science

Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment

Curated on September 28, 2013August 3, 2018 by Stefaan Verhulst

New Paper by Bhaveer Bhana, Stephen Flowerday, and Aharon Satt in the International Journal of Distributed Sensor Networks: “The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and non-emergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system.
The study illustrates how participatory crowdsourcing (specifically humans as sensors) can be used as a Smart City initiative focusing on public safety by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision making when selecting an optimal crowdsourcing initiative. Public safety data quality criteria were developed to assess and identify the problems affecting data quality.
This study is guided by design science methodology and applies three driving theories: the Data Information Knowledge Action Result (DIKAR) model, the characteristics of a Smart City, and a credible Data Quality Framework. Four critical success factors were developed to ensure high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.”

Mobile phone data are a treasure-trove for development

Curated on September 27, 2013August 3, 2018 by Stefaan Verhulst

Paul van der Boor and Amy Wesolowski in SciDevNet: “Each of us generates streams of digital information — a digital ‘exhaust trail’ that provides real-time information to guide decisions that affect our lives. For example, Google informs us about traffic by using both its ‘My Location’ feature on mobile phones and third-party databases to aggregate location data. BBVA, one of Spain’s largest banks, analyses transactions such as credit card payments as well as ATM withdrawals to find out when and where peak spending occurs.This type of data harvest is of great value. But, often, there is so much data that its owners lack the know-how to process it and fail to realise its potential value to policymakers.
Meanwhile, many countries, particularly in the developing world, have a dearth of information. In resource-poor nations, the public sector often lives in an analogue world where piles of paper impede operations and policymakers are hindered by uncertainty about their own strengths and capabilities.Nonetheless, mobile phones have quickly pervaded the lives of even the poorest: 75 per cent of the world’s 5.5 billion mobile subscriptions are in emerging markets. These people are also generating digital trails of anything from their movements to mobile phone top-up patterns. It may seem that putting this information to use would take vast analytical capacity. But using relatively simple methods, researchers can analyse existing mobile phone data, especially in poor countries, to improve decision-making.
Think of existing, available data as low-hanging fruit that we — two graduate students — could analyse in less than a month. This is not a test of data-scientist prowess, but more a way of saying that anyone could do it.
There are three areas that should be ‘low-hanging fruit’ in terms of their potential to dramatically improve decision-making in information-poor countries: coupling healthcare data with mobile phone data to predict disease outbreaks; using mobile phone money transactions and top-up data to assess economic growth; and predicting travel patterns after a natural disaster using historical movement patterns from mobile phone data to design robust response programmes.
Another possibility is using call-data records to analyse urban movement to identify traffic congestion points. Nationally, this can be used to prioritise infrastructure projects such as road expansion and bridge building.
The information that these analyses could provide would be lifesaving — not just informative or revenue-increasing, like much of this work currently performed in developed countries.
But some work of high social value is being done. For example, different teams of European and US researchers are trying to estimate the links between mobile phone use and regional economic development. They are using various techniques, such as merging night-time satellite imagery from NASA with mobile phone data to create behavioural fingerprints. They have found that this may be a cost-effective way to understand a country’s economic activity and, potentially, guide government spending.
Another example is given by researchers (including one of this article’s authors) who have analysed call-data records from subscribers in Kenya to understand malaria transmission within the country and design better strategies for its elimination. [1]
In this study, published in Science, the location data of the mobile phones of more than 14 million Kenyan subscribers was combined with national malaria prevalence data. After identifying the sources and sinks of malaria parasites and overlaying these with phone movements, analysis was used to identify likely transmission corridors. UK scientists later used similar methods to create different epidemic scenarios for the Côte d’Ivoire.”

Prizes and Productivity: How Winning the Fields Medal Affects Scientific Output

Curated on September 23, 2013November 1, 2018 by Stefaan Verhulst

New NBER working paper by George J. Borjas and Kirk B. Doran: “Knowledge generation is key to economic growth, and scientific prizes are designed to encourage it. But how does winning a prestigious prize affect future output? We compare the productivity of Fields medalists (winners of the top mathematics prize) to that of similarly brilliant contenders. The two groups have similar publication rates until the award year, after which the winners’ productivity declines. The medalists begin to “play the field,” studying unfamiliar topics at the expense of writing papers. It appears that tournaments can have large post-prize effects on the effort allocation of knowledge producers.”

