Paper by Dominik Rozkrut, Olga Świerkot-Strużewska, and Gemma Van Halderen: “Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information….(More)”.
Paper by M. Usman Mirza et al: “Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality.
To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all….(More)”.
Book edited by Emre Eren Korkmaz: “…discusses how states deploy frontier and digital technologies to manage and control migratory movements. Assessing the development of blockchain technologies for digital identities and cash transfer; artificial intelligence for smart borders, resettlement of refugees and assessing asylum applications; social media and mobile phone applications to track and surveil migrants, it critically examines the consequences of new technological developments and evaluates their impact on the rights of migrants and refugees.
Chapters evaluate the technology-based public-private projects that govern migration globally and illustrate the political implications of these virtual borders. International contributors compare and contrast different forms of political expression, in both personal technologies, such as social media for refugees and smugglers, and automated decision-making algorithms used by states to enable migration governance. This timely book challenges hegemonic approach to migration governance and provides cases demonstrating the dangers of employing frontier technologies denying basic rights, liberties and agencies of migrants and refugees.
Stepping into a contentious political climate for migrants and refugees, this provocative book is ideal reading for scholars and researchers of political science and public policy, particularly those focusing on migration and refugee studies. It will also benefit policymakers and practitioners dealing with migration, such as humanitarian NGOs, UN agencies and local authorities….(More)”.
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)”.
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)”.
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)”.
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)”
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.
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.
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)”.
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)”.
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)”