Crowded Cities

Curated on Posted on by Stefaan Verhulst

Crowded Cities: “In the Netherlands every year more than 6 billion cigarette filters are tossed onto the street. It’s easy to toss, but it’s not easy to pick them up. Since each filter takes 12 years to degrade we realised it’s time to take action.

Through observation we concluded crows are the smartest around us to reach any spot in the city. What if crows can bring cigarette filters to one of our Crowbars to exchange the filter for food? This is how our adventure started.

The Crowbar

Cigarette filters, you find them in the park next to you in the grass, in dirty ditches and under your shoes. What if we could find a way to collect these butts from all corners of our city and precious parks? With crows, that have become perfectly adapted to city life, we can! By training crows to recognize and pick up cigarette filters we can solve this tenacious problem of city pollution. It is the Crowbar that does the training for us and gives out food as a reward….(More)”.

The UN is using ethereum’s technology to fund food for thousands of refugees

Curated on Posted on by Stefaan Verhulst

Joon Ian Wong at Quartz: “The United Nations agency in charge of food aid—often billed as the largest aid organization in the world—is betting that an ethereum-based blockchain technology could be the key to delivering aid efficiently to refugees while slashing the costs of doing so.

The agency, known as the World Food Programme (WFP), is the rare example of an organization that has delivered tangible results from its blockchain experiments—unlike the big banks that have experimented with the technology for years.

The WFP says it has transferred $1.4 million in food vouchers to 10,500 Syrian refugees in Jordan since May, and it plans to expand. “We need to bring the project from the current capacity to many, many, more,” says Houman Haddad, the WFP executive leading the project. “By that I mean 1 million transactions per day.”

Haddad, in Mexico to speak at the Ethereum Foundation’s annual developer conference, hopes to expand the UN project, called Building Blocks, from providing payment vouchers for one camp to providing vouchers for four camps, covering 100,000 people, by next January. He hopes to attract developers and partners to the UN project from his conference appearance, organized by the foundation, which acts as a steward for the technical development of the ethereum protocol….

The problem of internal bureaucratic warfare, of course, isn’t limited to the UN. Paul Currion, who co-founded Disberse, another blockchain-based aid delivery platform, lauds the speediness of the WFP effort. “It’s fantastic for proving this can work in the field,” he says. But “we’ve found that the hard work is integrating blockchain technology into existing organizational processes—we can’t just hand people a ticket and expect them to get on the high-speed blockchain train; we also need to drive with them to the station,” he says….(More)”.

 

Linux Foundation Debuts Community Data License Agreement

Curated on Posted on by Stefaan Verhulst

Press Release: “The Linux Foundation, the nonprofit advancing professional open source management for mass collaboration, today announced the Community Data License Agreement(CDLA) family of open data agreements. In an era of expansive and often underused data, the CDLA licenses are an effort to define a licensing framework to support collaborative communities built around curating and sharing “open” data.

Inspired by the collaborative software development models of open source software, the CDLA licenses are designed to enable individuals and organizations of all types to share data as easily as they currently share open source software code. Soundly drafted licensing models can help people form communities to assemble, curate and maintain vast amounts of data, measured in petabytes and exabytes, to bring new value to communities of all types, to build new business opportunities and to power new applications that promise to enhance safety and services.

The growth of big data analytics, machine learning and artificial intelligence (AI) technologies has allowed people to extract unprecedented levels of insight from data. Now the challenge is to assemble the critical mass of data for those tools to analyze. The CDLA licenses are designed to help governments, academic institutions, businesses and other organizations open up and share data, with the goal of creating communities that curate and share data openly.

For instance, if automakers, suppliers and civil infrastructure services can share data, they may be able to improve safety, decrease energy consumption and improve predictive maintenance. Self-driving cars are heavily dependent on AI systems for navigation, and need massive volumes of data to function properly. Once on the road, they can generate nearly a gigabyte of data every second. For the average car, that means two petabytes of sensor, audio, video and other data each year.

