Big Data’s Impact on Public Transportation

Curated on Posted on by Stefaan Verhulst

InnovationEnterprise: “Getting around any big city can be a real pain. Traffic jams seem to be a constant complaint, and simply getting to work can turn into a chore, even on the best of days. With more people than ever before flocking to the world’s major metropolitan areas, the issues of crowding and inefficient transportation only stand to get much worse. Luckily, the traditional methods of managing public transportation could be on the verge of changing thanks to advances in big data. While big data use cases have been a part of the business world for years now, city planners and transportation experts are quickly realizing how valuable it can be when making improvements to city transportation. That hour long commute may no longer be something travelers will have to worry about in the future.

In much the same way that big data has transformed businesses around the world by offering greater insight in the behavior of their customers, it can also provide a deeper look at travellers. Like retail customers, commuters have certain patterns they like to keep to when on the road or riding the rails. Travellers also have their own motivations and desires, and getting to the heart of their actions is all part of what big data analytics is about. By analyzing these actions and the factors that go into them, transportation experts can gain a better understanding of why people choose certain routes or why they prefer one method of transportation over another. Based on these findings, planners can then figure out where to focus their efforts and respond to the needs of millions of commuters.

Gathering the accurate data needed to make knowledgeable decisions regarding city transportation can be a challenge in itself, especially considering how many people commute to work in a major city. New methods of data collection have made that effort easier and a lot less costly. One way that’s been implemented is through the gathering of call data records (CDR). From regular transactions made from mobile devices, information about location, time, and duration of an action (like a phone call) can give data scientists the necessary details on where people are traveling to, how long it takes them to get to their destination, and other useful statistics. The valuable part of this data is the sample size, which provides a much bigger picture of the transportation patterns of travellers.

That’s not the only way cities are using big data to improve public transportation though. Melbourne in Australia has long been considered one of the world’s best cities for public transit, and much of that is thanks to big data. With big data and ad hoc analysis, Melbourne’s acclaimed tram system can automatically reconfigure routes in response to sudden problems or challenges, such as a major city event or natural disaster. Data is also used in this system to fix problems before they turn serious.Sensors located in equipment like tram cars and tracks can detect when maintenance is needed on a specific part. Crews are quickly dispatched to repair what needs fixing, and the tram system continues to run smoothly. This is similar to the idea of the Internet of Things, wherein embedded sensors collect data that is then analyzed to identify problems and improve efficiency.

Sao Paulo, Brazil is another city that sees the value of using big data for its public transportation. The city’s efforts concentrate on improving the management of its bus fleet. With big data collected in real time, the city can get a more accurate picture of just how many people are riding the buses, which routes are on time, how drivers respond to changing conditions, and many other factors. Based off of this information, Sao Paulo can optimize its operations, providing added vehicles where demand is genuine whilst finding which routes are the most efficient. Without big data analytics, this process would have taken a very long time and would likely be hit-or-miss in terms of accuracy, but now, big data provides more certainty in a shorter amount of time….(More)”

Why open data should be central to Fifa reform

Curated on Posted on by Stefaan Verhulst

Gavin Starks in The Guardian: “Over the past two weeks, Fifa has faced mounting pressure to radically improve its transparency and governance in the wake of corruption allegations. David Cameron has called for reforms including expanding the use of open data.

Open data is information made available by governments, businesses and other groups for anyone to read, use and share. Data.gov.uk was launched as the home of UK open government data in January 2010 and now has almost 21,000 published datasets, including on government spending.

Allowing citizens to freely access data related to the institutions that govern them is essential to a well-functioning democratic society. It is the first step towards holding leaders to account for failures and wrongdoing.

Fifa has a responsibility for the shared interests of millions of fans around the world. Football’s popularity means that Fifa’s governance has wide-ranging implications for society, too. This is particularly true of decisions about hosting the World Cup, which is often tied to large-scale government investment in infrastructure and even extends to law-making. Brazil spent up to £10bn hosting the 2014 World Cup and had to legalise the sale of beer at matches.

