Paper by David Tindall, John McLevey, Yasmin Koop-Monteiro, Alexander Graham: “While sociologists have studied social networks for about one hundred years, recent developments in data, technology, and methods of analysis provide opportunities for social network analysis (SNA) to play a prominent role in the new research world of big data and computational social science (CSS). In our review, we focus on four broad topics: (1) Collecting Social Network Data from the Web, (2) Non-traditional and Bipartite/Multi-mode Networks, including Discourse and Semantic Networks, and Social-Ecological Networks, (3) Recent Developments in Statistical Inference for Networks, and (4) Ethics in Computational Network Research…(More)”
The Use of Artificial Intelligence as a Strategy to Analyse Urban Informality
Article by Agustina Iñiguez: “Within the Latin American and Caribbean region, it has been recorded that at least 25% of the population lives in informal settlements. Given that their expansion is one of the major problems afflicting these cities, a project is presented, supported by the IDB, which proposes how new technologies are capable of contributing to the identification and detection of these areas in order to intervene in them and help reduce urban informality.
Informal settlements, also known as slums, shantytowns, camps or favelas, depending on the country in question, are uncontrolled settlements on land where, in many cases, the conditions for a dignified life are not in place. Through self-built dwellings, these sites are generally the result of the continuous growth of the housing deficit.
For decades, the possibility of collecting information about the Earth’s surface through satellite imagery has been contributing to the analysis and production of increasingly accurate and useful maps for urban planning. In this way, not only the growth of cities can be seen, but also the speed at which they are growing and the characteristics of their buildings.
Advances in artificial intelligence facilitate the processing of a large amount of information. When a satellite or aerial image is taken of a neighbourhood where a municipal team has previously demarcated informal areas, the image is processed by an algorithm that will identify the characteristic visual patterns of the area observed from space. The algorithm will then identify other areas with similar characteristics in other images, automatically recognising the districts where informality predominates. It is worth noting that while satellites are able to report both where and how informal settlements are growing, specialised equipment and processing infrastructure are also required…(More)”
The Staggering Ecological Impacts of Computation and the Cloud
Essay by Steven Gonzalez Monserrate: “While in technical parlance the “Cloud” might refer to the pooling of computing resources over a network, in popular culture, “Cloud” has come to signify and encompass the full gamut of infrastructures that make online activity possible, everything from Instagram to Hulu to Google Drive. Like a puffy cumulus drifting across a clear blue sky, refusing to maintain a solid shape or form, the Cloud of the digital is elusive, its inner workings largely mysterious to the wider public, an example of what MIT cybernetician Norbert Weiner once called a “black box.” But just as the clouds above us, however formless or ethereal they may appear to be, are in fact made of matter, the Cloud of the digital is also relentlessly material.
To get at the matter of the Cloud we must unravel the coils of coaxial cables, fiber optic tubes, cellular towers, air conditioners, power distribution units, transformers, water pipes, computer servers, and more. We must attend to its material flows of electricity, water, air, heat, metals, minerals, and rare earth elements that undergird our digital lives. In this way, the Cloud is not only material, but is also an ecological force. As it continues to expand, its environmental impact increases, even as the engineers, technicians, and executives behind its infrastructures strive to balance profitability with sustainability. Nowhere is this dilemma more visible than in the walls of the infrastructures where the content of the Cloud lives: the factory libraries where data is stored and computational power is pooled to keep our cloud applications afloat….
To quell this thermodynamic threat, data centers overwhelmingly rely on air conditioning, a mechanical process that refrigerates the gaseous medium of air, so that it can displace or lift perilous heat away from computers. Today, power-hungry computer room air conditioners (CRACs) or computer room air handlers (CRAHs) are staples of even the most advanced data centers. In North America, most data centers draw power from “dirty” electricity grids, especially in Virginia’s “data center alley,” the site of 70 percent of the world’s internet traffic in 2019. To cool, the Cloud burns carbon, what Jeffrey Moro calls an “elemental irony.” In most data centers today, cooling accounts for greater than 40 percent of electricity usage….(More)”.