Similarly, climate modeling can integrate measurements captured by government agencies with simulation data from other organizations and then use machine learning systems to look for patterns in the information. It’s estimated that a single model can yield a petabyte of data, a volume that challenges standard computer algorithms, but is useful for machine learning systems. This knowledge may help improve agriculture or aid in studying extreme weather patterns.

And if government agencies share aggregated data on building permits, school enrollment figures, sewer and water usage, their citizens benefit from the ability of commercial entities to anticipate their future needs and respond with infrastructure and facilities that arrive in anticipation of citizens’ demands.

“An open data license is essential for the frictionless sharing of the data that powers both critical technologies and societal benefits,” said Jim Zemlin, Executive Director of The Linux Foundation. “The success of open source software provides a powerful example of what can be accomplished when people come together around a resource and advance it for the common good. The CDLA licenses are a key step in that direction and will encourage the continued growth of applications and infrastructure.”…(More)”.

How “Big Data” Went Bust

Curated on Posted on by Stefaan Verhulst
 at Slate:”…Five years later, data plays a vastly expanded role in our lives, yet the term big data has gone out of fashion—and acquired something of an unsavory reputation. It’s worth looking back at what, exactly, happened to the revolution we were promised, and where data, analytics, and algorithms are headed now….

The problem with “big data” is not that data is bad. It’s not even that big data is bad: Applied carefully, massive data sets can reveal important trends that would otherwise go undetected. It’s the fetishization of data, and its uncritical use, that tends to lead to disaster, as Julia Rose West recently wrote for Slate. And that’s what “big data,” as a catchphrase, came to represent.

By its nature, big data is hard to interpret. When you’re collecting billions of data points—clicks or cursor positions on a website; turns of a turnstile in a large public space; hourly wind speed observations from around the world; tweets—the provenance of any given data point is obscured. This in turn means that seemingly high-level trends might turn out to be artifacts of problems in the data or methodology at the most granular level possible. But perhaps the bigger problem is that the data you have are usually only a proxy for what you really want to know. Big data doesn’t solve that problem—it magnifies it….

Aside from swearing off data and reverting to anecdote and intuition, there are at least two viable ways to deal with the problems that arise from the imperfect relationship between a data set and the real-world outcome you’re trying to measure or predict.

One is, in short: moar data. This has long been Facebook’s approach. When it became apparent that users’ “likes” were a flawed proxy for what they actually wanted to see more of in their feeds, the company responded by adding more and more proxies to its model. It began measuring other things, like the amount of time they spent looking at a post in their feed, the amount of time they spent reading a story they had clicked on, and whether they hit “like” before or after they had read the piece. When Facebook’s engineers had gone as far as they could in weighting and optimizing those metrics, they found that users were still unsatisfied in important ways. So the company added yet more metrics to the sauce: It started running huge user-survey panels, added new reaction emojis by which users could convey more nuanced sentiments, and started using A.I. to detect clickbait-y language in posts by pages and publishers. The company knows none of these proxies are perfect. But by constantly adding more of them to the mix, it can theoretically edge ever closer to an algorithm that delivers to users the posts that they most want to see.

One downside of the moar data approach is that it’s hard and expensive. Another is that the more variables are added to your model, the more complex, opaque, and unintelligible its methodology becomes. This is part of the problem Pasquale articulated in The Black Box Society. Even the most sophisticated algorithm, drawing on the best data sets, can go awry—and when it does, diagnosing the problem can be nigh-impossible. There are also the perils of “overfitting” and false confidence: The more sophisticated your model becomes, the more perfectly it seems to match up with all your past observations, and the more faith you place in it, the greater the danger that it will eventually fail you in a dramatic way. (Think mortgage crisis, election prediction models, and Zynga.)

