influencers who can be engaged as amplifying partners.

Running Strong One Year Later

Throughout the 2014 Boston Marathon, we used three monitoring tools to deliver smart alerts to key partners across the organization:
• An engagement management tool organized conversations for account performance and monitoring.
• A brand listening tool scanned for emerging trends across the city and uncovered related conversations.
• A location-based predictive tool identified early alerts to discover potential problems along the marathon route.
With the team and tools in place, policy-based training supports the sustained growth and operation of these conversation channels. A data-driven engagement strategy unearths all of our stories, where we, as public servants and neighbors, build better communities together….”

The Emerging Science of Computational Anthropology

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: The increasing availability of big data from mobile phones and location-based apps has triggered a revolution in the understanding of human mobility patterns. This data shows the ebb and flow of the daily commute in and out of cities, the pattern of travel around the world and even how disease can spread through cities via their transport systems.
So there is considerable interest in looking more closely at human mobility patterns to see just how well it can be predicted and how these predictions might be used in everything from disease control and city planning to traffic forecasting and location-based advertising.
Today we get an insight into the kind of detailed that is possible thanks to the work of Zimo Yang at Microsoft research in Beijing and a few pals. These guys start with the hypothesis that people who live in a city have a pattern of mobility that is significantly different from those who are merely visiting. By dividing travelers into locals and non-locals, their ability to predict where people are likely to visit dramatically improves.
Zimo and co begin with data from a Chinese location-based social network called Jiepang.com. This is similar to Foursquare in the US. It allows users to record the places they visit and to connect with friends at these locations and to find others with similar interests.
The data points are known as check-ins and the team downloaded more than 1.3 million of them from five big cities in China: Beijing, Shanghai, Nanjing, Chengdu and Hong Kong. They then used 90 per cent of the data to train their algorithms and the remaining 10 per cent to test it. The Jiapang data includes the users’ hometowns so it’s easy to see whether an individual is checking in in their own city or somewhere else.
The question that Zimo and co want to answer is the following: given a particular user and their current location, where are they most likely to visit in the near future? In practice, that means analysing the user’s data, such as their hometown and the locations recently visited, and coming up with a list of other locations that they are likely to visit based on the type of people who visited these locations in the past.
Zimo and co used their training dataset to learn the mobility pattern of locals and non-locals and the popularity of the locations they visited. The team then applied this to the test dataset to see whether their algorithm was able to predict where locals and non-locals were likely to visit.
They found that their best results came from analysing the pattern of behaviour of a particular individual and estimating the extent to which this person behaves like a local. That produced a weighting called the indigenization coefficient that the researchers could then use to determine the mobility patterns this person was likely to follow in future.
In fact, Zimo and co say they can spot non-locals in this way without even knowing their home location. “Because non-natives tend to visit popular locations, like the Imperial Palace in Beijing and the Bund in Shanghai, while natives usually check in around their homes and workplaces,” they add.
The team say this approach considerably outperforms the mixed algorithms that use only individual visiting history and location popularity. “To our surprise, a hybrid algorithm weighted by the indigenization coefficients outperforms the mixed algorithm accounting for additional demographical information.”
It’s easy to imagine how such an algorithm might be useful for businesses who want to target certain types of travelers or local people. But there is a more interesting application too.
Zimo and co say that it is possible to monitor the way an individual’s mobility patterns change over time. So if a person moves to a new city, it should be possible to see how long it takes them to settle in.
One way of measuring this is in their mobility patterns: whether they are more like those of a local or a non-local. “We may be able to estimate whether a non-native person will behave like a native person after a time period and if so, how long in average a person takes to become a native-like one,” say Zimo and co.
That could have a fascinating impact on the way anthropologists study migration and the way immigrants become part of a local community. This is computational anthropology a science that is clearly in its early stages but one that has huge potential for the future.”
Ref: arxiv.org/abs/1405.7769 : Indigenization of Urban Mobility

Big Data, new epistemologies and paradigm shifts

Curated on Posted on by Stefaan Verhulst

Paper by Rob Kitchin in the Journal “Big Data and Society”: This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, history, economy and society. It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and (2) there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology”

