Modernizing government’s approach to transportation and land use data: Challenges and opportunities

Curated on Posted on by Stefaan Verhulst

Adie Tomer and Ranjitha Shivaram at Brookings: “In the fields of transportation and land use planning, the public sector has long taken the leading role in the collection, analysis, and dissemination of data. Often, public data sets drawn from traveler diaries, surveys, and supply-side transportation maps were the only way to understand how people move around in the built environment – how they get to work, how they drop kids off at school, where they choose to work out or relax, and so on.

But, change is afoot: today, there are not only new data providers, but also new types of data. Cellphones, GPS trackers, and other navigation devices offer real-time demand-side data. For instance, mobile phone data can point to where distracted driving is a problem and help implement measures to deter such behavior. Insurance data and geo-located police data can guide traffic safety improvements, especially in accident-prone zones. Geotagged photo data can illustrate the use of popular public spaces by locals and tourists alike, enabling greater return on investment from public spaces. Data from exercise apps like Fitbit and Runkeeper can help identify recreational hot spots that attract people and those that don’t.

However, integrating all this data into how we actually plan and build communities—including the transportation systems that move all of us and our goods—will not be easy. There are several core challenges. Limited staff capacity and restricted budgets in public agencies can slow adoption. Governmental procurement policies are stuck in an analog era. Privacy concerns introduce risk and uncertainty. Private data could be simply unavailable to public consumers. And even if governments could acquire all of the new data and analytics that interest them, their planning and investment models must be updated to fully utilize these new resources.

Using a mix of primary research and expert interviews, this report catalogs emerging data sets related to transportation and land use, and assesses the ease by which they can be integrated into how public agencies manage the built environment. It finds that there is reason for the hype; we have the ability to know more about how humans move around today than at any time in history. But, despite all the obvious opportunities, not addressing core challenges will limit public agencies’ ability to put all that data to use for the collective good….(More)”

Open data on universities – New fuel for transformation

Curated on Posted on by Stefaan Verhulst

François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.

Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.

Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.

Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.

Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.

Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.

Benefits of open data

Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.

By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.

Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.

‘Open washing’

But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”

Formalised data citation practices would encourage more authors to make their data available for reuse

Curated on Posted on by Stefaan Verhulst

 Hyoungjoo Park and Dietmar Wolfram at the LSE Impact Blog: “Today’s researchers work in a heavily data-intensive and collaborative environment in order to further scientific discovery across and within fields. It is becoming routine for researchers (i.e. authors and data publishers) to submit their research data, such as datasets, biological samples in biomedical fields, and computer code, as supplementary information in order to comply with data sharing requirements of major funding agencies, high-profile journals, and data journals. This is part of open science, where data and any publication products are expected to be made available to anyone interested.

Given that researchers benefit from publicly shared data through data reuse in their own research, researchers who provide access to data should be acknowledged for their contributions, much in the same way that authors are recognised for their research publications through citation. Researchers who use shared data or other shared research products (e.g. open access software, tissue cultures) should also acknowledge the providers of these resources through formal citation. At present, data citation is not widely practised in most disciplines and as an object of study remains largely overlooked….

We found that data citations appear in the references section of an article less frequently than in the main text, making it difficult to identify the reward and credit for data authors (i.e. data sharers). Consistent data citation formats could not be found. Current data citation practices do not (yet) benefit data sharers. Also, data citation was sometimes located in the supplementary information, outside of the references. Data that had been reused was often not acknowledged in the reference lists, but was rather hidden in the representation of data (e.g. tables, figures, images, graphs, and other elements), which may be a consequence of the fact that data citation practices are not yet common in scholarly communications.

Ongoing challenges remain in identifying and documenting data citation. First, the practice of informal data citation presents a challenge for accurately documenting data citation. …

Second, data recitation by one or more co-authors of earlier studies (i.e. self-citation) is common, which reduces the broader impact of data sharing by limiting much of the reuse to the original authors..

