Open Smart Cities in Canada: Environmental Scan and Case Studies


Report by Tracey LauriaultRachel Bloom, Carly Livingstone and Jean-Noé Landry: “This executive summary consolidates findings from a smart city environmental scan (E-Scan) and five case studies of smart city initiatives in Canada. The E-Scan entailed compiling and reviewing documents and definitions produced by smart city vendors, think tanks, associations, consulting firms, standards organizations, conferences, civil society organizations, including critical academic literature, government reports, marketing material, specifications and requirements documents. This research was motivated by a desire to identify international shapers of smart cities and to better understand what differentiates a smart city from an Open Smart City….(More)”.

Literature review on collective intelligence: a crowd science perspective


Chao Yu in the International Journal of Crowd Science: “A group can be of more power and better wisdom than the sum of the individuals. Foreign scholars have noticed that for a long time and called it collective intelligence. It has emerged from the communication, collaboration, competition and brain storming, etc. Collective intelligence appears in many fields such as public decisions, voting activities, social networks and crowdsourcing.

Crowd science mainly focuses on the basic principles and laws of the intelligent activities of groups under the new interconnection model. It explores how to give full play to the intelligence agents and groups, dig their potential to solve the problems that are difficult for a single agent.

In this paper, we present a literature review on collective intelligence in a crowd science perspective. We focus on researchers’ related work, especially that under which circumstance can group show their wisdom, how to measure it, how to optimize it and its modern or future applications in the digital world. That is exactly what the crowd science pays close attention to….(More)”.

What if a nuke goes off in Washington, D.C.? Simulations of artificial societies help planners cope with the unthinkable


Mitchell Waldrop at Science: “…The point of such models is to avoid describing human affairs from the top down with fixed equations, as is traditionally done in such fields as economics and epidemiology. Instead, outcomes such as a financial crash or the spread of a disease emerge from the bottom up, through the interactions of many individuals, leading to a real-world richness and spontaneity that is otherwise hard to simulate.

That kind of detail is exactly what emergency managers need, says Christopher Barrett, a computer scientist who directs the Biocomplexity Institute at Virginia Polytechnic Institute and State University (Virginia Tech) in Blacksburg, which developed the NPS1 model for the government. The NPS1 model can warn managers, for example, that a power failure at point X might well lead to a surprise traffic jam at point Y. If they decide to deploy mobile cell towers in the early hours of the crisis to restore communications, NPS1 can tell them whether more civilians will take to the roads, or fewer. “Agent-based models are how you get all these pieces sorted out and look at the interactions,” Barrett says.

The downside is that models like NPS1 tend to be big—each of the model’s initial runs kept a 500-microprocessor computing cluster busy for a day and a half—forcing the agents to be relatively simple-minded. “There’s a fundamental trade-off between the complexity of individual agents and the size of the simulation,” says Jonathan Pfautz, who funds agent-based modeling of social behavior as a program manager at the Defense Advanced Research Projects Agency in Arlington, Virginia.

But computers keep getting bigger and more powerful, as do the data sets used to populate and calibrate the models. In fields as diverse as economics, transportation, public health, and urban planning, more and more decision-makers are taking agent-based models seriously. “They’re the most flexible and detailed models out there,” says Ira Longini, who models epidemics at the University of Florida in Gainesville, “which makes them by far the most effective in understanding and directing policy.”

he roots of agent-based modeling go back at least to the 1940s, when computer pioneers such as Alan Turing experimented with locally interacting bits of software to model complex behavior in physics and biology. But the current wave of development didn’t get underway until the mid-1990s….(More)”.

Modernizing Crime Statistics: New Systems for Measuring Crime


(Second) Report by the National Academies of Sciences, Engineering, and Medicine: “To derive statistics about crime – to estimate its levels and trends, assess its costs to and impacts on society, and inform law enforcement approaches to prevent it – a conceptual framework for defining and thinking about crime is virtually a prerequisite. Developing and maintaining such a framework is no easy task, because the mechanics of crime are ever evolving and shifting: tied to shifts and development in technology, society, and legislation.

Interest in understanding crime surged in the 1920s, which proved to be a pivotal decade for the collection of nationwide crime statistics. Now established as a permanent agency, the Census Bureau commissioned the drafting of a manual for preparing crime statistics—intended for use by the police, corrections departments, and courts alike. The new manual sought to solve a perennial problem by suggesting a standard taxonomy of crime. Shortly after the Census Bureau issued its manual, the International Association of Chiefs of Police in convention adopted a resolution to create a Committee on Uniform Crime Records —to begin the process of describing what a national system of data on crimes known to the police might look like.

