Fighting Illegal Fishing With Big Data

Curated on Posted on by Stefaan Verhulst

Emily Matchar in Smithsonian: “In many ways, the ocean is the Wild West. The distances are vast, the law enforcement agents few and far between, and the legal jurisdiction often unclear. In this environment, illegal activity flourishes. Illegal fishing is so common that experts estimate as much as a third of fish sold in the U.S. was fished illegally. This illegal fishing decimates the ocean’s already dwindling fish populations and gives rise to modern slavery, where fishermen are tricked onto vessels and forced to work, sometimes for years.

A new use of data technology aims to help curb these abuses by shining a light on the high seas. The technology uses ships’ satellite signals to detect instances of transshipment, when two vessels meet at sea to exchange cargo. As transshipment is a major way illegally caught fish makes it into the legal supply chain, tracking it could potentially help stop the practice.

“[Transshipment] really allows people to do something out of sight,” says David Kroodsma, the research program director at Global Fishing Watch, an online data platform launched by Google in partnership with the nonprofits Oceana and SkyTruth. “It’s something that obscures supply chains. It’s basically being able to do things without any oversight. And that’s a problem when you’re using a shared resource like the oceans.”

Global Fishing Watch analyzed some 21 billion satellite signals broadcast by ships, which are required to carry transceivers for collision avoidance, from between 2012 and 2016. It then used an artificial intelligence system it created to identify which ships were refrigerated cargo vessels (known in the industry as “reefers”). They then verified this information with fishery registries and other sources, eventually identifying 794 reefers—90 percent of the world’s total number of such vessels. They tracked instances where a reefer and a fishing vessel were moving at similar speeds in close proximity, labeling these instances as “likely transshipments,” and also traced instances where reefers were traveling in a way that indicated a rendezvous with a fishing vessel, even if no fishing vessel was present—fishing vessels often turn off their satellite systems when they don’t want to be seen. All in all there were more than 90,000 likely or potential transshipments recorded.

Even if these encounters were in fact transshipments, they would not all have been for nefarious purposes. They may have taken place to refuel or load up on supplies. But looking at the patterns of where the potential transshipments happen is revealing. Very few are seen close to the coasts of the U.S., Canada and much of Europe, all places with tight fishery regulations. There are hotspots off the coast of Peru and Argentina, all over Africa, and off the coast of Russia. Some 40 percent of encounters happen in international waters, far enough off the coast that no country has jurisdiction.

The tracked reefers were flying flags from some 40 different countries. But that doesn’t necessarily tell us much about where they really come from. Nearly half of the reefers tracked were flying “flags of convenience,” meaning they’re registered in countries other than where the ship’s owners are from to take advantage of those countries’ lax regulations….(More)”

Read more: http://www.smithsonianmag.com/innovation/fighting-illegal-fishing-big-data-180962321/#7eCwGrGS5v5gWjFz.99
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Denmark is appointing an ambassador to big tech

Curated on Posted on by Stefaan Verhulst

Matthew Hughes in The Next Web: “Question: Is Facebook a country? It sounds silly, but when you think about it, it does have many attributes in common with nation states. For starters, it’s got a population that’s bigger than that of India, and its 2016 revenue wasn’t too far from Estonia’s GDP. It also has a ‘national ethos’. If America’s philosophy is capitalism, Cuba’s is communism, and Sweden’s is social democracy, Facebook’s is ‘togetherness’, as corny as that may sound.

 Given all of the above, is it really any surprise that Denmark is considering appointing a ‘big tech ambassador’ whose job is to establish and manage the country’s relationship with the world’s most powerful tech companies?

Denmark’s “digital ambassador” is a first. No country has ever created such a role. Their job will be to liase with the likes of Google, Twitter, Facebook.

Given the fraught relationship many European countries have with American big-tech – especially on issues of taxation, privacy, and national security – Denmark’s decision to extend an olive branch seems sensible.

Speaking with the Washington Post, Danish Foreign Minister Anders Samuelsen said, “just as we engage in a diplomatic dialogue with countries, we also need to establish and prioritize comprehensive relations with tech actors, such as Google, Facebook, Apple and so on. The idea is, we see a lot of companies and new technologies that will in many ways involve and be part of everyday life of citizens in Denmark.”….(More)”

From Nairobi to Manila, mobile phones are changing the lives of bus riders

Curated on Posted on by Stefaan Verhulst
Shomik Mehnidrata at Transport for Development Blog: “Every day around the world, millions of people rely on buses to get around. In many cities, these services carry the bulk of urban trips, especially in Africa and Latin America. They are known by many different names—matatus, dalalas, minibus taxis, colectivos, diablos rojos, micros, etc.—but all have one thing in common: they are either hardly regulated… or not regulated at all. Although buses play a critical role in the daily life of many urban dwellers, there are a variety of complaints that have spurred calls for improvement and reform.