Making cities smarter through citizen engagement

Curated on Posted on by Stefaan Verhulst

Vaidehi Shah at Eco-Business: “Rapidly progressing information communications technology (ICT) is giving rise to an almost infinite range of innovations that can be implemented in cities to make them more efficient and better connected. However, in order for technology to yield sustainable solutions, planners must prioritise citizen engagement and strong leadership.
This was the consensus on Tuesday at the World Cities Summit 2014, where representatives from city and national governments, technology firms and private sector organisations gathered in Singapore to discuss strategies and challenges to achieving sustainable cities in the future.
Laura Ipsen, Microsoft corporate vice president for worldwide public sector, identified globalisation, social media, big data, and mobility as the four major technological trends prevailing in cities today, as she spoke at the plenary session with a theme on “The next urban decade: critical challenges and opportunities”.
Despite these increasing trends, she cautioned, “technology does not build infrastructure, but it does help better engage citizens and businesses through public-private partnerships”.
For example, “LoveCleanStreets”, an online tool developed by Microsoft and partners, enables London residents to report infrastructure problems such as damaged roads or signs, shared Ipsen.
“By engaging citizens through this application, cities can fix problems early, before they get worse,” she said.
In Singapore, the ‘MyWaters’ app of PUB, Singapore’s national water agency, is also a key tool for the government to keep citizens up-to-date of water quality and safety issues in the country, she added.
Even if governments did not actively develop solutions themselves, simply making the immense amounts of data collected by the city open to businesses and citizens could make a big difference to urban liveability, Mark Chandler, director of the San Francisco Mayor’s Office of International Trade and Commerce, pointed out.
Opening up all of the data collected by San Francisco, for instance, yielded 60 free mobile applications that allow residents to access urban solutions related to public transport, parking, and electricity, among others, he explained. This easy and convenient access to infrastructure and amenities, which are a daily necessity, is integral to “a quality of life that keeps the talented workforce in the city,” Chandler said….”

Estonian plan for 'data embassies' overseas to back up government databases

Curated on Posted on by Stefaan Verhulst

Graeme Burton in Computing: “Estonia is planning to open “data embassies” overseas to back up government databases and to operate government “in the cloud“.
The aim is partly to improve efficiency, but driven largely by fear of invasion and occupation, Jaan Priisalu, the director general of Estonian Information System Authority, told Sky News.
He said: “We are planning to actually operate our government in the cloud. It’s clear also how it helps to protect the country, the territory. Usually when you are the military planner and you are planning the occupation of the territory, then one of the rules is suppress the existing institutions.
“And if you are not able to do it, it means that this political price of occupying the country will simply rise for planners.”
Part of the rationale for the plan, he continued, was fear of attack from Russia in particular, which has been heightened following the occupation of Crimea, formerly in Ukraine.
“It’s quite clear that you can have problems with your neighbours. And our biggest neighbour is Russia, and nowadays it’s quite aggressive. This is clear.”
The plan is to back up critical government databases outside of Estonia so that affairs of state can be conducted in the cloud, even if the country is invaded. It would also have the benefit of keeping government information out of invaders’ hands – provided it can keep its government cloud secure.
According to Sky News, the UK is already in advanced talks about hosting the Estonian government databases and may make the UK the first of Estonia’s data embassies.
Having wrested independence from the Soviet Union in 1991, Estonia has experienced frequent tension with its much bigger neighbour. In 2007, for example, after the relocation of the “Bronze Soldier of Tallinn” and the exhumation of the soldiers buried in a square in the centre of the capital to a military cemetery in April 2007, the country was subject to a prolonged cyber-attack sourced to Russia.
Russian hacker “Sp0Raw” said that the most efficient of the online attacks on Estonia could not have been carried out without the approval of Russian authorities and added that the hackers seemed to act under “recommendations” from parties in government. However, claims by Estonia that the Russian government was directly involved in the attacks were “empty words, not supported by technical data”.
Mike Witt, deputy director of the US Computer Emergency Response Team (CERT), suggested that the distributed denial-of-service (DDOS) attacks, while crippling to the Estonian government at the time, were not significant in scale from a technical standpoint. However, the Estonian government was forced to shut down many of its online operations in response.
At the same time, the Estonian government has been accused of implementing anti-Russian laws and discriminating against its large ethnic Russian population.
Last week, the Estonian government unveiled a plan to allow anyone in the world to apply for “digital citizenship“ of the country, enabling them to use Estonian online services, open bank accounts, and start companies without having to physically reside in the country.”

