Satellite Images and Shadow Analysis: How The Times Verifies Eyewitness Videos


 Christoph Koettl at the New York Times: “Was a video of a chemical attack really filmed in Syria? What time of day did an airstrike happen? Which military unit was involved in a shooting in Afghanistan? Is this dramatic image of glowing clouds really showing wildfires in California?

These are some of the questions the video team at The New York Times has to answer when reviewing raw eyewitness videos, often posted to social media. It can be a highly challenging process, as misinformation shared through digital social networks is a serious problem for a modern-day newsroom. Visual information in the digital age is easy to manipulate, and even easier to spread.

What is thus required for conducting visual investigations based on social media content is a mix of traditional journalistic diligence and cutting-edge internet skills, as can be seen in our recent investigation into the chemical attack in Douma, Syria.

 The following provides some insight into our video verification process. It is not a comprehensive overview, but highlights some of our most trusted techniques and tools….(More)”.

Don’t forget people in the use of big data for development


Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)1. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.

Pick your poison: How a crowdsourcing app helped identify and reduce food poisoning


Alex Papas at LATimes: “At some point in life, almost everyone will have experienced the debilitating effects of a foodborne illness. Whether an under-cooked chicken kebab, an E. coli infested salad or some toxic fish, a good day can quickly become a loathsome frenzy of vomiting and diarrhoea caused by poorly prepared or poorly kept food.

Since 2009, the website iwaspoisoned.com has allowed victims of food-poisoning victims to help others avoid such an ordeal by crowd-sourcing food illnesses on one easy-to-use, consumer-led platform.

Whereas previously a consumer struck down by food poisoning may have been limited to complaining to the offending food outlet, IWasPosioned allows users to submit detailed reports of food-poisoning incidents – including symptoms, location and space to describe the exact effects and duration of the incident. The information is then transferred in real time to public health organisations and food industry groups, who  use the data to flag potentially dangerous foodborne illness before a serious outbreak occurs.

In the United States alone, where food safety standards are among the highest in the world, there are still 48 million cases of food poisoning per year. From those cases, 128,000 result in hospitalisation and 3,000 in death, according to data from the U.S. Food and Drug Association.

Back in 2008 the site’s founder, Patrick Quade, himself fell foul to food poisoning after eating a BLT from a New York deli which caused him to be violently ill. Concerned by the lack of options for reporting such incidents, he set up the novel crowdsourcing platform, which also aims at improving transparency in the food monitoring industry.

The emergence of IWasPoisoned is part of the wider trend of consumers taking revenge against companies via digital platforms, which spans various industries. In the case of IWasPoisoned, reports of foodborne illness have seriously tarnished the reputations of several major food retailers….(More)”.

How Smart Should a City Be? Toronto Is Finding Out


Laura Bliss at CityLab: “A data-driven “neighborhood of the future” masterminded by a Google corporate sibling, the Quayside project could be a milestone in digital-age city-building. But after a year of scandal in Silicon Valley, questions about privacy and security remain…

Quayside was billed as “the world’s first neighborhood built from the internet up,” according to Sidewalk Labs’ vision plan, which won the RFP to develop this waterfront parcel. The startup’s pitch married “digital infrastructure” with an utopian promise: to make life easier, cheaper, and happier for Torontonians.

Everything from pedestrian traffic and energy use to the fill-height of a public trash bin and the occupancy of an apartment building could be counted, geo-tagged, and put to use by a wifi-connected “digital layer” undergirding the neighborhood’s physical elements. It would sense movement, gather data, and send information back to a centralized map of the neighborhood. “With heightened ability to measure the neighborhood comes better ways to manage it,” stated the winning document. “Sidewalk expects Quayside to become the most measurable community in the world.”

“Smart cities are largely an invention of the private sector—an effort to create a market within government,” Wylie wrote in Canada’s Globe and Mail newspaper in December 2017. “The business opportunities are clear. The risks inherent to residents, less so.” A month later, at a Toronto City Council meeting, Wylie gave a deputation asking officials to “ensure that the data and data infrastructure of this project are the property of the city of Toronto and its residents.”