However, we are now witnessing a different, more organic kind of change that is disrupting the world of informal buses using ubiquitous cheap sensors and mobile technology. One hotbed of innovation is Nairobi, Kenya’s bustling capital. Two years ago, Nairobi made a splash in the world of urban transport by mapping all the routes of informal matatus. Other countries have sought to replicate this model, with open source tools and crowdsourcing supporting similar efforts in Mexico, Manila, and beyond. Back in Nairobi, the Magic Bus app helps commuters use sms services to reserve and pay for seats in matatus; in September 2016, MagicBus’ potential for easing commuter pain in the Kenyan capital was rewarded with a $1 million prize. Other programs implemented in collaboration with insurers and operators are experimenting with on-board sensors to identify and correct dangerous driver behavior such as sudden braking and acceleration. Ma3Route, also in Nairobi (there is a pattern here!) used crowdsourcing to identify dangerous drivers as well as congestion. At the same time, operators are upping their game: using technology to improve system management, control and routing in La Paz, and working with universities to improve their financial planning and capabilities in Cape Town.

Against this backdrop, the question is then: can these ongoing experimental initiatives offer a coherent alternative to formal reform? …(More)”.

Connecting the dots: Building the case for open data to fight corruption

Curated on Posted on by Stefaan Verhulst

Web Foundation: “This research, published with Transparency International, measures the progress made by 5 key countries in implementing the G20 Anti-Corruption Open Data Principles.

These principles, adopted by G20 countries in 2015, committed countries to increasing and improving the publication of public information, driving forward open data as a tool in anti-corruption efforts.

However, this research – looking at Brazil, France, Germany, Indonesia and South Africa – finds a disappointing lack of progress. No country studied has released all the datasets identified as being key to anti-corruption and much of the information is hard to find and hard use.

Key findings:

  • No country released all anti-corruption datasets
  • Quality issues means data is often not useful or useable
  • Much of the data is not published in line with open data standards, making comparability difficult
  • In many countries there is a lack of open data skills among officials in charge of anti-corruption initiatives

Download the overview report here (PDF), and access the individual country case studies Brazil; France; Germany; Indonesia and South Africa… (More)”

Data Disrupts Corruption

Curated on Posted on by Stefaan Verhulst

Carlos Santiso & Ben Roseth at Stanford Social Innovation Review: “…The Panama Papers scandal demonstrates the power of data analytics to uncover corruption in a world flooded with terabytes needing only the computing capacity to make sense of it all. The Rousse impeachment illustrates how open data can be used to bring leaders to account. Together, these stories show how data, both “big” and “open,” is driving the fight against corruption with fast-paced, evidence-driven, crowd-sourced efforts. Open data can put vast quantities of information into the hands of countless watchdogs and whistleblowers. Big data can turn that information into insight, making corruption easier to identify, trace, and predict. To realize the movement’s full potential, technologists, activists, officials, and citizens must redouble their efforts to integrate data analytics into policy making and government institutions….

Making big data open cannot, in itself, drive anticorruption efforts. “Without analytics,” a 2014 White House report on big data and individual privacy underscored, “big datasets could be stored, and they could be retrieved, wholly or selectively. But what comes out would be exactly what went in.”

In this context, it is useful to distinguish the four main stages of data analytics to illustrate its potential in the global fight against corruption: Descriptive analytics uses data to describe what has happened in analyzing complex policy issues; diagnostic analytics goes a step further by mining and triangulating data to explain why a specific policy problem has happened, identify its root causes, and decipher underlying structural trends; predictive analytics uses data and algorithms to predict what is most likely to occur, by utilizing machine learning; and prescriptive analytics proposes what should be done to cause or prevent something from happening….

Despite the big data movement’s promise for fighting corruption, many challenges remain. The smart use of open and big data should focus not only on uncovering corruption, but also on better understanding its underlying causes and preventing its recurrence. Anticorruption analytics cannot exist in a vacuum; it must fit in a strategic institutional framework that starts with quality information and leads to reform. Even the most sophisticated technologies and data innovations cannot prevent what French novelist Théophile Gautier described as the “inexplicable attraction of corruption, even amongst the most honest souls.” Unless it is harnessed for improvements in governance and institutions, data analytics will not have the impact that it could, nor be sustainable in the long run…(More)”.