Report 1 performed a comprehensive reassessment of what is meant by crime in U.S. crime statistics and recommends a new classification of crime to organize measurement efforts. This second report examines methodological and implementation issues and presents a conceptual blueprint for modernizing crime statistics….(More)”.

UK can lead the way on ethical AI, says Lords Committee


Lords Select Committee: “The UK is in a strong position to be a world leader in the development of artificial intelligence (AI). This position, coupled with the wider adoption of AI, could deliver a major boost to the economy for years to come. The best way to do this is to put ethics at the centre of AI’s development and use concludes a report by the House of Lords Select Committee on Artificial Intelligence, AI in the UK: ready, willing and able?, published today….

One of the recommendations of the report is for a cross-sector AI Code to be established, which can be adopted nationally, and internationally. The Committee’s suggested five principles for such a code are:

  1. Artificial intelligence should be developed for the common good and benefit of humanity.
  2. Artificial intelligence should operate on principles of intelligibility and fairness.
  3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.
  4. All citizens should have the right to be educated to enable them to flourish mentally, emotionally and economically alongside artificial intelligence.
  5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

Other conclusions from the report include:

  • Many jobs will be enhanced by AI, many will disappear and many new, as yet unknown jobs, will be created. Significant Government investment in skills and training will be necessary to mitigate the negative effects of AI. Retraining will become a lifelong necessity.
  • Individuals need to be able to have greater personal control over their data, and the way in which it is used. The ways in which data is gathered and accessed needs to change, so that everyone can have fair and reasonable access to data, while citizens and consumers can protect their privacy and personal agency. This means using established concepts, such as open data, ethics advisory boards and data protection legislation, and developing new frameworks and mechanisms, such as data portability and data trusts.
  • The monopolisation of data by big technology companies must be avoided, and greater competition is required. The Government, with the Competition and Markets Authority, must review the use of data by large technology companies operating in the UK.
  • The prejudices of the past must not be unwittingly built into automated systems. The Government should incentivise the development of new approaches to the auditing of datasets used in AI, and also to encourage greater diversity in the training and recruitment of AI specialists.
  • Transparency in AI is needed. The industry, through the AI Council, should establish a voluntary mechanism to inform consumers when AI is being used to make significant or sensitive decisions.
  • At earlier stages of education, children need to be adequately prepared for working with, and using, AI. The ethical design and use of AI should become an integral part of the curriculum.
  • The Government should be bold and use targeted procurement to provide a boost to AI development and deployment. It could encourage the development of solutions to public policy challenges through speculative investment. There have been impressive advances in AI for healthcare, which the NHS should capitalise on.
  • It is not currently clear whether existing liability law will be sufficient when AI systems malfunction or cause harm to users, and clarity in this area is needed. The Committee recommend that the Law Commission investigate this issue.
  • The Government needs to draw up a national policy framework, in lockstep with the Industrial Strategy, to ensure the coordination and successful delivery of AI policy in the UK….(More)”.

From Texts to Tweets to Satellites: The Power of Big Data to Fill Gender Data Gaps


 at UN Foundation Blog: “Twitter posts, credit card purchases, phone calls, and satellites are all part of our day-to-day digital landscape.

Detailed data, known broadly as “big data” because of the massive amounts of passively collected and high-frequency information that such interactions generate, are produced every time we use one of these technologies. These digital traces have great potential and have already developed a track record for application in global development and humanitarian response.

Data2X has focused particularly on what big data can tell us about the lives of women and girls in resource-poor settings. Our research, released today in a new report, Big Data and the Well-Being of Women and Girls, demonstrates how four big data sources can be harnessed to fill gender data gaps and inform policy aimed at mitigating global gender inequality. Big data can complement traditional surveys and other data sources, offering a glimpse into dimensions of girls’ and women’s lives that have otherwise been overlooked and providing a level of precision and timeliness that policymakers need to make actionable decisions.

Here are three findings from our report that underscore the power and potential offered by big data to fill gender data gaps:

  1. Social media data can improve understanding of the mental health of girls and women.

Mental health conditions, from anxiety to depression, are thought to be significant contributors to the global burden of disease, particularly for young women, though precise data on mental health is sparse in most countries. However, research by Georgia Tech University, commissioned by Data2X, finds that social media provides an accurate barometer of mental health status…..

  1. Cell phone and credit card records can illustrate women’s economic and social patterns – and track impacts of shocks in the economy.

Our spending priorities and social habits often indicate economic status, and these activities can also expose economic disparities between women and men.