The Contours of Crowd Capability

Curated on September 20, 2013December 11, 2018 by Stefaan Verhulst

New paper by Prashant Shukla and John Prpi: “The existence of dispersed knowledge has been a subject of inquiry for more than six decades. Despite the longevity of this rich research tradition, the “knowledge problem” has remained largely unresolved both in research and practice, and remains “the central theoretical problem of all social science”. However, in the 21st century, organizations are presented with opportunities through technology to potentially benefit from the dispersed knowledge problem to some extent. One such opportunity is represented by the recent emergence of a variety of crowd-engaging information systems (IS).

In this vein, Crowdsourcing is being widely studied in numerous contexts, and the knowledge generated from these IS phenomena is well-documented. At the same time, other organizations are leveraging dispersed knowledge by putting in place IS-applications such as Predication Markets to gather large sample-size forecasts from within and without the organization. Similarly, we are also observing many organizations using IS-tools such as “Wikis” to access the knowledge of dispersed populations within the boundaries of the organization. Further still, other organizations are applying gamification techniques to accumulate Citizen Science knowledge from the public at large through IS.
Among these seemingly disparate phenomena, a complex ecology of crowd- engaging IS has emerged, involving millions of people all around the world generating knowledge for organizations through IS. However, despite the obvious scale and reach of this emerging crowd-engagement paradigm, there are no examples of research (as far as we know), that systematically compares and contrasts a large variety of these existing crowd-engaging IS-tools in one work. Understanding this current state of affairs, we seek to address this significant research void by comparing and contrasting a number of the crowd-engaging forms of IS currently available for organizational use.

To achieve this goal, we employ the Theory of Crowd Capital as a lens to systematically structure our investigation of crowd-engaging IS. Employing this parsimonious lens, we first explain how Crowd Capital is generated through Crowd Capability in organizations. Taking this conceptual platform as a point of departure, in Section 3, we offer an array of examples of IS currently in use in modern practice to generate Crowd Capital. We compare and contrast these emerging IS techniques using the Crowd Capability construct, therein highlighting some important choices that organizations face when entering the crowd- engagement fray. This comparison, which we term “The Contours of Crowd Capability”, can be used by decision-makers and researchers alike, to differentiate among the many extant methods of Crowd Capital generation. At the same time, our comparison also illustrates some important differences to be found in the internal organizational processes that accompany each form of crowd-engaging IS. In section 4, we conclude with a discussion of the limitations of our work.”

Smarter Than You Think: How Technology is Changing Our Minds for the Better

Curated on September 18, 2013November 7, 2018 by Stefaan Verhulst

New book by Clive Thompson: “It’s undeniable—technology is changing the way we think. But is it for the better? Amid a chorus of doomsayers, Clive Thompson delivers a resounding “yes.” The Internet age has produced a radical new style of human intelligence, worthy of both celebration and analysis. We learn more and retain it longer, write and think with global audiences, and even gain an ESP-like awareness of the world around us. Modern technology is making us smarter, better connected, and often deeper—both as individuals and as a society.
In Smarter Than You Think Thompson shows that every technological innovation—from the written word to the printing press to the telegraph—has provoked the very same anxieties that plague us today. We panic that life will never be the same, that our attentions are eroding, that culture is being trivialized. But as in the past, we adapt—learning to use the new and retaining what’s good of the old.”

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.”