Following Sepp Blatter’s resignation, Fifa will gather its executive committee in July to plan for a presidential election, expected to take place in mid-December. Open data should form the cornerstone of any prospective candidate’s manifesto. It can help Fifa make better spending decisions and ensure partners deliver value for money, restore the trust of the international football community.

Fifa’s lengthy annual financial report gives summaries of financial expenditure,budgeted at £184m for operations and governance alone in 2016, but individual transactions are not published. Publishing spending data incentivises better spending decisions. If all Fifa’s outgoings – which totalled around £3.5bn between 2011 and 2014 – were made open, it would encourage much more efficiency….(more)”

Platform helps displaced families in Haiti crowdfund new homes

Curated on Posted on by Stefaan Verhulst

Springwise: “We have seen a number of products — such as HUSH2 — which aim to provide short-term housing for the displaced or homeless in the wake of conflicts or natural disasters. These shelters are undoubtedly vital but they are essentially temporary, so New Story — a non profit based in Haiti — has another solution. New Story is a crowdfunding platform which enables families to raise the USD 6,000 necessary to finance building a new, long-term home.

New Story has partnered with Mission of Hope, which sources families in need and helps them to launch a crowdfunding campaign via the platform. Potential donors can read the family’s story and see a breakdown of expenses — including materials and labor — before choosing to donate. All the money raised goes directly to each project, which is then carried out by local contractors in Haiti. The houses, which are three room block homes, are usually completed within two months, after which families post video updates for their donors….(More)”

Selected Readings on Data Governance

Curated on Posted on by Jos Berens

Jos Berens (Centre for Innovation, Leiden University) and Stefaan G. Verhulst (GovLab)

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 data governance was originally published in 2015.

Context
The field of Data Collaboratives is premised on the idea that sharing and opening-up private sector datasets has great – and yet untapped – potential for promoting social good. At the same time, the potential of data collaboratives depends on the level of societal trust in the exchange, analysis and use of the data exchanged. Strong data governance frameworks are essential to ensure responsible data use. Without such governance regimes, the emergent data ecosystem will be hampered and the (perceived) risks will dominate the (perceived) benefits. Further, without adopting a human-centered approach to the design of data governance frameworks, including iterative prototyping and careful consideration of the experience, the responses may fail to be flexible and targeted to real needs.

Selected Readings List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Better Place Lab, “Privacy, Transparency and Trust.” Mozilla, 2015. Available from: http://www.betterplace-lab.org/privacy-report.

  • This report looks specifically at the risks involved in the social sector having access to datasets, and the main risks development organizations should focus on to develop a responsible data use practice.
  • Focusing on five specific countries (Brazil, China, Germany, India and Indonesia), the report displays specific country profiles, followed by a comparative analysis centering around the topics of privacy, transparency, online behavior and trust.
  • Some of the key findings mentioned are:
    • A general concern on the importance of privacy, with cultural differences influencing conception of what privacy is.
    • Cultural differences determining how transparency is perceived, and how much value is attached to achieving it.
    • To build trust, individuals need to feel a personal connection or get a personal recommendation – it is hard to build trust regarding automated processes.

Montjoye, Yves Alexandre de; Kendall, Jake and; Kerry, Cameron F. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, 2015. Available from: http://www.brookings.edu/research/papers/2014/11/12-enabling-humanitarian-use-mobile-phone-data.

  • Focussing in particular on mobile phone data, this paper explores ways of mitigating privacy harms involved in using call detail records for social good.
  • Key takeaways are the following recommendations for using data for social good:
    • Engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.
    • Accepting that no framework for maximizing data for the public good will offer perfect protection for privacy, but there must be a balanced application of privacy concerns against the potential for social good.
    • Establishing systems and processes for recognizing trusted third-parties and systems to manage datasets, enable detailed audits, and control the use of data so as to combat the potential for data abuse and re-identification of anonymous data.
    • Simplifying the process among developing governments in regards to the collection and use of mobile phone metadata data for research and public good purposes.