By compiling cell phone and credit card records, our research partners at MIT traced patterns of women’s expenditures, spending priorities, and physical mobility. The research found that women have less mobility diversity than men, live further away from city centers, and report less total expenditure per capita…..

  1. Satellite imagery can map rivers and roads, but it can also measure gender inequality.

Satellite imagery has the power to capture high-resolution, real-time data on everything from natural landscape features, like vegetation and river flows, to human infrastructure, like roads and schools. Research by our partners at the Flowminder Foundation finds that it is also able to measure gender inequality….(More)”.

Behavior Change for Good Initiative


“At the Behavior Change for Good Initiativewe know that solving the mystery of enduring behavior change offers an enormous opportunity to improve lives. We unite an interdisciplinary team of scientists with leading practitioners in education, healthcare, and consumer financial services, all of whom seek to address the question: How can we make behavior change stick?…

We are developing an interactive digital platform to improve daily decisions about health, education, and savings. For the first time, a world-class team of scientific experts will be able to continually test and improve a behavior change program by seamlessly incorporating the latest insights from their research into massive random-assignment experiments. Their interactive digital platform seeks to improve daily health, education, and savings decisions of millions…(More)”.

Use of data & technology for promoting waste sector accountability in Nepal


Saroj Bista at YoungInnovations: “All the Nepalese people are saddened to see waste abandoned in the Capital, Kathmandu. Among them, many are concerned to find solutions to such a problem, including Kathmandu City. A 2015 report stated that Kathmandu Metropolitan City (KMC) alone receives 525 tonnes of waste in a day while it manages to collect 516 tonnes out if it, meaning that 8 tonnes of waste are left/abandoned….

Despite many stakeholders including the government sector, non-governmental organizations, private sectors have been working to address the problem associated with solid waste mapping in urban sector, the problem continued to exist.

YoungInnovations and Clean Up Nepal came together to discuss if we could tackle this problemWe discussed if keeping track of everybody’s efforts as well as noticing every piece of waste in the city raises accountability of stakeholders adds a value. YoungInnovations has over a decade of experience in developing data and evidence-based tech solutions to problem. Clean Up Nepal is a civil society organization working to provide an enabling environment to improve solid waste management and water, sanitation and hygiene in Nepal by working closely with local communities and relevant stakeholders. In this, both the organizations agreed to work mixing the expertise of each other to offer the government with an technology that avails stakeholders with proper data related to solid waste and its management.

Also, the preliminary idea was tested with some ongoing initiatives of such kind (Waste AtlasLetsdoitworld etc) while consultations were held with some of the organizations like The GovLabICIMOD learn from their expertise on open data as well as environmental aspects. A remarkable example of smart waste management being carried out in Ulaanbaatar, Capital of Mongolia did motivate us to test the idea in Nepal….

Nepal Waste Map Web App

Nepal Waste Map web is a composite of several features primarily focused at the following:

  1. Display of key stats and information about solid waste
  2. Admin panel to interact with the data for taking possible actions (update, edit and delete)…

Nepal Waste Map Mobile

A Mobile App primarily reflects Nepal Waste Map in the mobile phones. Most of the features resemble with the Nepal Waste Map Web App.

However, some functionalities in the app are key in terms of data aspects:

Crowdsourcing Functionality

Any public (users) who use the app can report issues related to illegal waste dumping and waste esp. Plastic burning. Example: if I saw somebody burning plastic wastes, I can use the app for reporting such an incident along with the photo as evidence as well as coordinates. The admin of the web app can view the report in a real time and take action (not limited to defined as acknowledge and marking resolved)…(More)”.

Data rights are civic rights: a participatory framework for GDPR in the US?


Elena Souris and Hollie Russon Gilman at Vox: “…While online rights are coming into question, it’s worth considering how those will overlap with offline rights and civic engagement.

The two may initially seem completely separate, but democracy itself depends on information and communication, and a balance of privacy (secret ballot) and transparency. As communication moves almost entirely to networked online technology platforms, the governance questions surrounding data and privacy have far-reaching civic and political implications for how people interact with all aspects of their lives, from commerce and government services to their friends, families, and communities. That is why we need a conversation about data protections, empowering users with their own information, and transparency — ultimately, data rights are now civic rights…

What could a golden mean in the US look like? Is it possible to take principles of the GDPR and apply a more community based, citizen-centric approach across states and localities in the United States? Could a US version of the GDPR be designed in a way that included public participation? Perhaps there could be an ongoing participatory role? Most of all, the questions underpinning data regulation need to serve as an impetus for an honest conversation about equity across digital access, digital literacy, and now digital privacy.

