What Is Mobility Data? Where Is It Used?

Curated on Posted on by Stefaan Verhulst

Brief by Andrew J. Zahuranec, Stefaan Verhulst, Andrew Young, Aditi Ramesh, and Brennan Lake: “Mobility data is data about the geographic location of a device passively produced through normal activity. Throughout the pandemic, public health experts and public officials have used mobility data to understand patterns of COVID-19’s spread and the impact of disease control measures. However, privacy advocates and others have questioned the need for this data and raised concerns about the capacity of such data-driven tools to facilitate surveillance, improper data use, and other exploitative practices.

In April, The GovLab, Cuebiq, and the Open Data Institute released The Use of Mobility Data for Responding to the COVID-19 Pandemic, which relied on several case studies to look at the opportunities, risks, and challenges associated with mobility data. Today, we hope to supplement that report with a new resource: a brief on what mobility data is and the different types of data it can include. The piece is a one-pager to allow decision-makers to easily read it. It provides real-world examples from the report to illustrate how different data types can be used in a responsible way…..(More)”.

The Ethics and Laws of Medical Big Data

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Chapter by Hrefna Gunnarsdottir et al: “The COVID-19 pandemic has highlighted that leveraging medical big data can help to better predict and control outbreaks from the outset. However, there are still challenges to overcome in the 21st century to efficiently use medical big data, promote innovation and public health activities and, at the same time, adequately protect individuals’ privacy. The metaphor that property is a “bundle of sticks”, each representing a different right, applies equally to medical big data. Understanding medical big data in this way raises a number of questions, including: Who has the right to make money off its buying and selling, or is it inalienable? When does medical big data become sufficiently stripped of identifiers that the rights of an individual concerning the data disappear? How have different regimes such as the General Data Protection Regulation in Europe and the Health Insurance Portability and Accountability Act in the US answered these questions differently? In this chapter, we will discuss three topics: (1) privacy and data sharing, (2) informed consent, and (3) ownership. We will identify and examine ethical and legal challenges and make suggestions on how to address them. In our discussion of each of the topics, we will also give examples related to the use of medical big data during the COVID-19 pandemic, though the issues we raise extend far beyond it….(More)”.

How Big Data is Transforming the Way We Plan Our Cities

Curated on Posted on by Stefaan Verhulst

Paper by Rawad Choubassi and Lamia Abdelfattah: “The availability of ubiquitous location-based data in cities has had far-reaching implications on analytical powers in various disciplines. This article focuses on some of the accrued benefits to urban transport planners and the urban planning field at large. It contends that the gains of Big Data and real-time information has not only improved analytical strength, but has also created ripple effects in the systemic approaches of city planning, integrating ex-post studies within the design cycle and redefining the planning process as a microscopic, iterative and self-correcting process. Case studies from the field are used to further highlight these newfound abilities to process fine-grained analyses and propose more customized location-based solutions, offered by Big Data. A detailed description of the Torrance Living Lab experience maps out some of the potentials of using movement data from Big Data sources to design an alternative mobility plan for a low-density urban area. Finally, the paper reflects on Big Data’s limited capacity at present to replace traditional forecast modelling tools, despite demonstrated advantages over traditional methods in gaining insight from past and present travel trends….(More)”.

Towards an accountable Internet of Things: A call for ‘reviewability’

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Paper by Chris Norval, Jennifer Cobbe and Jatinder Singh: “As the IoT becomes increasingly ubiquitous, concerns are being raised about how IoT systems are being built and deployed. Connected devices will generate vast quantities of data, which drive algorithmic systems and result in real-world consequences. Things will go wrong, and when they do, how do we identify what happened, why they happened, and who is responsible? Given the complexity of such systems, where do we even begin?
This chapter outlines aspects of accountability as they relate to IoT, in the context of the increasingly interconnected and data-driven nature of such systems. Specifically, we argue the urgent need for mechanisms – legal, technical, and organisational – that facilitate the review of IoT systems. Such mechanisms work to support accountability, by enabling the relevant stakeholders to better understand, assess, interrogate and challenge the connected environments that increasingly pervade our world….(More)”

How a Google Street View image of your house predicts your risk of a car accident

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “Google Street View has become a surprisingly useful way to learn about the world without stepping into it. People use it to plan journeys, to explore holiday destinations, and to virtually stalk friends and enemies alike.