Centre for Democracy and Technology, “Health Big Data in the Commercial Context.” Centre for Democracy and Technology, 2015. Available from: https://cdt.org/insight/health-big-data-in-the-commercial-context/.

  • Focusing particularly on the privacy issues related to using data generated by individuals, this paper explores the overlap in privacy questions this field has with other data uses.
  • The authors note that although the Health Insurance Portability and Accountability Act (HIPAA) has proven a successful approach in ensuring accountability for health data, most of these standards do not apply to developers of the new technologies used to collect these new data sets.
  • For non-HIPAA covered, customer facing technologies, the paper bases an alternative framework for consideration of privacy issues. The framework is based on the Fair Information Practice Principles, and three rounds of stakeholder consultations.

Center for Information Policy Leadership, “A Risk-based Approach to Privacy: Improving Effectiveness in Practice.” Centre for Information Policy Leadership, Hunton & Williams LLP, 2015. Available from: https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/white_paper_1-a_risk_based_approach_to_privacy_improving_effectiveness_in_practice.pdf.

  • This white paper is part of a project aiming to explain what is often referred to as a new, risk-based approach to privacy, and the development of a privacy risk framework and methodology.
  • With the pace of technological progress often outstripping the capabilities of privacy officers to keep up, this method aims to offer the ability to approach privacy matters in a structured way, assessing privacy implications from the perspective of possible negative impact on individuals.
  • With the intended outcomes of the project being “materials to help policy-makers and legislators to identify desired outcomes and shape rules for the future which are more effective and less burdensome”, insights from this paper might also feed into the development of innovative governance mechanisms aimed specifically at preventing individual harm.

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. Available from: http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

  • This paper argues that as a result of the proliferation of large scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.
  • It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”
  • Proposed as a means to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles, and be able to describe how those programs provide the required protections for individuals.”
  • The following essential elements of accountability are listed:
    • Organisation commitment to accountability and adoption of internal policies consistent with external criteria
    • Mechanisms to put privacy policies into effect, including tools, training and education
    • Systems for internal, ongoing oversight and assurance reviews and external verification
    • Transparency and mechanisms for individual participation
    • Means of remediation and external enforcement

Crawford, Kate; Schulz, Jason. “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harm.” NYU School of Law, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784&download=yes.

  • Considering the privacy implications of large-scale analysis of numerous data sources, this paper proposes the implementation of a ‘procedural data due process’ mechanism to arm data subjects against potential privacy intrusions.
  • The authors acknowledge that some privacy protection structures already know similar mechanisms. However, due to the “inherent analytical assumptions and methodological biases” of big data systems, the authors argue for a more rigorous framework.

Letouze, Emmanuel, and; Vinck, Patrick. “The Ethics and Politics of Call Data Analytics”, DataPop Alliance, 2015. Available from: http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf.

  • Focusing on the use of Call Detail Records (CDRs) for social good in development contexts, this whitepaper explores both the potential of these datasets – in part by detailing recent successful efforts in the space – and political and ethical constraints to their use.
  • Drawing from the Menlo Report Ethical Principles Guiding ICT Research, the paper explores how these principles might be unpacked to inform an ethics framework for the analysis of CDRs.

Data for Development External Ethics Panel, “Report of the External Ethics Review Panel.” Orange, 2015. Available from: http://www.d4d.orange.com/fr/content/download/43823/426571/version/2/file/D4D_Challenge_DEEP_Report_IBE.pdf.

  • This report presents the findings of the external expert panel overseeing the Orange Data for Development Challenge.
  • Several types of issues faced by the panel are described, along with the various ways in which the panel dealt with those issues.

Federal Trade Commission Staff Report, “Mobile Privacy Disclosures: Building Trust Through Transparency.” Federal Trade Commission, 2013. Available from: www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf.