In this case, the unwary Trojans would be Waterfront Toronto, the nonprofit corporation appointed by three levels of Canadian government to own, manage, and build on the Port Lands, 800 largely undeveloped acres between downtown and Lake Ontario. When Waterfront Toronto gave Sidewalk Labs a green light for Quayside in October, the startup committed $50 million to a one-year consultation, which was recently extended by several months. The plan is to submit a final “Master Innovation and Development Plan” by the end of this year.

That somewhat Orwellian vision of city management had privacy advocates and academics concerned from the the start. Bianca Wylie, the co-founder of the technology advocacy group Tech Reset Canada, has been perhaps the most outspoken of the project’s local critics. For the last year, she’s spoken up at public fora, written pointed op-edsand Medium posts, and warned city officials of what she sees as the “Trojan horse” of smart city marketing: private companies that stride into town promising better urban governance, but are really there to sell software and monetize citizen data.

But there has been no guarantee about who would own the data at the core of its proposal—much of which would ostensibly be gathered in public space. Also unresolved is the question of whether this data could be sold. With little transparency about what that means from the company or its partner, some Torontonians are wondering what Waterfront Toronto—and by extension, the public—is giving away….(More)”.

Palaces for the People: How Social Infrastructure Can Help Fight Inequality, Polarization, and the Decline of Civic Life


The Known Known


Book Review by Sue Halpern in The New York Review of Books of The Known Citizen: A History of Privacy in Modern America by Sarah E. Igo; Habeas Data: Privacy vs. the Rise of Surveillance Tech by Cyrus Farivar;  Beyond Abortion: Roe v. Wade and the Battle for Privacy by Mary Ziegler; Privacy’s Blueprint: The Battle to Control the Design of New Technologies by Woodrow Hartzog: “In 1999, when Scott McNealy, the founder and CEO of Sun Microsystems, declared, “You have zero privacy…get over it,” most of us, still new to the World Wide Web, had no idea what he meant. Eleven years later, when Mark Zuckerberg said that “the social norms” of privacy had “evolved” because “people [had] really gotten comfortable not only sharing more information and different kinds, but more openly and with more people,” his words expressed what was becoming a common Silicon Valley trope: privacy was obsolete.

By then, Zuckerberg’s invention, Facebook, had 500 million users, was growing 4.5 percent a month, and had recently surpassed its rival, MySpace. Twitter had overcome skepticism that people would be interested in a zippy parade of 140-character posts; at the end of 2010 it had 54 million active users. (It now has 336 million.) YouTube was in its fifth year, the micro-blogging platform Tumblr was into its third, and Instagram had just been created. Social media, which encouraged and relied on people to share their thoughts, passions, interests, and images, making them the Web’s content providers, were ascendant.

Users found it empowering to bypass, and even supersede, the traditional gatekeepers of information and culture. The social Web appeared to bring to fruition the early promise of the Internet: that it would democratize the creation and dissemination of knowledge. If, in the process, individuals were uploading photos of drunken parties, and discussing their sexual fetishes, and pulling back the curtain on all sorts of previously hidden personal behaviors, wasn’t that liberating, too? How could anyone argue that privacy had been invaded or compromised or effaced when these revelations were voluntary?

The short answer is that they couldn’t. And they didn’t. Users, who in the early days of social media were predominantly young, were largely guileless and unconcerned about privacy. In a survey of sixty-four of her students at Rochester Institute of Technology in 2006, Susan Barnes found that they “wanted to keep information private, but did not seem to realize that Facebook is a public space.” When a random sample of young people was asked in 2007 by researchers from the Pew Research Center if “they had any concerns about publicly posted photos, most…said they were not worried about risks to their privacy.” (This was largely before Facebook and other tech companies began tracking and monetizing one’s every move on- and offline.)

In retrospect, the tendencies toward disclosure and prurience online should not have been surprising….(More)”.