Coase’s theories predicted Internet’s impact on how business is done

Curated on September 9, 2013August 3, 2018 by Stefaan Verhulst

Don Tapscott in The Globe and Mail: “Renowned economist Ronald Coase died last week at the age of 102. Among his many achievements, Mr. Coase was awarded the 1991 Nobel Prize in Economics, largely for his inspiring 1937 paper The Nature of the Firm. The Nobel committee applauded the academic for his “discovery and clarification of the significance of transaction costs … for the institutional structure and functioning of the economy.”
Mr. Coase’s enduring legacy may well be that 60 years later, his paper and theories help us understand the Internet’s impact on business, the economy and all our institutions… Mr. Coase wondered why there was no market within the firm. Why is it unprofitable to have each worker, each step in the production process, become an independent buyer and seller? Why doesn’t the draftsperson auction their services to the engineer? Why is it that the engineer does not sell designs to the highest bidder? Mr. Coase argued that preventing this from happening created marketplace friction.
Mr. Coase argued that this friction gave rise to transaction costs – or to put it more broadly, collaboration or relationship costs. There are three types of these relationship costs. First are search costs, such as the hunt for appropriate suppliers. Second are contractual costs, including price and contract negotiations. Third are the co-ordination costs of meshing the different products and processes.
The upshot is that most vertically integrated corporations found it cheaper and simpler to perform most functions in-house, rather than incurring the cost, hassle and risk of constant transactions with outside partners….This is no longer the case. Many behemoths have lost market share to more supple competitors. Digital technologies slash transaction and collaboration costs. Smart companies are making their boundaries porous, using the Internet to harness knowledge, resources and capabilities outside the company. Everywhere,leading firms set a context for innovation and then invite their customers, partners and other third parties to co-create their products and services.
Today’s economic engines are Internet-based clusters of businesses. While each company retains its identity, companies function together, creating more wealth than they could ever hope to create individually. Where corporations were once gigantic, new business ecosystems tend toward the amorphous.
Procter & Gamble now gets 60 per cent of its innovation from outside corporate walls. Boeing has built a massive ecosystem to design and manufacture jumbo jets. China’s motorcycle industry, which consists of dozens of companies collaborating with no single company pulling the strings, now comprises 40 per cent of global motorcycle production.
Looked at one way, Amazon.com is a website with many employees that ships books. Looked at another way, however, Amazon is a vast ecosystem that includes authors, publishers, customers who write reviews for the site, delivery companies like UPS, and tens of thousands of affiliates that market products and arrange fulfilment through the Amazon network. Hundreds of thousands of people are involved in Amazon’s viral marketing network.
This is leading to the biggest change to the corporation in a century and altering how we orchestrate capability to innovate, create goods and services and engage with the world. From now on, the ecosystem itself, not the corporation per se, should serve as the point of departure for every business strategist seeking to understand the new economy – and for every manager, entrepreneur and investor seeking to prosper in it.
Nor does the Internet tonic apply only to corporations. The Web is dropping transaction costs everywhere – enabling networked approaches to almost every institution in society, from government, media, science and health care to our energy grid, transportation systems and institutions for global problem solving.
Governments can change from being vertically integrated, industrial-age bureaucracies to become networks. By releasing their treasures of raw data, governments can now become platforms upon which companies, NGOs, academics, foundations, individuals and other government agencies can collaborate to create public value…”

Political Scientists Acknowledge Need to Make Stronger Case for Their Field

Curated on September 6, 2013August 15, 2018 by Stefaan Verhulst

Beth McMurtrie in The Chronicle of Higher Education: “Back in March, Congress limited federal support for political-science research by the National Science Foundation to projects that promote national security or American economic interests. That decision was a victory for Sen. Tom Coburn, a Republican from Oklahoma who has long aimed to eliminate all NSF grants for political science, arguing that unlike the hard sciences it rarely produces concrete benefits to society.
Congress’s action has led to soul searching within the discipline about how effective academics have been in conveying the value of their work to the public. It has also revived a longstanding debate among political scientists about the shift toward more statistically sophisticated, mathematically esoteric research, and its usefulness outside of academe. Those discussions were out front at the annual conference of the American Political Science Association, held here last week.
Rogers M. Smith, a political-science professor at the University of Pennsylvania, was one of 13 members of a panel that discussed the controversy over NSF money for political-science studies. He put the problem bluntly: “We need to make a better case for ourselves.”
Few on the panel, in fact, seemed to think that political science had done a good job on that front. The association has created a task force—led by Arthur Lupia, a political-science professor at the University of Michigan at Ann Arbor—to improve public perceptions of political science’s value. He said his colleagues could learn from organizations like the American Association for the Advancement of Science, which holds special sessions for the news media at its annual conference to explain the work of its members to the public.”