But researchers have found more insidious uses. In 2017 a team of researchers used the images to study the distribution of car types in the US and then used that data to determine the demographic makeup of the country. It turns out that the car you drive is a surprisingly reliable proxy for your income level, your education, your occupation, and even the way you vote in elections.

Street view of houses in Poland

Now a different group has gone even further. Łukasz Kidziński at Stanford University in California and Kinga Kita-Wojciechowska at the University of Warsaw in Poland have used Street View images of people’s houses to determine how likely they are to be involved in a car accident. That’s valuable information that an insurance company could use to set premiums.

The result raises important questions about the way personal information can leak from seemingly innocent data sets and whether organizations should be able to use it for commercial purposes.

Insurance data

The researchers’ method is straightforward. They began with a data set of 20,000 records of people who had taken out car insurance in Poland between 2013 and 2015. These were randomly selected from the database of an undisclosed insurance company.

Each record included the address of the policyholder and the number of damage claims he or she made during the 2013–’15 period. The insurer also shared its own prediction of future claims, calculated using its state-of-the-art risk model that takes into account the policyholder’s zip code and the driver’s age, sex, claim history, and so on.

The question that Kidziński and Kita-Wojciechowska investigated is whether they could make a more accurate prediction using a Google Street View image of the policyholder’s house….(More)”.

Geospatial Data Market Study

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Study by Frontier Economics: “Frontier Economics was commissioned by the Geospatial Commission to carry out a detailed economic study of the size, features and characteristics of the UK geospatial data market. The Geospatial Commission was established within the Cabinet Office in 2018, as an independent, expert committee responsible for setting the UK’s Geospatial Strategy and coordinating public sector geospatial activity. The Geospatial Commission’s aim is to unlock the significant economic, social and environmental opportunities offered by location data. The UK’s Geospatial Strategy (2020) sets out how the UK can unlock the full power of location data and take advantage of the significant economic, social and environmental opportunities offered by location data….

Like many other forms of data, the value of geospatial data is not limited to the data creator or data user. Value from using geospatial data can be subdivided into several different categories, based on who the value accrues to:

Direct use value: where value accrues to users of geospatial data. This could include government using geospatial data to better manage public assets like roadways.

Indirect use value: where value is also derived by indirect beneficiaries who interact with direct users. This could include users of the public assets who benefit from better public service provision.

Spillover use value: value that accrues to others who are not a direct data user or indirect beneficiary. This could, for example, include lower levels of emissions due to improvement management of the road network by government. The benefits of lower emissions are felt by all of society even those who do not use the road network.

As the value from geospatial data does not always accrue to the direct user of the data, there is a risk of underinvestment in geospatial technology and services. Our £6 billion estimate of turnover for a subset of geospatial firms in 2018 does not take account of these wider economic benefits that “spill over” across the UK economy, and generate additional value. As such, the value that geospatial data delivers is likely to be significantly higher than we have estimated and is therefore an area for potential future investment….(More)”.

A qualitative study of big data and the opioid epidemic: recommendations for data governance

Curated on Posted on by Stefaan Verhulst

Paper by Elizabeth A. Evans, Elizabeth Delorme, Karl Cyr & Daniel M. Goldstein: “The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms….

We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health.

Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches.

Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions….(More)”

Social license for the use of big data in the COVID-19 era

Curated on Posted on by Stefaan Verhulst

Commentary by James A. Shaw, Nayha Sethi & Christine K. Cassel: “… Social license refers to the informal permissions granted to institutions such as governments or corporations by members of the public to carry out a particular set of activities. Much of the literature on the topic of social license has arisen in the field of natural resources management, emphasizing issues that include but go beyond environmental stewardship4. In their seminal work on social license in the pulp and paper industry, Gunningham et al. defined social license as the “demands and expectations” placed on organizations by members of civil society which “may be tougher than those imposed by regulation”; these expectations thereby demand actions that go beyond existing legal rules to demonstrate concern for the interests of publics. We use the plural term “publics” as opposed to the singular “public” to illustrate that stakeholder groups to which organizations must appeal are often diverse and varied in their assessments of whether a given organizational activity is acceptable6. Despite the potentially fragmented views of various publics, the concept of social license is considered in a holistic way (either an organization has it or does not). Social license is closely related to public trust, and where publics view a particular institution as trustworthy it is more likely to have social license to engage in activities such as the collection and use of personal data7.