Launch of UN Biodiversity Lab 2.0: Spatial data and the future of our planet
Press Release: “…The UNBL 2.0 is a free, open-source platform that enables governments and others to access state-of-the-art maps and data on nature, climate change, and human development in new ways to generate insight for nature and sustainable development. It is freely available online to governments and other stakeholders as a digital public good…
The UNBL 2.0 release responds to a known global gap in the types of spatial data and tools, providing an invaluable resource to nations around the world to take transformative action. Users can now access over 400 of the world’s best available global spatial data layers; create secure workspaces to incorporate national data alongside global data; use curated data collections to generate insight for action; and more. Without specialized tools or training, decision-makers can leverage the power of spatial data to support priority-setting and the implementation of nature-based solutions. Dynamic metrics and indicators on the state of our planet are also available….(More)”.
AI, big data, and the future of consent
Paper by Adam J. Andreotta, Nin Kirkham & Marco Rizzi: “In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts…(More)”
Satellite Earth observation for sustainable rural development
A blog post by Peter Hargreaves: “…We find ourselves in a “golden age for satellite exploration”. ‘Big Data’ from satellite Earth observation – hereafter denoted ‘EO’ – could be an important part of the solution to the shortage of socioeconomic data required to inform several of the goals and targets that compose the United Nations (UN) Sustainable Development Goals (SDGs) [hyperlink]. In particular, the goals that pertain to socioeconomic and human wellbeing dimensions of development. EO data could play a significant role in producing the transparent data system necessary to achieve sustainable development….
Census and nationally representative household surveys are the medium through which most socioeconomic data are collected. It is impossible to understand socioeconomic conditions without them – I cannot stress this enough. But they have limitations, particularly in terms of cost and spatio-temporal coverage. In an ideal world, we would vastly upscale the spatial and temporal reporting of these surveys to cover more places and points in time. But this mass enumeration would be prohibitively expensive and *logistically impossible*. Imagine the quantity of data produced and the burden placed upon National Statistics Offices (NSOs) and governmental institutions? The 2030 end point for the SDGs would be upon us before much of the data was processed leaving very little time to use the outputs for policy.
This is where unconventional data enters the debate, and in this sphere – that of measuring socioeconomic conditions for development – EO data is unconventional. EO data has considerable potential to augment survey and census data for measuring rural poverty development in rural spaces, especially during intercensal periods, and where ground data are patchy, or non-existent. While on the subject, there is an important point to make: you can’t use EO to understand everything about a particular context. It does not matter how elaborate the model or the effort put in. Quite simply, EO cannot give you the full picture.
What EO *does* have is a five-decade temporal legacy (most platforms and data products are near continuous), and its broadly open access with low to negligible acquisition costs. EO data is also availabile across multiple spatial resolutions and is often easily comparable and complementary. When we say, ‘five-decade temporal legacy’, this means that there are roughly 50 years of EO data (if we use the Landsat program as an anchor). Not all EO platforms have operated across the whole timeline – Figure 1 below offers an idea of when different platforms were launched and for how long they were, or have been, operational. What’s more, data will be increasingly available and accessible, catalysed by technological innovation and investment in public and private ventures. A lot of this data is open access e.g. EO platforms operated by NASA or the ESA Copernicus programme, which include Landsat, MODIS, AVHRR, VIIRs, and the Sentinels amongst others. Meanwhile, the availability of EO data across multiple spatial resolutions enables disaggregation of data alongside survey and census data for subnational monitoring of socioeconomic conditions….(More)”.
Household Financial Transaction Data
Paper by Scott R. Baker & Lorenz Kueng: “The growth of the availability and use of detailed household financial transaction microdata has dramatically expanded the ability of researchers to understand both household decision-making as well as aggregate fluctuations across a wide range of fields. This class of transaction data is derived from a myriad of sources including financial institutions, FinTech apps, and payment intermediaries. We review how these detailed data have been utilized in finance and economics research and the benefits they enable beyond more traditional measures of income, spending, and wealth. We discuss the future potential for this flexible class of data in firm-focused research, real-time policy analysis, and macro statistics….(More)”.
Lessons learned from telco data informing COVID-19 responses: toward an early warning system for future pandemics?