Another possible response to the problems that arise from biases in big data sets is what some have taken to calling “small data.” Small data refers to data sets that are simple enough to be analyzed and interpreted directly by humans, without recourse to supercomputers or Hadoop jobs. Like “slow food,” the term arose as a conscious reaction to the prevalence of its opposite….(More)”

 

Priceless? A new framework for estimating the cost of open government reforms

Curated on Posted on by Stefaan Verhulst

New paper by Praneetha Vissapragada and Naomi Joswiak: “The Open Government Costing initiative, seeded with funding from the World Bank, was undertaken to develop a practical and actionable approach to pinpointing the full economic costs of various open government programs. The methodology developed through this initiative represents an important step towards conducting more sophisticated cost-benefit analyses – and ultimately understanding the true value – of open government reforms intended to increase citizen engagement, promote transparency and accountability, and combat corruption, insights that have been sorely lacking in the open government community to date. The Open Government Costing Framework and Methods section (Section 2 of this report) outlines the critical components needed to conduct cost analysis of open government programs, with the ultimate objective of putting a price tag on key open government reform programs in various countries at a particular point in time. This framework introduces a costing process that employs six essential steps for conducting a cost study, including (1) defining the scope of the program, (2) identifying types of costs to assess, (3) developing a framework for costing, (4) identifying key components, (5) conducting data collection and (6) conducting data analysis. While the costing methods are built on related approaches used for analysis in other sectors such as health and nutrition, this framework and methodology was specifically adapted for open government programs and thus addresses the unique challenges associated with these types of initiatives. Using the methods outlined in this document, we conducted a cost analysis of two case studies: (1) ProZorro, an e-procurement program in Ukraine; and (2) Sierra Leone’s Open Data Program….(More)”

When Cartography Meets Disaster Relief

Curated on Posted on by Stefaan Verhulst
Mimi Kirk at CityLab: “Almost three weeks after Hurricane Maria hit Puerto Rico, the island is in a grim state. Fewer than 15 percent of residents have power, and much of the island has no clean drinking water. Delivery of food and other necessities, especially to remote areas, has been hampered by a variety of ills, including a lack of cellular service, washed-out roads, additional rainfall, and what analysts and Puerto Ricans say is a slow and insufficient response from the U.S. government.

Another issue slowing recovery? Maps—or lack of them. While pre-Maria maps of Puerto Rico were fairly complete, their level of detail was nowhere near that of other parts of the United States. Platforms such as Google Maps are more comprehensive on the mainland than on the island, explains Juan Saldarriaga, a research scholar at the Center for Spatial Research at Columbia University. This is because companies like Google often create maps for financial reasons, selling them to advertisers or as navigation devices, so areas that have less economic activity are given less attention.

This lack of detail impedes recovery efforts: Without basic information on the location of buildings, for instance, rescue workers don’t know how many people were living in an area before the hurricane struck—and thus how much aid is needed.

Crowdsourced mapping can help. Saldarriaga recently organized a “mapathon” at Columbia, in which volunteers examined satellite imagery of Puerto Rico and added missing buildings, roads, bridges, and other landmarks in the open-source platform OpenStreetMap. While some universities and other groups are hosting similar events, anyone with an internet connection and computer can participate.

Saldarriaga and his co-organizers collaborated with Humanitarian OpenStreetMap Team (HOT), a nonprofit that works to create crowdsourced maps for aid and development work. Volunteers like Saldarriaga largely drive HOT’s “crisis mapping” projects, the first of which occurred in 2010 after Haiti’s earthquake…(More)”.

A Better Way to Trace Scattered Refugees

Curated on Posted on by Stefaan Verhulst

Tina Rosenberg in The New York Times: “…No one knew where his family had gone. Then an African refugee in Ottawa told him about Refunite. He went on its website and opened an account. He gave his name, phone number and place of origin, and listed family members he was searching for.

Three-quarters of a century ago, while World War II still raged, the Allies created the International Tracing Service to help the millions who had fled their homes. Its central name index grew to 50 million cards, with information on 17.5 million individuals. The index still exists — and still gets queries — today.

Index cards have become digital databases, of course. And some agencies have brought tracing into the digital age in other ways. Unicef, for example, equips staff during humanitarian emergencies with a software called Primero, which helps them get children food, medical care and other help — and register information about unaccompanied children. A parent searching for a child can register as well. An algorithm makes the connection — “like a date-finder or matchmaker,” said Robert MacTavish, who leads the Primero project.