The question of how the leaders of an organization might better understand whether they have social license for a particular set of activities has also been addressed in the literature. In a review of literature on social license, Moffat et al. highlighted disagreement in the research community about whether social license can be accurately measured4. Certain groups of researchers emphasize that because of the intangible nature of social license, accurate measurement will never truly be possible. Others propose conceptual models of the determinants of social license, and establish surveys that assess those determinants to indicate the presence or absence of social license in a given context. However, accurate measurement of social license remains a point of debate….(More)”.

An Open-Source Tool to Accelerate Scientific Knowledge Discovery

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Mozilla: “Timely and open access to novel outputs is key to scientific research. It allows scientists to reproduce, test, and build on one another’s work — and ultimately unlock progress.

The most recent example of this is the research into COVID-19. Much of the work was published in open access journals, swiftly reviewed and ultimately improving our understanding of how to slow the spread and treat the disease. Although this rapid increase in scientific publications is evident in other domains too, we might not be reaping the benefits. The tools to parse and combine this newly created knowledge have roughly remained the same for years.

Today, Mozilla Fellow Kostas Stathoulopoulos is launching Orion — an open-source tool to illuminate the science behind the science and accelerate knowledge discovery in the life sciences. Orion enables users to monitor progress in science, visually explore the scientific landscape, and search for relevant publications.

Orion

Orion collects, enriches and analyses scientific publications in the life sciences from Microsoft Academic Graph.

Users can leverage Orion’s views to interact with the data. The Exploration view shows all of the academic publications in a three-dimensional visualization. Every particle is a paper and the distance between them signifies their semantic similarity; the closer two particles are, the more semantically similar. The Metrics view visualizes indicators of scientific progress and how they have changed over time for countries and thematic topics. The Search view enables the users to search for publications by submitting either a keyword or a longer query, for example, a sentence or a paragraph of a blog they read online….(More)”.

The Razor’s Edge: Liberalizing the Digital Surveillance Ecosystem

Curated on Posted on by Stefaan Verhulst

Report by CNAS: “The COVID-19 pandemic is accelerating global trends in digital surveillance. Public health imperatives, combined with opportunism by autocratic regimes and authoritarian-leaning leaders, are expanding personal data collection and surveillance. This tendency toward increased surveillance is taking shape differently in repressive regimes, open societies, and the nation-states in between.

China, run by the Chinese Communist Party (CCP), is leading the world in using technology to enforce social control, monitor populations, and influence behavior. Part of maximizing this control depends on data aggregation and a growing capacity to link the digital and physical world in real time, where online offenses result in brisk repercussions. Further, China is increasing investments in surveillance technology and attempting to influence the patterns of technology’s global use through the export of authoritarian norms, values, and governance practices. For example, China champions its own technology standards to the rest of the world, while simultaneously peddling legislative models abroad that facilitate access to personal data by the state. Today, the COVID-19 pandemic offers China and other authoritarian nations the opportunity to test and expand their existing surveillance powers internally, as well as make these more extensive measures permanent.

Global swing states are already exhibiting troubling trends in their use of digital surveillance, including establishing centralized, government-held databases and trading surveillance practices with authoritarian regimes. Amid the pandemic, swing states like India seem to be taking cues from autocratic regimes by mandating the download of government-enabled contact-tracing applications. Yet, for now, these swing states appear responsive to their citizenry and sensitive to public agitation over privacy concerns.

Today, the COVID-19 pandemic offers China and other authoritarian nations the opportunity to test and expand their existing surveillance powers internally, as well as make these more extensive measures permanent.

Open societies and democracies can demonstrate global surveillance trends similar to authoritarian regimes and swing states, including the expansion of digital surveillance in the name of public safety and growing private sector capabilities to collect and analyze data on individuals. Yet these trends toward greater surveillance still occur within the context of pluralistic, open societies that feature ongoing debates about the limits of surveillance. However, the pandemic stands to shift the debate in these countries from skepticism over personal data collection to wider acceptance. Thus far, the spectrum of responses to public surveillance reflects the diversity of democracies’ citizenry and processes….(More)”.