Following Fenno: Learning from Senate Candidates in the Age of Social Media and Party Polarization


David C.W. Parker  at The Forum: “Nearly 40 years ago, Richard Fenno published Home Style, a seminal volume explaining how members of Congress think about and engage in the process of representation. To accomplish his task, he observed members of Congress as they crafted and communicated their representational styles to the folks back home in their districts. The book, and Fenno’s ensuing research agenda, served as a clarion call to move beyond sophisticated quantitative analyses of roll call voting and elite interviews in Washington, D.C. to comprehend congressional representation. Instead, Fenno argued, political scientists are better served by going home with members of Congress where “their perceptions of their constituencies are shaped, sharpened, or altered” (Fenno 1978, p. xiii). These perceptions of constituencies fundamentally shape what members of Congress do at home and in Washington. If members of Congress are single-minded seekers of reelection, as we often assume, then political scientists must begin with the constituent relationship essential to winning reelection. Go home, Fenno says, to understand Congress.

There are many ways constituency relationships can be understood and uncovered; the preferred method for Fenno is participant observation, which he variously terms as “soaking and poking” or “just hanging around.” Although it sounds easy enough to sit and watch, good participant observation requires many considerations (as Fenno details in a thorough appendix to Home Style). In this appendix, and in another series of essays, Fenno grapples forthrightly with the tough choices researchers must consider when watching and learning from politicians.

In this essay, I respond to Fenno’s thought-provoking methodological treatise in Home Style and the ensuing collection of musings he published as Watching Politicians: Essays on Participant Observation. I do so for three reasons: First, I wish to reinforce Fenno’s call to action. As the study of political science has matured, it has moved away from engaging with politicians in the field across the various sub-fields, favoring statistical analyses. “Everyone cites Fenno, but no one does Fenno,” I recently opined, echoing another scholar commenting on Fenno’s work (Fenno 2013, p. 2; Parker 2015, p. 246). Unfortunately, that sentiment is supported by data (Grimmer 2013, pp. 13–19; Curry 2017). Although quantitative and formal analyses have led to important insights into the study of political behavior and institutions, politics is as important to our discipline as science. And in politics, the motives and concerns of people are important to witness, not just because they add complexity and richness to our stories, but because they aid in theory generation.1 Fenno’s study was exploratory, but is full of key theoretical insights relevant to explaining how members of Congress understand their constituencies and the ensuing political choices they make.

Second, to “do” participant observation requires understanding the choices the methodology imposes. This necessitates that those who practice this method of discovery document and share their experiences (Lin 2000). The more the prospective participant observer can understand the size of the choice set she faces and the potential consequences at each decision point in advance, the better her odds of avoiding unanticipated consequences with both immediate and long-term research ramifications. I hope that adding my cumulative experiences to this ongoing methodological conversation will assist in minimizing both unexpected and undesirable consequences for those who follow into the field. Fenno is open about his own choices, and the difficult decisions he faced as a participant observer. Encouraging scholars to engage in participant observation is only half the battle. The other half is to encourage interested scholars to think about those same choices and methodological considerations, while acknowledging that context precludes a one-size fits all approach. Fenno’s choices may not be your choices – and that might be just fine depending upon your circumstances. Fenno would wholeheartedly agree.

Finally, Congress and American politics have changed considerably from when Fenno embarked on his research in Home Style. At the end of his introduction, Fenno writes that “this book is about the early to mid-1970s only. These years were characterized by the steady decline of strong national party attachments and strong local party organizations. … Had these conditions been different, House members might have behaved differently in their constituencies” (xv). Developments since Fenno put down his pen include political parties polarizing to an almost unprecedented degree, partisan attachments strengthening among voters, and technology emerging to change fundamentally how politicians engage with constituents. In light of this evolution of political culture in Washington and at home, it is worth considering the consequences for the participant-observation research approach. Many have asked me if it is still possible to do such work in the current political environment, and if so, what are the challenges facing political scientists going into the field? This essay provides some answers.

I proceed as follows: First, I briefly discuss my own foray into the world of participant observation, which occurred during the 2012 Senate race in Montana. Second, I consider two important methodological considerations raised by Fenno: access and participation as an observer. Third, I relate these two issues to a final consideration: the development of social media and the consequences of this for the participant observation enterprise. Finally, I show the perils of social science divorced from context, as demonstrated by the recent Stanford-Dartmouth mailer scandal. I conclude with not just a plea for us to pick up where Fenno has left off, but by suggesting that more thinking like a participant observer would benefit the discipline as whole by reminding us of our ethical obligations as researchers to each other, and to the political community that we study…(More)”.