  • This report looks at ways to address privacy concerns regarding mobile phone data use. Specific advise is provided for the following actors:
    • Platforms, or operating systems providers
    • App developers
    • Advertising networks and other third parties
    • App developer trade associations, along with academics, usability experts and privacy researchers

Mirani, Leo. “How to use mobile phone data for good without invading anyone’s privacy.” Quartz, 2015. Available from: http://qz.com/398257/how-to-use-mobile-phone-data-for-good-without-invading-anyones-privacy/.

  • This paper considers the privacy implications of using call detail records for social good, and ways to mitigate risks of privacy intrusion.
  • Taking example of the Orange D4D challenge and the anonymization strategy that was employed there, the paper describes how classic ‘anonymization’ is often not enough. The paper then lists further measures that can be taken to ensure adequate privacy protection.

Bernholz, Lucy. “Several Examples of Digital Ethics and Proposed Practices” Stanford Ethics of Data conference, 2014, Available from: http://www.scribd.com/doc/237527226/Several-Examples-of-Digital-Ethics-and-Proposed-Practices.

  • This list of readings prepared for Stanford’s Ethics of Data conference lists some of the leading available literature regarding ethical data use.

Abrams, Martin. “A Unified Ethical Frame for Big Data Analysis.” The Information Accountability Foundation, 2014. Available from: http://www.privacyconference2014.org/media/17388/Plenary5-Martin-Abrams-Ethics-Fundamental-Rights-and-BigData.pdf.

  • Going beyond privacy, this paper discusses the following elements as central to developing a broad framework for data analysis:
    • Beneficial
    • Progressive
    • Sustainable
    • Respectful
    • Fair

Lane, Julia; Stodden, Victoria; Bender, Stefan, and; Nissenbaum, Helen, “Privacy, Big Data and the Public Good”, Cambridge University Press, 2014. Available from: http://www.dataprivacybook.org.

  • This book treats the privacy issues surrounding the use of big data for promoting the public good.
  • The questions being asked include the following:
    • What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?
    • What are the rules of engagement?
    • What are the best ways to provide access while protecting confidentiality?
    • Are there reasonable mechanisms to compensate citizens for privacy loss?

Richards, Neil M, and; King, Jonathan H. “Big Data Ethics”. Wake Forest Law Review, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174.

  • This paper describes the growing impact of big data analytics on society, and argues that because of this impact, a set of ethical principles to guide data use is called for.
  • The four proposed themes are: privacy, confidentiality, transparency and identity.
  • Finally, the paper discusses how big data can be integrated into society, going into multiple facets of this integration, including the law, roles of institutions and ethical principles.

OECD, “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data”. Available from: http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm.

  • A globally used set of principles to inform thought about handling personal data, the OECD privacy guidelines serve as one the leading standards for informing privacy policies and data governance structures.
  • The basic principles of national application are the following:
    • Collection Limitation Principle
    • Data Quality Principle
    • Purpose Specification Principle
    • Use Limitation Principle
    • Security Safeguards Principle
    • Openness Principle
    • Individual Participation Principle
    • Accountability Principle

The White House Big Data and Privacy Working Group, “Big Data: Seizing Opportunities, Preserving Values”, White House, 2015. Available from: https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf.

  • Documenting the findings of the White House big data and privacy working group, this report lists i.a. the following key recommendations regarding data governance:
    • Bringing greater transparency to the data services industry
    • Stimulating international conversation on big data, with multiple stakeholders
    • With regard to educational data: ensuring data is used for the purpose it is collected for
    • Paying attention to the potential for big data to facilitate discrimination, and expanding technical understanding to stop discrimination

William Hoffman, “Pathways for Progress” World Economic Forum, 2015. Available from: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf.

  • This paper treats i.a. the lack of well-defined and balanced governance mechanisms as one of the key obstacles preventing particularly corporate sector data from being shared in a controlled space.
  • An approach that balances the benefits against the risks of large scale data usage in a development context, building trust among all stake holders in the data ecosystem, is viewed as key.
  • Furthermore, this whitepaper notes that new governance models are required not just by the growing amount of data and analytical capacity, and more refined methods for analysis. The current “super-structure” of information flows between institutions is also seen as one of the key reasons to develop alternatives to the current – outdated – approaches to data governance.