Most United Nations agencies rely for family tracing on the International Committee of the Red Cross, the global network of national Red Cross and Red Crescent societies. Florence Anselmo, who directs the I.C.R.C.’s Central Tracing Agency, said that the I.C.R.C. and United Nations agencies can’t look in one another’s databases. That’s necessary for privacy reasons, but it’s an obstacle to family tracing.

Another problem: Online databases allow the displaced to do their own searches. But the I.C.R.C. has these for only a few emergency situations. Anselmo said that most tracing is done by the staff of national Red Cross societies, who respond to requests from other countries. But there is no global database, so people looking for loved ones must guess which countries to search.

The organization is working on developing an algorithm for matching, but for now, the search engines are human. “When we talk about tracing, it’s not only about data matching,” Anselmo said. “There’s a whole part about accompanying families: the human aspect, professionals as well as volunteers who are able to look for people — even go house to house if needed.”

This is the mom-and-pop general store model of tracing: The customer makes a request at the counter, then a shopkeeper with knowledge of her goods and a kind smile goes to the back and brings it out, throwing in a lollipop. But the world has 65 million forcibly displaced people, a record number. Personalized help to choose from limited stock is appropriate in many cases. But it cannot possibly be enough.

Refunite seeks to become the eBay of family tracing….(More)”

Co-creating an Open Government Data Driven Public Service: The Case of Chicago’s Food Inspection Forecasting Model

Curated on Posted on by Stefaan Verhulst

Conference paper by Keegan Mcbride et al: “Large amounts of Open Government Data (OGD) have become available and co-created public services have started to emerge, but there is only limited empirical material available on co-created OGD-driven public services. The authors have built a conceptual model around an innovation process based on the ideas of co-production and agile development for co-created OGD-driven public service. An exploratory case study on Chicago’s use of OGD in a predictive analytics model that forecasts critical safety violations at food serving establishments was carried out to expose the intricate process of how co-creation occurs and what factors allow for it to take place. Six factors were identified as playing a key role in allowing the co-creation of an OGD-driven public service to take place: external funding, motivated stakeholders, innovative leaders, proper communication channels, an existing OGD portal, and agile development practices. The conceptual model was generally validated, but further propositions on co-created OGD-driven public services emerged. These propositions state that the availability of OGD and tools for data analytics have the potential to enable the co-creation of OGD-driven public services, governments releasing OGD are acting as a platform and from this platform the co-creation of new and innovative OGD-driven public services may take place, and that the idea of Government as a Platform (GaaP) does appear to be an idea that allows for the topics of co-creation and OGD to be merged together….(More)”.

From Katrina To Harvey: How Disaster Relief Is Evolving With Technology

Curated on Posted on by Stefaan Verhulst

Cale Guthrie Weissman at Fast Company: “Open data may sound like a nerdy thing, but this weekend has proven it’s also a lifesaver in more ways than one.

As Hurricane Harvey pelted the southern coast of Texas, a local open-data resource helped provide accurate and up-to-date information to the state’s residents. Inside Harris County’s intricate bayou system–intended to both collect water and effectively drain it–gauges were installed to sense when water is overflowing. The sensors transmit the data to a website, which has become a vital go-to for Houston residents….

This open access to flood gauges is just one of the many ways new tech-driven projects have helped improve responses to disasters over the years. “There’s no question that technology has played a much more significant role,” says Lemaitre, “since even Hurricane Sandy.”

While Sandy was noted in 2012 for its ability to connect people with Twitter hashtags and other relatively nascent social apps like Instagram, the last few years have brought a paradigm shift in terms of how emergency relief organizations integrate technology into their responses….

Social media isn’t just for the residents. Local and national agencies–including FEMA–rely on this information and are using it to help create faster and more effective disaster responses. Following the disaster with Hurricane Katrina, FEMA worked over the last decade to revamp its culture and methods for reacting to these sorts of situations. “You’re seeing the federal government adapt pretty quickly,” says Lemaitre.

There are a few examples of this. For instance, FEMA now has an app to push necessary information about disaster preparedness. The agency also employs people to cull the open web for information that would help make its efforts better and more effective. These “social listeners” look at all the available Facebook, Snapchat, and other social media posts in aggregate. Crews are brought on during disasters to gather intelligence, and then report about areas that need relief efforts–getting “the right information to the right people,” says Lemaitre.