Third, currently indexed data citations may not include rapidly advancing areas, such as in the hard sciences or computer engineering, because approximately 90% of indexed works were associated with journal articles…

Fourth, the number of authors associated with shared datasets raises questions of the ownership of and responsibility for a collective work, although some journals require one author to be responsible for the data used in the study…(More). (See also An examination of research data sharing and re-use: implications for data citation practice, published in Scientometrics)

Public servants to go on blind coffee dates for innovation

Curated on Posted on by Stefaan Verhulst

David Donaldson at The Mandarin: “Victorian public servants will have the opportunity to be randomly matched with others for coffee dates, as part of the government’s plan to foster links across silos and bolster innovation.

That is just one of many initiatives planned by Victoria in its new Public Sector Innovation Strategy, released on Tuesday.The plan acknowledges that plenty of innovative thinking is already happening, so the best way to drive further ideas is to connect people better and provide tools and case studies so they can learn from one another.

Six themes repeatedly came up in conversations around innovation in the public service, says the document:

  1. Leaders who enable and reward — too often new ideas are stifled by leaders who don’t support them;
  2. Employees who feel confident and supported;
  3. Learning well — pockets of innovation exist, and stronger efforts to learn and develop from them will help;
  4. Sharing with each other;
  5. Partnering with the community and other organisations;
  6. Delivering value — don’t innovate on random things. Focus on what makes a difference.

“This strategy helps to find, encourage and support change that adds value across the public sector. We need to unlock good intent and talent, share examples and experiences, and learn from each other,” says Chris Eccles, secretary of the Department of Premier and Cabinet.

“At our best, we all contribute our different skills and roles to generate more public value, shaped by the common purpose of creating a better society.”

To kick off progress, the government has outlined a series of actions it will undertake:

  • A reverse mentoring plan to help executives learn from more junior staff. Due September 2017.
  • Build on a current departmental trial that builds innovation into executive performance development plans. December 2017.
  • Establish a high-profile event to recognise and reward practical innovation across government. March 2018.
  • A practical innovation bank, to provide a common digital space for cross-government sharing of practical resources (case studies, contacts, templates, guides, lessons learned and so on). December 2017.
  • Ideas challenge toolkit to provide guidance on how to run an ideas challenge. December 2017.
  • Learning lab trial, which will provide an incubator environment for cross-government use on a project by project basis. March 2018.
  • VPS Academy, a new peer to peer learning project, will go through two more pilots to build the case for scaling up. July and December 2017…(More)”.

Open Data Blueprint

Curated on Posted on by Stefaan Verhulst

ODX Canada: “In Canada, the open data environment should be viewed as a supply chain. The movement of open data from producers to consumers involves many different organizations, people, activities, projects and initiatives, all of which work together to push out a final product. Naturally, if there is a break or hurdle in this supply chain, it doesn’t work efficiently. A fundamental hurdle highlighted by companies across the country was the inability to scale their business at the provincial, national and international levels.

This blueprint aims to address the challenges Canadian entrepreneurs are facing by encouraging municipalities to launch open data initiatives. By sharing best practices, we hope to encourage the accessibility of datasets within existing jurisdictions. The structured recommendations in this Open Data Blueprint are based on feedback and best practices seen in major cities across Canada collected through ODX’s primary research….(More)”

(Read more about the OD150 initiative here)

NIH-funded team uses smartphone data in global study of physical activity

Curated on Posted on by Stefaan Verhulst

National Institutes of Health: “Using a larger dataset than for any previous human movement study, National Institutes of Health-funded researchers at Stanford University in Palo Alto, California, have tracked physical activity by population for more than 100 countries. Their research follows on a recent estimate that more than 5 million people die each year from causes associated with inactivity.

The large-scale study of daily step data from anonymous smartphone users dials in on how countries, genders, and community types fare in terms of physical activity and what results may mean for intervention efforts around physical activity and obesity. The study was published July 10, 2017, in the advance online edition of Nature.