Nepal Aid Workers Helped by Drones, Crowdsourcing

Curated on Posted on by Stefaan Verhulst
Shirley Wang et al in the Wall Street Journal: “….It is too early to gauge the exact impact of the technology in Nepal relief efforts, which have just begun amid chaos on the ground. Aid organizations have reported hospitals are overstretched, a shortage of capacity at Katmandu’s airport is crippling aid distribution and damaged roads and the mountainous country’s difficult terrain make reaching villages difficult.

Still, technology is playing an increasing role in the global response to humanitarian crises. Within hours of Saturday’s 7.8-magnitude temblor, U.S. giants such as Google Inc. and Facebook Inc. were offering their networks for use in verifying survivors and helping worried friends and relatives locate their loved ones.

Advances in online mapping—long used to calculate distances and plot driving routes—and the ability of camera-equipped drones are playing an increasingly important role in coordinating emergency responses at ground zero of any disaster.

A community of nonprofit groups uses satellite images, private images and open-source mapping technology to remap areas affected by the earthquake. They mark damaged buildings and roads so rescuers can identify the worst-hit areas and assess how accessible different areas are. The technology complements more traditional intelligence from aircraft.

Such crowdsourced real-time mapping technologies were first used in the 2010 Haiti earthquake, according to Chris Grundy, a professor in Geographical Information Systems at the London School of Hygiene and Tropical Medicine. The technology “has been advancing a little bit every time [every situation where it is used] as we start to see what works,” said Prof. Grundy.

The American Red Cross supplied its relief team on the Wednesday night flight to Nepal from Washington, D.C. with 50 digital maps and an inch-thick pile of paper maps that help identify where the needs are. The charity has a mapping project with the British Red Cross, Doctors Without Borders and the Humanitarian OpenStreetMap Team, a crowdsourced data-sharing group.

Almost a week after the Nepal earthquake, two more people have been pulled from the rubble in Katmandu by teams of international rescuers. But hope for finding more survivors is waning. Photo: Sean McLain/The Wall Street Journal.

Mapping efforts have grown substantially since Haiti, according to Dale Kunce, head of the geographic information systems team at the American Red Cross. In the two months after the Haiti temblor, 600 mapping contributors made 1.5 million edits, while in the first 48 hours after the Nepal earthquake, 2,000 mappers had already made three million edits, Mr. Kunce said.

Some 3,400 volunteers from around the world are now inspecting images of Nepal online to identify road networks and conditions, to assess the extent of damage and pinpoint open spaces where displaced persons tend to congregate, according to Nama Budhathoki, executive director of a nonprofit technology company called Katmandu Living Labs.

His group is operating from a cramped but largely undamaged meeting room in a central-Katmandu office building to help coordinate the global effort of various mapping organizations with the needs of agencies like Doctors Without Borders and the international Red Cross community.

In recent days the Nepal Red Cross and Nepalese army have requested and been supplied with updated maps of severely damaged districts, said Dr. Budhathoki….(More)”

Five Headlines from a Big Month for the Data Revolution

Curated on Posted on by Stefaan Verhulst

Sarah T. Lucas at Post2015.org: “If the history of the data revolution were written today, it would include three major dates. May 2013, when theHigh Level Panel on the Post-2015 Development Agenda first coined the phrase “data revolution.” November 2014, when the UN Secretary-General’s Independent Expert Advisory Group (IEAG) set a vision for it. And April 2015, when five headliner stories pushed the data revolution from great idea to a concrete roadmap for action.

The April 2015 Data Revolution Headlines

1. The African Data Consensus puts Africa in the lead on bringing the data revolution to the regional level. TheAfrica Data Consensus (ADC) envisions “a profound shift in the way that data is harnessed to impact on development decision-making, with a particular emphasis on building a culture of usage.” The ADC finds consensus across 15 “data communities”—ranging from open data to official statistics to geospatial data, and is endorsed by Africa’s ministers of finance. The ADC gets top billing in my book, as the first contribution that truly reflects a large diversity of voices and creates a political hook for action. (Stay tuned for a blog from my colleague Rachel Quint on the ADC).

