DATA – Page 428 – The Living Library

Cities show how to make open data usable

Curated on July 12, 2015August 3, 2018 by Stefaan Verhulst

Bianca Spinosa at GCN: “Government agencies have no shortage of shareable data. Data.gov, the open-data clearinghouse that launched in May 2009, had more than 147,331 datasets as of mid-July, and state and local governments are joining federal agencies in releasing ever-broader arrays of information.

The challenge, however, remains making all that data usable. Obama administration officials like to talk about how the government’s weather data supports forecasting and analysis that support businesses and help Americans every day. But relatively few datasets do more than just sit there, and fewer still are truly accessible for the average person.

At the federal level, that’s often because agency missions do not directly affect citizens the way that local governments do. Nevertheless, every agency has customers and communities of interest, and there are lessons feds can learn from how cities are sharing their data with the public.

One such model is Citygram. The app links to a city’s open-data platform and sends subscribers a weekly text or email message about selected activities in their neighborhoods. Charlotte officials worked closely with Code for America fellows to develop the software, and the app launched in December 2014 in that city and in Lexington, Ky.

Three other cities – New York, Seattle, and San Francisco – have since joined, and Orlando, Fla.; Honolulu; the Research Triangle area of North Carolina; and Montgomery County, Md., are considering doing so.

Citygram “takes open data and transforms it, curates it and translates it into human speech,” said Twyla McDermott, Charlotte’s corporate IT program manager. “People want to know what’s happening around them.”

Demonstrating real-world utility

People in the participating cities can go to Citygram.org, select their city and choose topics of interest (such as pending rezonings or new business locations). Then they enter their address and a radius to consider “nearby” and finally select either text or email for their weekly notifications.

Any city government can use the technology, which is open source and freely available on GitHub. San Francisco put its own unique spin on the app by allowing subscribers to sign up for notifications on tree plantings. With Citygram NYC, New Yorkers can find information on vehicle collisions within a radius of up to 4 miles….(More)”

The data or the hunch

Curated on July 12, 2015December 11, 2018 by Stefaan Verhulst

Ian Leslie at Intelligent Life: “THE GIFT FOR talent-spotting is mysterious, highly prized and celebrated. We love to hear stories about the baseball coach who can spot the raw ability of an erratic young pitcher, the boss who sees potential in the guy in the post room, the director who picks a soloist out of the chorus line. Talent shows are a staple of the TV schedules. We like to believe that certain people—sometimes ourselves—can just sense when a person has something special. But there is another method of spotting talent which doesn’t rely on hunches. In place of intuition, it offers data and analysis. Rather than relying on the gut, it invites us to use our heads. It tends not to make for such romantic stories, but it is effective—which is why, despite our affection, the hunch is everywhere in retreat.

Strike one against the hunch was the publication of “Moneyball” by Michael Lewis (2003), which has attained the status of a management manual for many in sport and beyond. Lewis reported on a cash-strapped major-league baseball team, the Oakland A’s, who enjoyed unlikely success against bigger and better-funded competitors. Their secret sauce was data. Their general manager, Billy Beane, had realised that when it came to evaluating players, the gut instincts of experienced baseball scouts were unreliable, and he employed statisticians to identify talent overlooked by the big clubs…..

These days, when a football club is interested in a player, it considers the average distance he runs in a game, the number of passes and tackles or blocks he makes, his shots on goal, the ratio of goals to shots, and many other details nobody thought to measure a generation ago. Sport is far from the only industry in which talent-spotting is becoming a matter of measurement. Prithwijit Mukerji, a postgraduate at the University of Westminster in London, recently published a paper on the way the music industry is being transformed by “the Moneyball approach”. By harvesting data from Facebook and Twitter and music services like Spotify and Shazam, executives can track what we are listening to in far more detail than ever before, and use it as a guide to what we will listen to next….

This is the day of the analyst. In education, academics are working their way towards a reliable method of evaluating teachers, by running data on test scores of pupils, controlled for factors such as prior achievement and raw ability. The methodology is imperfect, but research suggests that it’s not as bad as just watching someone teach. A 2011 study led by Michael Strong at the University of California identified a group of teachers who had raised student achievement and a group who had not. They showed videos of the teachers’ lessons to observers and asked them to guess which were in which group. The judges tended to agree on who was effective and ineffective, but, 60% of the time, they were wrong. They would have been better off flipping a coin. This applies even to experts: the Gates Foundation funded a vast study of lesson observations, and found that the judgments of trained inspectors were highly inconsistent.