“Big data is not just about big numbers, but also the patterns that can explain important health trends,” said Grace Peng, Ph.D., director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) program in Computational Modeling, Simulation and Analysis.

“Data science and modeling can be immensely powerful tools. They can aid in harnessing and analyzing all the personalized data that we get from our phones and wearable devices.”

Almost three quarters of adults in developed countries and half of adults in developing economies carry a smartphone. The devices are equipped with tiny accelerometers, computer chip that maintains the orientation of the screen, and can also automatically record stepping motions. The users whose data contributed to this study subscribed to the Azumio Argus app, a free application for tracking physical activity and other health behaviors….

In addition to the step records, the researchers accessed age, gender, and height and weight status of users who registered the smartphone app. They used the same calculation that economists use for income inequality — called the Gini index — to calculate activity inequality by country.

“These results reveal how much of a population is activity-rich, and how much of a population is activity-poor,” Delp said. “In regions with high activity inequality there are many people who are activity poor, and activity inequality is a strong predictor of health outcomes.”…

The researchers investigated the idea that making improvements in a city’s walkability — creating an environment that is safe and enjoyable to walk — could reduce activity inequality and the activity gender gap.

“If you must cross major highways to get from point A to point B in a city, the walkability is low; people rely on cars,” Delp said. “In cities like New York and San Francisco, where you can get across town on foot safely, the city has high walkability.”

Data from 69 U.S. cities showed that higher walkability scores are associated with lower activity inequality. Higher walkability is associated with significantly more daily steps across all age, gender, and body-mass-index categories.  However, the researchers found that women recorded comparatively less activity than men in places that are less walkable.

The study exemplifies how smartphones can deliver new insights about key health behaviors, including what the authors categorize as the global pandemic of physical inactivity….(More)”.

Principles and Practices for a Federal Statistical Agency

Curated on Posted on by Stefaan Verhulst

National Academies of Sciences Report: “Publicly available statistics from government agencies that are credible, relevant, accurate, and timely are essential for policy makers, individuals, households, businesses, academic institutions, and other organizations to make informed decisions. Even more, the effective operation of a democratic system of government depends on the unhindered flow of statistical information to its citizens.

In the United States, federal statistical agencies in cabinet departments and independent agencies are the governmental units whose principal function is to compile, analyze, and disseminate information for such statistical purposes as describing population characteristics and trends, planning and monitoring programs, and conducting research and evaluation. The work of these agencies is coordinated by the U.S. Office of Management and Budget. Statistical agencies may acquire information not only from surveys or censuses of people and organizations, but also from such sources as government administrative records, private-sector datasets, and Internet sources that are judged of suitable quality and relevance for statistical use. They may conduct analyses, but they do not advocate policies or take partisan positions. Statistical purposes for which they provide information relate to descriptions of groups and exclude any interest in or identification of an individual person, institution, or economic unit.

Four principles are fundamental for a federal statistical agency: relevance to policy issues, credibility among data users, trust among data providers, and independence from political and other undue external influence.� Principles and Practices for a Federal Statistical Agency: Sixth Edition presents and comments on these principles as they’ve been impacted by changes in laws, regulations, and other aspects of the environment of federal statistical agencies over the past 4 years….(More)”.