2. The Sustainable Development Solutions Network (SDSN) gets our minds (and wallets) around the data needed to measure the SDGs. The SDSN Needs Assessment for SDG Monitoring and Statistical Capacity Development maps the investments needed to improve official statistics. My favorite parts are the clear typology of data (see pg. 12), and that the authors are very open about the methods, assumptions, and leaps of faith they had to take in the costing exercise. They also start an important discussion about how advances in information and communications technology, satellite imagery, and other new technologies have the potential to expand coverage, increase analytic capacity, and reduce the cost of data systems.

3. The Overseas Development Institute (ODI) calls on us to find the “missing millions.” ODI’s The Data Revolution: Finding the Missing Millions presents the stark reality of data gaps and what they mean for understanding and addressing development challenges. The authors highlight that even that most fundamental of measures—of poverty levels—could be understated by as much as a quarter. And that’s just the beginning. The report also pushes us to think beyond the costs of data, and focus on how much good data can save. With examples of data lowering the cost of doing government business, the authors remind us to think about data as an investment with real economic and social returns.

4. Paris21 offers a roadmap for putting national statistic offices (NSOs) at the heart of the data revolution.Paris21’s Roadmap for a Country-Led Data Revolution does not mince words. It calls on the data revolution to “turn a vicious cycle of [NSO] underperformance and inadequate resources into a virtuous one where increased demand leads to improved performance and an increase in resources and capacity.” It makes the case for why NSOs are central and need more support, while also pushing them to modernize, innovate, and open up. The roadmap gets my vote for best design. This ain’t your grandfather’s statistics report!

5. The Cartagena Data Festival features real-live data heroes and fosters new partnerships. The Festival featured data innovators (such as terra-i using satellite data to track deforestation), NSOs on the leading edge of modernization and reform (such as Colombia and the Philippines), traditional actors using old data in new ways (such as the Inter-American Development Bank’s fantastic energy database), groups focused on citizen-generated data (such as The Data Shift and UN My World), private firms working with big data for social good (such asTelefónica), and many others—all reminding us that the data revolution is well underway and will not be stopped. Most importantly, it brought these actors together in one place. You could see the sparks flying as folks learned from each other and hatched plans together. The Festival gets my vote for best conference of a lifetime, with the perfect blend of substantive sessions, intense debate, learning, inspiration, new connections, and a lot of fun. (Stay tuned for a post from my colleague Kristen Stelljes and me for more on Cartagena).

This month full of headlines leaves no room for doubt—momentum is building fast on the data revolution. And just in time.

With the Financing for Development (FFD) conference in Addis Ababa in July, the agreement of Sustainable Development Goals in New York in September, and the Climate Summit in Paris in December, this is a big political year for global development. Data revolutionaries must seize this moment to push past vision, past roadmaps, to actual action and results…..(More)”

These researchers want to turn phones into earthquake detectors

Curated on Posted on by Stefaan Verhulst

Russell Brandom in TheVerge: “Early warning on earthquakes can help save lives, but many countries can’t afford them. That’s why scientists are turning to another location sensor already widespread in many countries: the smartphone. A single smartphone makes for a crappy earthquake sensor — but get enough of them reporting, and it won’t matter.

A new study, published today in Science Advances, says that the right network of cell phones might be able to substitute for modern seismograph arrays, providing a crucial early warning in the event of a quake. The study looks at historical earthquake data and modern smartphone hardware (based on the Nexus 5) and comes away with a map of how a smartphone-based earthquake detector might work. As it turns out, a phone’s GPS is more powerful than you might think.