How The Right People Analyzing The Best Data Are Transforming Government

Curated on Posted on by Stefaan Verhulst

NextGov: “Analytics is often touted as a new weapon in the technology arsenal of bleeding-edge organizations willing to spend lots of money to combat problems.
In reality, that’s not the case at all. Certainly, there are complex big data analytics tools that will analyze massive data sets to look for the proverbial needle in a haystack, but analytics 101 also includes smarter ways to look at existing data sets.
In this arena, government is making serious strides, according to Kathryn Stack, advisor for evidence-based innovation at the Office of Management and Budget. Speaking in Washington on Thursday at an analytics conference hosted by IBM, Stack provided an outline for agencies to spur innovation and improve mission by making smarter use of the data they already produce.
Interestingly, the first step has nothing to do with technology and everything to do with people. Get “the right people in the room,” Stack said, and make sure they value learning.
“One thing I have learned in my career is that if you really want transformative change, it’s important to bring the right players together across organizations – from your own department and different parts of government,” Stack said. “Too often, we lose a lot of money when siloed organizations lose sight of what the problem really is and spend a bunch of money, and at the end of the day we have invested in the wrong thing that doesn’t address the problem.”
The Department of Labor provides a great example for how to change a static organizational culture into one that integrates performance management, evaluation- and innovation-based processes. The department, she said, created a chief evaluation office and set up evaluation offices for each of its bureaus. These offices were tasked with focusing on important questions to improve performance, going inside programs to learn what is and isn’t working and identifying barriers that impeded experimentation and learning. At the same time, they helped develop partnerships across the agency – a major importance for any organization looking to make drastic changes.
Don’t overlook experimentation either, Stack said. Citing innovation leaders in the private sector such as Google, which runs 12,000 randomized experiments per year, Stack said agencies should not be afraid to get out and run with ideas. Not all of them will be good – only about 10 percent of Google’s experiments usher in new business changes – but even failures can bring meaningful value to the mission.
Stack used an experiment conducted by the United Kingdom’s Behavioral Insights Team as evidence.
The team continually tweaked language to tax compliance letters sent to individuals delinquent on their taxes. Significant experimentation ushered in lots of data, and the team analyzed it to find that one phrase, “Nine out of ten Britains pay their taxes on time,” improved collected revenue by five percent. That case shows how failures can bring about important successes.
“If you want to succeed, you’ve got to be willing to fail and test things out,” Stack said.
Any successful analytics effort in government is going to employ the right people, the best data – Stack said it’s not a secret that the government collects both useful and not-so-useful, “crappy” data – as well as the right technology and processes, too. For instance, there are numerous ways to measure return on investment, including dollars per customer served or costs per successful outcome.
“What is the total investment you have to make in a certain strategy in order to get a successful outcome?” Stack said. “Think about cost per outcome and how you do those calculations.”…”

Linking Social, Open, and Enterprise Data

Curated on Posted on by Stefaan Verhulst

Paper by T Omitola, J Davies, A Duke, H Glaser, N Shadbolt for Proceeding WIMS ’14 (Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics): “The new world of big data, of the LOD cloud, of the app economy, and of social media means that organisations no longer own, much less control, all the data they need to make the best informed business decisions. In this paper, we describe how we built a system using Linked Data principles to bring in data from Web 2.0 sites (LinkedIn, Salesforce), and other external business sites such as OpenCorporates, linking these together with pertinent internal British Telecommunications enterprise data into that enterprise data space. We describe the challenges faced during the implementation, which include sourcing the datasets, finding the appropriate “join points” from the individual datasets, as well as developing the client application used for data publication. We describe our solutions to these challenges and discuss the design decisions made. We conclude by drawing some general principles from this work.”