Justice in Algorithmic Robes

Curated on Posted on by Stefaan Verhulst

Editorial by Joseph Savirimuthu of a Special Issue of the International Review of Law, Computers & Technology: “The role and impact of algorithms has attracted considerable interest in the media. Its impact is already being reflected in adjustments made in a number of sectors – entertainment, travel, transport, cities and financial services. From an innovation point of view, algorithms enable new knowledge to be created and identify solutions to problems. The emergence of smart sensing technologies, 3D printing, automated systems and robotics is seamlessly being interwoven into discourses such as ‘the collaborative economy’, ‘governance by platforms’ and ‘empowerment’. Innovations such as body worn cameras, fitness trackers, 3D printing, smart meters, robotics and Big Data hold out the promise of a new algorithmic future. However, the shift in focus from natural and scarce resources towards information also makes individuals the objects and the mediated construction of access and knowledge infrastructures now provide the conditions for harnessing value from data. The increasing role of algorithms in environments mediated by technology also coincide with growing inter-disciplinary scholarship voicing concerns about the vulnerability of the values we associate with fundamental freedoms and how these are being algorithmically reconfigured or dismantled in a systematic manner. The themed issue, Justice in Algorithmic Robes, is intended to initiate a dialogue on both the challenges and opportunities as digitalization ushers in a period of transformation that has no immediate parallels in terms of scale, speed and reach. The articles provide different perspectives to the transformation taking place in the digital environment. The contributors offer an inter-disciplinary view of how the digital economy is being invigorated and evaluate the regulatory responses – in particular, how these transformations interact with law. The different spheres covered in Justice in Algorithmic Robes – the relations between the State and individuals, autonomous technology, designing human–computer interactions, infrastructures of trust, accountability in the age of Big Data, and health and wearables – not only reveal the problem of defining spheres of economic, political and social activity, but also highlight how these contexts evolve into structures for dominance, power and control. Re-imagining the role of law does not mean that technology is the problem but the central idea from the contributions is that how we critically interpret and construct Justice in Algorithmic Robes is probably the first step we must take, always mindful of the fact that law may actually reinforce power structures….(Full Issue)”.

Children and the Data Cycle: Rights And Ethics in a Big Data World

Curated on Posted on by Stefaan Verhulst

Gabrielle Berman andKerry Albright at UNICEF: “In an era of increasing dependence on data science and big data, the voices of one set of major stakeholders – the world’s children and those who advocate on their behalf – have been largely absent. A recent paper estimates one in three global internet users is a child, yet there has been little rigorous debate or understanding of how to adapt traditional, offline ethical standards for research involving data collection from children, to a big data, online environment (Livingstone et al., 2015). This paper argues that due to the potential for severe, long-lasting and differential impacts on children, child rights need to be firmly integrated onto the agendas of global debates about ethics and data science. The authors outline their rationale for a greater focus on child rights and ethics in data science and suggest steps to move forward, focusing on the various actors within the data chain including data generators, collectors, analysts and end-users. It concludes by calling for a much stronger appreciation of the links between child rights, ethics and data science disciplines and for enhanced discourse between stakeholders in the data chain, and those responsible for upholding the rights of children, globally….(More)”.

Gender Biases in Cyberspace: A Two-Stage Model, the New Arena of Wikipedia and Other Websites

Curated on Posted on by Stefaan Verhulst

Paper by Shlomit Yanisky-Ravid and Amy Mittelman: “Increasingly, there has been a focus on creating democratic standards and norms in order to best facilitate open exchange of information and communication online―a goal that fits neatly within the feminist aim to democratize content creation and community. Collaborative websites, such as blogs, social networks, and, as focused on in this Article, Wikipedia, represent both a cyberspace community entirely outside the strictures of the traditional (intellectual) proprietary paradigm and one that professes to truly embody the philosophy of a completely open, free, and democratic resource for all. In theory, collaborative websites are the solution for which social activists, intellectual property opponents, and feminist theorists have been waiting. Unfortunately, we are now realizing that this utopian dream does not exist as anticipated: the Internet is neither neutral nor open to everyone. More importantly, these websites are not egalitarian; rather, they facilitate new ways to exclude and subordinate women. This Article innovatively argues that the virtual world excludes women in two stages: first, by controlling websites and filtering out women; and second, by exposing women who survived the first stage to a hostile environment. Wikipedia, as well as other cyber-space environments, demonstrates the execution of the model, which results in the exclusion of women from the virtual sphere with all the implications thereof….(More)”.