A modern phone has almost everything you could want in an earthquake sensor

Early warning systems are designed to pick up the first tremors of an earthquake, projecting where the incoming quake is centered and how strong it’s likely to be. When they work, the systems are able to give citizens and first responders crucial time to prepare for the quake. There are already seismograph-based systems in place in California, Mexico, and Japan, but poorer countries often don’t have the means to implement and maintain them. This new method wouldn’t be as good as most scientific earthquake sensors, but those can cost tens of thousands of dollars each, making a smartphone-based sensor a lot cheaper. For countries that can’t afford a seismograph-based system (which includes much of the Southern Hemisphere), it could make a crucial difference in catching quakes early.

A modern phone has almost everything you could want in an earthquake sensor: specifically, a GPS-powered location sensor, an accelerometer, and multiple data connections. There are also a lot of them, even in poor countries, so a distributed system could count on getting data points from multiple angles….(More)”

New surveys reveal dynamism, challenges of open data-driven businesses in developing countries

Curated on Posted on by Stefaan Verhulst

Alla Morrison at World Bank Open Data blog: “Was there a class of entrepreneurs emerging to take advantage of the economic possibilities offered by open data, were investors keen to back such companies, were governments tuned to and responsive to the demands of such companies, and what were some of the key financing challenges and opportunities in emerging markets? As we began our work on the concept of an Open Fund, we partnered with Ennovent (India), MDIF (East Asia and Latin America) and Digital Data Divide (Africa) to conduct short market surveys to answer these questions, with a focus on trying to understand whether a financing gap truly existed in these markets. The studies were fairly quick (4-6 weeks) and reached only a small number of companies (193 in India, 70 in Latin America, 63 in South East Asia, and 41 in Africa – and not everybody responded) but the findings were fairly consistent.

  • Open data is still a very nascent concept in emerging markets. and there’s only a small class of entrepreneurs/investors that is aware of the economic possibilities; there’s a lot of work to do in the ‘enabling environment’
    • In many regions the distinction between open data, big data, and private sector generated/scraped/collected data was blurry at best among entrepreneurs and investors (some of our findings consequently are better indicators of  data-driven rather than open data-driven businesses)
  • There’s a small but growing number of open data-driven companies in all the markets we surveyed and these companies target a wide range of consumers/users and are active in multiple sectors
    • A large percentage of identified companies operate in sectors with high social impact – health and wellness, environment, agriculture, transport. For instance, in India, after excluding business analytics companies, a third of data companies seeking financing are in healthcare and a fifth in food and agriculture, and some of them have the low-income population or the rural segment of India as an intended beneficiary segment. In Latin America, the number of companies in business services, research and analytics was closely followed by health, environment and agriculture. In Southeast Asia, business, consumer services, and transport came out in the lead.
    • We found the highest number of companies in Latin America and Asia with the following countries leading the way – Mexico, Chile, and Brazil, with Colombia and Argentina closely behind in Latin America; and India, Indonesia, Philippines, and Malaysia in Asia
  • An actionable pipeline of data-driven companies exists in Latin America and in Asia
    • We heard demand for different kinds of financing (equity, debt, working capital) but the majority of the need was for equity and quasi-equity in amounts ranging from $100,000 to $5 million USD, with averages of between $2 and $3 million USD depending on the region.
  • There’s a significant financing gap in all the markets
    • The investment sizes required, while they range up to several million dollars, are generally small. Analysis of more than 300 data companies in Latin America and Asia indicates a total estimated need for financing of more than $400 million
  • Venture capitals generally don’t recognize data as a separate sector and club data-driven companies with their standard information communication technology (ICT) investments
    • Interviews with founders suggest that moving beyond seed stage is particularly difficult for data-driven startups. While many companies are able to cobble together an initial seed round augmented by bootstrapping to get their idea off the ground, they face a great deal of difficulty when trying to raise a second, larger seed round or Series A investment.
    • From the perspective of startups, investors favor banal e-commerce (e.g., according toTech in Asia, out of the $645 million in technology investments made public across the region in 2013, 92% were related to fashion and online retail) or consumer service startups and ignore open data-focused startups even if they have a strong business model and solid key performance indicators. The space is ripe for a long-term investor with a generous risk appetite and multiple bottom line goals.
  • Poor data quality was the number one issue these companies reported.
    • Companies reported significant waste and inefficiency in accessing/scraping/cleaning data.