The Paradox of Community Power: Cultural Processes and Elite Authority in Participatory Governance

Curated on Posted on by Stefaan Verhulst

Jeremy R. Levine in Social Forces: “From town halls to public forums, disadvantaged neighborhoods appear more “participatory” than ever. Yet increased participation has not necessarily resulted in increased influence. This article, drawing on a four-year ethnographic study of redevelopment politics in Boston, presents an explanation for the decoupling of participation from the promise of democratic decision-making. I find that poor urban residents gain the appearance of power and status by invoking and policing membership in “the community”—a boundary sometimes, though not always, implicitly defined by race. But this appearance of power is largely an illusion. In public meetings, government officials can reinforce their authority and disempower residents by exploiting the fact that the boundary demarcating “the community” lacks a standardized definition. When officials laud “the community” as an abstract ideal rather than a specific group of people, they reduce “the community process” to a bureaucratic procedure. Residents appear empowered, while officials retain ultimate decision-making authority. I use the tools of cultural sociology to make sense of these findings and conclude with implications for the study of participatory governance and urban inequality….(More)”.

Data in public health

Curated on Posted on by Stefaan Verhulst

Jeremy Berg in Science: “In 1854, physician John Snow helped curtail a cholera outbreak in a London neighborhood by mapping cases and identifying a central public water pump as the potential source. This event is considered by many to represent the founding of modern epidemiology. Data and analysis play an increasingly important role in public health today. This can be illustrated by examining the rise in the prevalence of autism spectrum disorders (ASDs), where data from varied sources highlight potential factors while ruling out others, such as childhood vaccines, facilitating wise policy choices…. A collaboration between the research community, a patient advocacy group, and a technology company (www.mss.ng) seeks to sequence the genomes of 10,000 well-phenotyped individuals from families affected by ASD, making the data freely available to researchers. Studies to date have confirmed that the genetics of autism are extremely complicated—a small number of genomic variations are closely associated with ASD, but many other variations have much lower predictive power. More than half of siblings, each of whom has ASD, have different ASD-associated variations. Future studies, facilitated by an open data approach, will no doubt help advance our understanding of this complex disorder….

A new data collection strategy was reported in 2013 to examine contagious diseases across the United States, including the impact of vaccines. Researchers digitized all available city and state notifiable disease data from 1888 to 2011, mostly from hard-copy sources. Information corresponding to nearly 88 million cases has been stored in a database that is open to interested parties without restriction (www.tycho.pitt.edu). Analyses of these data revealed that vaccine development and systematic vaccination programs have led to dramatic reductions in the number of cases. Overall, it is estimated that ∼100 million cases of serious childhood diseases have been prevented through these vaccination programs.

These examples illustrate how data collection and sharing through publication and other innovative means can drive research progress on major public health challenges. Such evidence, particularly on large populations, can help researchers and policy-makers move beyond anecdotes—which can be personally compelling, but often misleading—for the good of individuals and society….(More)”

Chile’s ‘Uber of Recycling’ Is Sparking a Recycling Revolution

Curated on Posted on by Stefaan Verhulst
Tomas Urbina at Motherboard: “In 2015, after finishing a soccer game in Chile’s capital, Santiago, engineering student Cristián Lara and his friends noticed an older man picking through a dumpster nearby. He was searching for anything that could be recycled, and loading it onto his bike.

“It looked like incredibly hard work,” Lara recalled. After talking to the man, it turns out he had been doing the same work for 10 years, and was still living in poverty.

The encounter gave Lara an idea. What if there was a way to connect the collector on the street directly to the massive waste streams that exist in Chile, and to the companies that pay decent money for recyclables?

“We knew we had to do something,” said 24-year-old Lara. That’s how a recycling app startup, called ReciclApp, was born. The app launched last August. Since then, the bearded young entrepreneur has been on a mission. Standing in their section of an open collaborative workspace on the fifth floor of the luminous new innovation centre at Santiago’s Catholic University, Lara let his glee shine through in his elevator pitch for the app.

“It’s the Uber of recycling,” he said.

It works like this: individuals, businesses, and institutions download the free app. Once they have cans, boxes or bottles to get rid of, they declare specific numbers in the app and choose a date and time period for pickup. From that data, the company creates and prints out routes for the collectors they work with. There are now an average of 200 collectors working with ReciclApp across Chile, and about 1,000 app users in the country.

For collectors, it’s an efficient route with guaranteed recyclables, and they keep all the money they make. Lara’s team cuts out the middleman transporters who would previously take the material to large recycling companies. ReciclApp even has designated storage centres where collectors can leave material before a truck from large recyclers shows up….

Lara estimates that there are about 100,000 people trying to earn money from recycling in Chile. Those that work with ReciclApp have more than doubled their recycling earnings on average from about $100 USD per month to $250 USD. But even that, Lara admitted, is a small gain when you consider Chile’s high cost of living….