Introduction to a special issue of Data and Policy (Open Access) by Richard Benjamins, Jeanine Vos, and Stefaan Verhulst: “More than a year into the COVID-19 pandemic, the damage is still unfolding. While some countries have recently managed to gain an element of control through aggressive vaccine campaigns, much of the developing world — South and Southeast Asia in particular — remain in a state of crisis. Given what we now know about the global nature of this disease and the potential for mutant versions to develop and spread, a crisis anywhere is cause for concern everywhere. The world remains very much in the grip of this public health crisis.
From the beginning, there has been hope that data and technology could offer solutions to help inform the government’s response strategy and decision-making. Many of the expectations have been focused on mobile data analytics in particular, whereby mobile network operators create mobility insights and decision-support tools generated from anonymized and aggregated telco data. This stems both from a growing group of mobile network operators having significantly invested in systems and capabilities to develop such products and services for public and private sector customers. As well as their value having been demonstrated in addressing different global challenges, ranging from models to better understand the spread of Zika in Brazil to interactive dashboards to aid emergency services during earthquakes and floods in Japan. Yet despite these experiences, many governments across the world still have limited awareness, capabilities and resources to leverage these tools, in their efforts to limit the spread of COVID-19 using non-pharmaceutical interventions (NPI), both from a medical and economic point of view.
Today, we release the first batch of papers of a special collection of Data & Policy that examines both the potential of mobile data, as well as the challenges faced in delivering these tools to inform government decision-making. Consisting of 5 papers from 33 researchers and experts from academia, industry and government, the articles cover a wide range of geographies, including Europe, Argentina, Brazil, Ecuador, France, Gambia, Germany, Ghana, Austria, Belgium, and Spain. Responding to our call for case studies to illustrate the opportunities (and challenges) offered by mobile big data in the fight against COVID-19, the authors of these papers describe a number of examples of how mobile and mobile-related data have been used to address the medical, economic, socio-cultural and political aspects of the pandemic….(More)”.
Big Tech platforms in health research: Re-purposing big data governance in light of the General Data Protection Regulation’s research exemption
Paper by Luca Marelli, Giuseppe Testa, and Ine van Hoyweghen: “The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention toward the challenges faced by the European General Data Protection Regulation in regulating the potentially disruptive engagement of Big Tech platforms in health research. The General Data Protection Regulation upholds a rather flexible regime for scientific research through a number of derogations to otherwise stricter data protection requirements, while providing a very broad interpretation of the notion of “scientific research”. Precisely the breadth of these exemptions combined with the ample scope of this notion could provide unintended leeway to the health data processing activities of Big Tech platforms, which have not been immune from carrying out privacy-infringing and socially disruptive practices in the health domain. We thus discuss further finer-grained demarcations to be traced within the broadly construed notion of scientific research, geared to implementing use-based data governance frameworks that distinguish health research activities that should benefit from a facilitated data protection regime from those that should not. We conclude that a “re-purposing” of big data governance approaches in health research is needed if European nations are to promote research activities within a framework of high safeguards for both individual citizens and society….(More)”.
New York vs Big Tech: Lawmakers Float Data Tax in Privacy Push
GovTech article: “While New York is not the first state to propose data privacy legislation, it is the first to propose a data privacy bill that would implement a tax on big tech companies that benefit from the sale of New Yorkers’ consumer data.
Known as the Data Economy Labor Compensation and Accountability Act, the bill looks to enact a 2 percent tax on annual receipts earned off New York residents’ data. This tax and other rules and regulations aimed at safeguarding citizens’ data will be enforced by a newly created Office of Consumer Data Protection outlined in the bill.
The office would require all data controllers and processors to register annually in order to meet state compliance requirements. Failure to do so, the bill states, would result in fines.
As for the tax, all funds will be put toward improving education and closing the digital divide.
“The revenue from the tax will be put towards digital literacy, workforce redevelopment, STEAM education (science, technology, engineering, arts and mathematics), K-12 education, workforce reskilling and retraining,” said Sen. Andrew Gounardes, D-22.
As for why the bill is being proposed now, Gounardes said, “Every day, big tech companies like Amazon, Apple, Facebook and Google capitalize on the unpaid labor of billions of people to create their products and services through targeted advertising and artificial intelligence.”…(More)”