The analysis below borrows heavily from the work done by the partners. We should of course mention that the findings are provisional and should not be considered authoritative (please see the section on methodology for more details)….(More).”

Bloomberg Philanthropies Launches $100 Million Data for Health Program in Developing Countries

Curated on Posted on by Stefaan Verhulst

Press Release: “Bloomberg Philanthropies, in partnership with the Australian government, is launching Data for Health, a $100 million initiative that will enable 20 low- and middle-income countries to vastly improve public health data collection.  Each year the World Health Organization estimates that 65% of all deaths worldwide – 35 million each year – go unrecorded. Millions more deaths lack a documented cause. This gap in data creates major obstacles for understanding and addressing public health problems. The Data for Health initiative seeks to provide governments, aid organizations, and public health leaders with tools and systems to better collect data – and use it to prioritize health challenges, develop policies, deploy resources, and measure success. Over the next four years, Data for Health aims to help 1.2 billion people in 20 countries across Africa, Asia, and Latin America live healthier, longer lives….

“Australia’s partnership on Data for Health coincides with the launch of innovationXchange, a new initiative to embrace exploration, experimentation, and risk through a focus on innovation,” said the Hon Julie Bishop MP, Australia’s Minister for Foreign Affairs. “Greater innovation in development assistance will allow us to do a better job of tackling the world’s most daunting problems, such as a lack of credible health data.”

In addition to improving the recording of births and deaths, Data for Health will support new mechanisms for conducting public health surveys. These surveys will monitor major risk factors for early death, including non-communicable diseases (chronic diseases that are not transmitted from person to person such as cancer and diabetes). With information from these surveys, illness caused by day-to-day behaviors such as tobacco use and poor nutrition habits can be targeted, addressed and prevented. Data for Health will take advantage of the wide-spread use of mobile phone devices in developing countries to enhance the efficiency of traditional household surveys, which are typically time-consuming and expensive…(More)”

Sensor Law

Curated on Posted on by Stefaan Verhulst

Paper by Sandra Braman: “For over two decades, information policy-making for human society has been increasingly supplemented, supplanted, and/or superceded by machinic decision-making; over three decades since legal decision-making has been explicitly put in place to serve machinic rather than social systems; and over four decades since designers of the Internet took the position that they were serving non-human (machinic, or daemon) users in addition to humans. As the “Internet of Things” becomes more and more of a reality, these developments increasingly shape the nature of governance itself. This paper’s discussion of contemporary trends in these diverse modes of human-computer interaction at the system level — interactions between social systems and technological systems — introduces the changing nature of the law as a sociotechnical problem in itself. In such an environment, technological innovations are often also legal innovations, and legal developments require socio-technical analysis as well as social, legal, political, and cultural approaches.

Examples of areas in which sensors are already receiving legal attention are rife. A non-comprehensive listing includes privacy concerns beginning but not ending with those raised by sensors embedded in phones and geolocation devices, which are the most widely discussed and those of which the public is most aware. Sensor issues arise in environmental law, health law, marine law, intellectual property law, and as they are raised by new technologies in use for national security purposes that include those confidence- and security-building measures intended for peacekeeping. They are raised by liability issues for objects that range from cars to ovens. And sensor issues are at the core of concerns about “telemetric policing,” as that is coming into use not only in North America and Europe, but in societies such as that of Brazil as well.

Sensors are involved in every stage of legal processes, from identification of persons of interest to determination of judgments and consequences of judgments. Their use significantly alters the historically-developed distinction among types of decision-making meant to come into use at different stages of the process, raising new questions about when, and how, human decision-making needs to dominate and when, and how, technological innovation might need to be shaped by the needs of social rather than human systems.

This paper will focus on the legal dimensions of sensors used in ubiquitous embedded computing….(More)”