THE LAST STRONGHOLD of the hunch is the interview. Most employers and some universities use interviews when deciding whom to hire or admit. In a conventional, unstructured interview, the candidate spends half an hour or so in a conversation directed at the whim of the interviewer. If you’re the one deciding, this is a reassuring practice: you feel as if you get a richer impression of the person than from the bare facts on their résumé, and that this enables you to make a better decision. The first theory may be true; the second is not.

Decades of scientific evidence suggest that the interview is close to useless as a tool for predicting how someone will do a job. Study after study has found that organisations make better decisions when they go by objective data, like the candidate’s qualifications, track record and performance in tests. “The assumption is, ‘if I meet them, I’ll know’,” says Jason Dana, of Yale School of Management, one of many scholars who have looked into the interview’s effectiveness. “People are wildly over-confident in their ability to do this, from a short meeting.” When employers adopt a holistic approach, combining the data with hunches formed in interviews, they make worse decisions than they do going on facts alone….” (More)

Using Twitter as a data source: An overview of current social media research tools

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

Wasim Ahmed at the LSE Impact Blog: “I have a social media research blog where I find and write about tools that can be used to capture and analyse data from social media platforms. My PhD looks at Twitter data for health, such as the Ebola outbreak in West Africa. I am increasingly asked why I am looking at Twitter, and what tools and methods there are of capturing and analysing data from other platforms such as Facebook, or even less traditional platforms such as Amazon book reviews. Brainstorming a couple of responses to this question by talking to members of the New Social Media New Social Science network, there are at least six reasons:

Twitter is a popular platform in terms of the media attention it receives and it therefore attracts more research due to its cultural status
Twitter makes it easier to find and follow conversations (i.e., by both its search feature and by tweets appearing in Google search results)
Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data
Twitter data is easy to retrieve as major incidents, news stories and events on Twitter are tend to be centred around a hashtag
The Twitter API is more open and accessible compared to other social media platforms, which makes Twitter more favourable to developers creating tools to access data. This consequently increases the availability of tools to researchers.
Many researchers themselves are using Twitter and because of their favourable personal experiences, they feel more comfortable with researching a familiar platform.

It is probable that a combination of response 1 to 6 have led to more research on Twitter. However, this raises another distinct but closely related question: when research is focused so heavily on Twitter, what (if any) are the implications of this on our methods?

As for the methods that are currently used in analysing Twitter data i.e., sentiment analysis, time series analysis (examining peaks in tweets), network analysis etc., can these be applied to other platforms or are different tools, methods and techniques required? In addition to qualitative methods such as content analysis, I have used the following four methods in analysing Twitter data for the purposes of my PhD, below I consider whether these would work for other social media platforms:

Sentiment analysis works well with Twitter data, as tweets are consistent in length (i.e., <= 140) would sentiment analysis work well with, for example Facebook data where posts may be longer?
Time series analysis is normally used when examining tweets overtime to see when a peak of tweets may occur, would examining time stamps in Facebook posts, or Instagram posts, for example, produce the same results? Or is this only a viable method because of the real-time nature of Twitter data?
Network analysis is used to visualize the connections between people and to better understand the structure of the conversation. Would this work as well on other platforms whereby users may not be connected to each other i.e., public Facebook pages?
Machine learning methods may work well with Twitter data due to the length of tweets (i.e., <= 140) but would these work for longer posts and for platforms that are not text based i.e., Instagram?

It may well be that at least some of these methods can be applied to other platforms, however they may not be the best methods, and may require the formulation of new methods, techniques, and tools.