ReciclApp intends to change that. “We’re going to start hiring waste collectors, so they’ll have a set wage, a schedule, and can earn extra income based on how much they collect and how many homes or businesses they visit,” said ReciclApp’s director of operations, 25-year-old Manuel Fonseca….

For Fuentes, 40, the biggest improvement is how she’s treated. “Families value us as workers now, not as the lady who asks for donations and picks through the garbage,” she said. “We spent too many years hidden in the shadows. I feel different now. I’m not embarrassed of my work the way I used to be.”….(More)”

Using Algorithms To Predict Gentrification

Curated on Posted on by Stefaan Verhulst

Tanvi Misra in CityLab: “I know it when I see it,” is as true for gentrification as it is for pornography. Usually, it’s when a neighborhood’s property values and demographics are already changing that the worries about displacement set in—rousing housing advocates and community organizers to action. But by that time, it’s often hard to pause, and put in safeguards for the neighborhood’s most vulnerable residents.

But what if there was an early warning system that detects where price appreciation or decline is about to occur? Predictive tools like this have been developed around the country, most notably by researchers in San Francisco. And their value is clear: city leaders and non-profits pinpoint where to preserve existing affordable housing, where to build more, and where to attract business investment ahead of time. But they’re often too academic or too obscure, which is why it’s not yet clear how they’re being used by policymakers and planners.

That’s the problem Ken Steif, at the University of Pennsylvania, is working to solve, in partnership with Alan Mallach, from the Center for Community Progress.

Mallach’s non-profit focused on revitalizing distressed neighborhoods, particularly in “legacy cities.” These are towns like St. Louis, Flint, Dayton, and Baltimore, that have experienced population loss and economic contraction in recent years, and suffer from property vacancies, blight, and unemployment. Mallach is interested in understanding which neighborhoods are likely to continue down that path, and which ones will do a 180-degree turn. Right now, he can intuitively make those predictions, based on his observations on neighborhood characteristics like housing stock, median income, and race. But an objective assessment can help confirm or deny his hypotheses.

That’s where Steif comes in. Having consulted with cities and non-profits on place-based data analytics, Steif has developed a number of algorithms that predict the movement of housing markets using expensive private data from entities like Zillow. Mallach suggested he try his algorithms on Census data, which is free and standardized.

The phenomenon he tested was  ‘endogenous gentrification’—this idea that an increase in home prices moves from wealthy neighborhoods to less expensive ones in its vicinity, like a wave. ..Steif used Census data from 1990 and 2000 to predict housing price change in 2010 in 29 big and small legacy cities. His algorithms took into account the relationship between the median home prices of a census tract to the ones around it, the proximity of census tracts to high-cost areas, and the spatial patterns in home price distribution. It also folded in variables like race, income and housing supply, among others.

After cross-checking the 2010 prediction with actual home prices, he projected the neighborhood change all the way to 2020. His algorithms were able to compute the speed and breadth of the wave of gentrification over time reasonably well, overall…(More)”.

Why Big Data Is a Big Deal for Cities

Curated on Posted on by Stefaan Verhulst

John M. Kamensky in Governing: “We hear a lot about “big data” and its potential value to government. But is it really fulfilling the high expectations that advocates have assigned to it? Is it really producing better public-sector decisions? It may be years before we have definitive answers to those questions, but new research suggests that it’s worth paying a lot of attention to.

University of Kansas Prof. Alfred Ho recently surveyed 65 mid-size and large cities to learn what is going on, on the front line, with the use of big data in making decisions. He found that big data has made it possible to “change the time span of a decision-making cycle by allowing real-time analysis of data to instantly inform decision-making.” This decision-making occurs in areas as diverse as program management, strategic planning, budgeting, performance reporting and citizen engagement.

Cities are natural repositories of big data that can be integrated and analyzed for policy- and program-management purposes. These repositories include data from public safety, education, health and social services, environment and energy, culture and recreation, and community and business development. They include both structured data, such as financial and tax transactions, and unstructured data, such as recorded sounds from gunshots and videos of pedestrian movement patterns. And they include data supplied by the public, such as the Boston residents who use a phone app to measure road quality and report problems.

These data repositories, Ho writes, are “fundamental building blocks,” but the challenge is to shift the ownership of data from separate departments to an integrated platform where the data can be shared.

There’s plenty of evidence that cities are moving in that direction and that they already are systematically using big data to make operational decisions. Among the 65 cities that Ho examined, he found that 49 have “some form of data analytics initiatives or projects” and that 30 have established “a multi-departmental team structure to do strategic planning for these data initiatives.”….The effective use of big data can lead to dialogs that cut across school-district, city, county, business and nonprofit-sector boundaries. But more importantly, it provides city leaders with the capacity to respond to citizens’ concerns more quickly and effectively….(More)”