Making a Smart City a Fairer City: Chicago’s Technologists Address Issues of Privacy, Ethics, and Equity, 2011-2018


Case study by Gabriel Kuris and Steven S. Strauss at Innovations for Successful Societies: “In 2011, voters in Chicago elected Rahm Emanuel, a 51-year-old former Chicago congressman, as their new mayor. Emanuel inherited a city on the upswing after years of decline but still marked by high rates of crime and poverty, racial segregation, and public distrust in government. The Emanuel administration hoped to harness the city’s trove of digital data to improve Chicagoans’ health, safety, and quality of life. During the next several years, Chief Data Officer Brett Goldstein and his successor Tom Schenk led innovative uses of city data, ranging from crisis management to the statistical targeting of restaurant inspections and pest extermination. As their teams took on more-sophisticated projects that predicted lead-poisoning risks and Escherichia coli outbreaks and created a citywide network of ambient sensors, the two faced new concerns about normative issues like privacy, ethics, and equity. By 2018, Chicago had won acclaim as a smarter city, but was it a fairer city? This case study discusses some of the approaches the city developed to address those challenges and manage the societal implications of cutting-edge technologies….(More)”.

The rise of policy innovation labs: A catalog of policy innovation labs across Canada


Report by the Centre for Policy Innovation and Public Engagement (CPIPE): “In recent years, governments all over the world have been embracing new and innovative ways to develop public policies and design public services, from crowdsourcing to human-centred design thinking. This trend in government innovation has led to the rise of the Policy Innovation Lab (PIL): individual units, both inside and outside of government, that apply the traditional principles of scientific laboratories – experimentation, testing, and measurement – to social problems.

PILs are an increasingly important development in public policy making, with a variety of methods and approaches to building relationships between governments, organizations, and citizens, and generating ideas and designing policy. Yet, these labs are under-researched: many are established without a full understanding of their role and value to the policy community. We aim to address this knowledge gap, and create opportunities where policy innovators can make connections with their peers and learn about the current practices and applications of policy innovation from one another.

This report identifies the innovation labs in Canada, profiling their methodologies, projects, and partners, mapping the policy innovation landscape across the country. Each one-page summary provides a profile for each lab, and highlights the existing innovation practices and networks in the public, academic, non-profit, and private sectors, and identifies methodological and ideological trends across the different labs and networks.

This report is the first of its kind in North America. In this highly dynamic space, new labs are emerging and disappearing all the time. The purpose of this report is to put a spotlight on policy innovations and their successes, and to build and strengthen connections between researchers, policymakers, and policy innovators. Through a strengthened and sustained community of practice, we hope to see governments continue to embrace new approaches for effective policymaking…(More)”.

With real-time decisions, Citi Bike breaks the cycle of empty stations


Melanie Lefkowitz at Cornell Chronicle: “Cornell research has improved bike sharing in New York and other cities, providing tools to ensure bikes are available when and where they’re needed through a crowdsourcing system that uses real-time information to make decisions.

Citi Bike redistributes its bicycles around New York City using a program called Bike Angels, based on research by David Shmoys, the Laibe/Acheson Professor of Business Management and Leadership Studies in the School of Operations Research and Information Engineering.

Through Bike Angels, which Shmoys helped Citi Bike develop three years ago, cyclists earn points adding up to free rides and other prizes by using or returning bikes at certain high-need stations. Originally, Bike Angels awarded points for the same pattern of stations every morning, and a different fixed pattern each afternoon rush; now the program uses an algorithm that continually updates the pattern of stations for which users earn points.

“The ability to make decisions that are sensitive to exactly what are today’s conditions enables us to be much more effective in assigning those points,” said Shmoys, who is also associate director of Cornell’s Institute for Computational Sustainability.

With co-authors Hangil Chung ’18 and Daniel Freund, Ph.D. ’18, Shmoys wrote “Bike Angels: An Analysis of Citi Bike’s Incentive Program,” a detailed report showing the effectiveness of this approach. …(More)”.