There’s also been a change in how this information is used. Often, when disasters are predicted, people send supplies to the affected areas as a way to try and help out. Yet they don’t know exactly where they should send it, and local organizations sometimes become inundated. This creates a huge logistical nightmare for relief organizations that are sitting on thousands of blankets and tarps in one place when they should be actively dispersing them across hundreds of miles.

“Before, you would just have a deluge of things dropped on top of a disaster that weren’t particularly helpful at times,” says Lemaitre. Now people are using sites like Facebook to ask where they should direct the supplies. For example, after a bad flood in Louisiana last year, a woman announced she had food and other necessities on Facebook and was able to direct the supplies to an area in need. This, says Lemaitre, is “the most effective way.”

Put together, Lemaitre has seen agencies evolve with technology to help create better systems for quicker disaster relief. This has also created a culture of learning updates and reacting in real time. Meanwhile, more data is becoming open, which is helping both people and agencies alike. (The National Weather Service, which has long trumpeted its open data for all, has become a revered stalwart for such information, and has already proven indispensable in Houston.)

Most important, the pace of technology has caused organizations to change their own procedures. Twelve years ago, during Katrina, the protocol was to wait until an assessment before deploying any assistance. Now organizations like FEMA know that just doesn’t work. “You can’t afford to lose time,” says Lemaitre. “Deploy as much as you can and be fast about it–you can always scale back.”

It’s important to note that, even with rapid technological improvements, there’s no way to compare one disaster response to another–it’s simply not apples to apples. All the same, organizations are still learning about where they should be looking and how to react, connecting people to their local communities when they need them most….(More)”.

Inside the Lab That’s Quantifying Happiness

Curated on Posted on by Stefaan Verhulst

Rowan Jacobsen at Outside: “In Mississippi, people tweet about cake and cookies an awful lot; in Colorado, it’s noodles. In Mississippi, the most-tweeted activity is eating; in Colorado, it’s running, skiing, hiking, snowboarding, and biking, in that order. In other words, the two states fall on opposite ends of the behavior spectrum. If you were to assign a caloric value to every food mentioned in every tweet by the citizens of the United States and a calories-burned value to every activity, and then totaled them up, you would find that Colorado tweets the best caloric ratio in the country and Mississippi the worst.

Sure, you’d be forgiven for doubting people’s honesty on Twitter. On those rare occasions when I destroy an entire pint of Ben and Jerry’s, I most assuredly do not tweet about it. Likewise, I don’t reach for my phone every time I strap on a pair of skis.

And yet there’s this: Mississippi has the worst rate of diabetes and heart disease in the country and Colorado has the best. Mississippi has the second-highest percentage of obesity; Colorado has the lowest. Mississippi has the worst life expectancy in the country; Colorado is near the top. Perhaps we are being more honest on social media than we think. And perhaps social media has more to tell us about the state of the country than we realize.

That’s the proposition of Peter Dodds and Chris Danforth, who co-direct the University of Vermont’s Computational Story Lab, a warren of whiteboards and grad students in a handsome brick building near the shores of Lake Champlain. Dodds and Danforth are applied mathematicians, but they would make a pretty good comedy duo. When I stopped by the lab recently, both were in running clothes and cracking jokes. They have an abundance of curls between them and the wiry energy of chronic thinkers. They came to UVM in 2006 to start the Vermont Complex Systems Center, which crunches big numbers from big systems and looks for patterns. Out of that, they hatched the Computational Story Lab, which sifts through some of that public data to discern the stories we’re telling ourselves. “It took us a while to come up with the name,” Dodds told me as we shotgunned espresso and gazed into his MacBook. “We were going to be the Department of Recreational Truth.”

This year, they teamed up with their PhD student Andy Reagan to launch the Lexicocalorimeter, an online tool that uses tweets to compute the calories in and calories out for every state. It’s no mere party trick; the Story Labbers believe the Lexicocalorimeter has important advantages over slower, more traditional methods of gathering health data….(More)”.