So, what are some of the tools available to social scientists for social media data? In the table below I provide an overview of some the tools I have been using (which require no programming knowledge and can be used by social scientists):…(More)”

Transforming City Governments for Successful Smart Cities

Curated on July 11, 2015December 11, 2018 by Stefaan Verhulst

New book edited by Rodríguez-Bolívar, Manuel Pedro: “There has been much attention paid to the idea of Smart Cities as researchers have sought to define and characterize the main aspects of the concept, including the role of creative industries in urban growth, the importance of social capital in urban development, and the role of urban sustainability. This book develops a critical view of the Smart City concept, the incentives and role of governments in promoting the development of Smart Cities and the analysis of experiences of e-government projects addressed to enhance Smart Cities. This book further analyzes the perceptions of stakeholders, such as public managers or politicians, regarding the incentives and role of governments in Smart Cities and the critical analysis of e-government projects to promote Smart Cities’ development, making the book valuable to academics, researchers, policy-makers, public managers, international organizations and technical experts in understanding the role of government to enhance Smart Cities’ projects….(More)”

The case for data ethics

Curated on July 9, 2015August 3, 2018 by Stefaan Verhulst

Steven Tiell at Accenture: “Personal data is the coin of the digital realm, which for business leaders creates a critical dilemma. Companies are being asked to gather more types of data faster than ever to maintain a competitive edge in the digital marketplace; at the same time, however, they are being asked to provide pervasive and granular control mechanisms over the use of that data throughout the data supply chain.

The stakes couldn’t be higher. If organizations, or the platforms they use to deliver services, fail to secure personal data, they expose themselves to tremendous risk—from eroding brand value and the hard-won trust of established vendors and customers to ceding market share, from violating laws to costing top executives their jobs.

To distinguish their businesses in this marketplace, leaders should be asking themselves two questions. What are the appropriate standards and practices our company needs to have in place to govern the handling of data? And how can our company make strong data controls a value proposition for our employees, customers and partners?

Defining effective compliance activities to support legal and regulatory obligations can be a starting point. However, mere compliance with existing regulations—which are, for the most part, focused on privacy—is insufficient. Respect for privacy is a byproduct of high ethical standards, but it is only part of the picture. Companies need to embrace data ethics, an expansive set of practices and behaviors grounded in a moral framework for the betterment of a community (however defined).

RAISING THE BAR

Why ethics? When communities of people—in this case, the business community at large—encounter new influences, the way they respond to and engage with those influences becomes the community’s shared ethics. Individuals who behave in accordance with these community norms are said to be moral, and those who are exemplary are able to gain the trust of their community.

Over time, as ethical standards within a community shift, the bar for trustworthiness is raised on the assumption that participants in civil society must, at a minimum, adhere to the rule of law. And thus, to maintain moral authority and a high degree of trust, actors in a community must constantly evolve to adopt the highest ethical standards.

Actors in the big data community, where security and privacy are at the core of relationships with stakeholders, must adhere to a high ethical standard to gain this trust. This requires them to go beyond privacy law and existing data control measures. It will also reward those who practice strong ethical behaviors and a high degree of transparency at every stage of the data supply chain. The most successful actors will become the platform-based trust authorities, and others will depend on these platforms for disclosure, sharing and analytics of big data assets.

Data ethics becomes a value proposition only once controls and capabilities are in place to granularly manage data assets at scale throughout the data supply chain. It is also beneficial when a community shares the same behavioral norms and taxonomy to describe the data itself, the ethical decision points along the data supply chain, and how those decisions lead to beneficial or harmful impacts….(More)”

Interactive app lets constituents help balance their city’s budget

Curated on July 8, 2015October 23, 2018 by Stefaan Verhulst

Springwise: “In this era of information, political spending and municipal budgets are still often shrouded in confusion and mystery. But a new web app called Balancing Act hopes to change that, by enabling US citizens to see the breakdown of their city’s budget via adjustable, comprehensive pie charts.

Created by Colorado-based consultants Engaged Public, Balancing Act not only shows citizens the current budget breakdown, it also enables them to experiment with hypothetical future budgets, adjusting spending and taxes to suit their own priorities. The project aims to engage and inform citizens about the money that their mayors and governments assign on their behalf and allow them to have more of a say in the future of their city. The resource has already been utilized by Pedro Segarra, Mayor of Hartford, Connecticut, who asked his citizens for their input on how best to balance the USD 49 million.

The system can be used to help governments understand the wants and needs of their constituents, as well as enable citizens to see the bigger picture when it comes to tough or unappealing policies. Eventually it can even be used to create the world’s first crowdsourced budget, giving the public the power to make their preferences heard in a clear, comprehensible way…(More)”