Across the country, we’re already seeing successful experiments with a more citizen-inclusive democracy, with localities and cities rising as engines of American re-innovationand laboratories of participatory democracy. Thanks to our federalist system, states are already paving the way for greater electoral reform, from public financing of campaigns to experiments with structures such as ranked-choice voting.

In these local federalist experiments, civic participation is slowly becoming a crucial tool. Innovations from participatory budgeting to interactive policy co-production sessions are giving people in communities a direct say in public policies. For example, the Rural Climate Dialogues in Minnesota empower rural residents to impact policy on long-term climate mitigation. Bowling Green, Kentucky, recently used the online deliberation platform Polisto identify common policy areas for consensus building. Scholars have been writing about various potential participatory models for our digital lives as well, including civic trusts.

Can we take these principles and begin a serious conversation for how to translate the best privacy practices, tools, and methods to ensure that people’s valuable online and offline resources — including their trust, attention span, and vital information — are also protected and honored? Since the people are a primary stakeholder in the conversation about civic data and data privacy, they should have a seat at the table.

Including citizens and residents in these conversations could have a big policy impact. First, working toward a participatory governance framework for civic data would enable people to understand the value of their data in the open market. Second, it would provide greater transparency to the value of networks — an individual’s social graph, a valuable asset, which, until now, people are generating in aggregate without anything in return. Third, it could amplify concerns of more vulnerable data users, including elderly or tech-illiterate citizens — and even refugees and international migrants, as Andrew Young and Stefaan Verhulst recently argued in the Stanford Social Innovation Review.

There are already templates and road maps for responsible data, but talking to those users themselves with a participatory governance approach could make them even more effective. Finally, citizens can help answer tough questions about what we value and when and how we need to make ethical choices with data.

Because data-collecting organizations will have to comply abroad soon, the GDPR is a good opportunity for the American social sector to consider data rights as civic rights and incorporate a participatory process to meet this challenge. Instead of simply assuming regulatory agencies will pave the way, a more participatory data framework could foster an ongoing process of civic empowerment and make the outcome more effective. It’s too soon to know the precise forms or mechanisms new data regulation should take. Instead of a rigid, predetermined format, the process needs to be community-driven by design — ensuring traditionally marginalized communities are front and center in this conversation, not only the elites who already hold the microphone.

It won’t be easy. Building a participatory governance structure for civic data will require empathy, compromise, and potentially challenging the preconceived relationship between people, institutions, and their information. The interplay between our online and offline selves is a continuous process of learning error. But if we simply replicate the top-down structures of the past, we can’t evolve toward a truly empowered digital democratic future. Instead, let’s use the GDPR as an opening in the United States for advancing the principles of a more transparent and participatory democracy….(More)”.

Friends with Academic Benefits


The new or interesting story isn’t just that Valerie, Betsy, and Steve’s friends had different social and academic impacts, but that they had various types of friendship networks. My research points to the importance of network structure—that is, the relationships among their friends—for college students’ success. Different network structures result from students’ experiences—such as race- and class-based marginalization on this predominantly White campus—and shape students’ experiences by helping or hindering them academically and socially.

I used social network techniques to analyze the friendship networks of 67 MU students and found they clumped into three distinctive types—tight-knitters, compartmentalizers, and samplers. Tight-knitters have one densely woven friendship group in which nearly all their friends are friends with one another. Compartmentalizers’ friends form two to four clusters, where friends know each other within clusters but rarely across them. And samplers make a friend or two from a variety of places, but the friends remain unconnected to each other. As shown in the figures, tight-knitters’ networks resemble a ball of yarn, compartmentalizers’ a bow-tie, and samplers’ a daisy. In these network maps, the person I interviewed is at the center and every other dot represents a friend, with lines representing connections among friends (that is, whether the person I interviewed believed that the two people knew each other). During the interviews, participants defined what friendship meant to them and listed as many friends as they liked (ranging from three to 45).

The students’ friendship network types influenced how friends matter for their academic and social successes and failures. Like Valerie, most Black and Latina/o students were tight-knitters. Their dense friendship networks provided a sense of home as a minority on a predominantly White campus. Tight-knit networks could provide academic support and motivation (as they did for Valerie) or pull students down academically if their friends lacked academic skills and motivation. Most White students were compartmentalizers like Betsy, and they succeeded with moderate levels of social support from friends and with social support and academic support from different clusters. Samplers came from a range of class and race backgrounds. Like Steve, samplers typically succeeded academically without relying on their friends. Friends were fun people who neither help nor hurt them academically. Socially, however, samplers reported feeling lonely and lacking social support….(More)”.