Large-scale population surveys have been an important source of data for the study of migration, and in many countries provide the only widely accessible data on migrants’ characteristics and outcomes after they arrive. For immigration policymakers, however, official survey data have some important limitations. Nonresponse to surveys is particularly likely to affect newly arrived migrants, biasing analysis toward more settled populations who have different characteristics (e.g., different fiscal costs), and hindering analysis of how integration outcomes evolve after arrival. Survey data are not well suited to capturing the dynamics of a mobile population, particularly among groups of migrants who spend substantial periods outside the country. And perhaps most importantly, official survey data usually identify migrants by country of birth and nationality (and sometimes self-reported reason for migration) but rarely include information on a person’s legal status either at arrival or at the time of data collection. This significantly limits the possibilities for evaluating policy and the impacts of policy changes: the characteristics of migrants coming for different reasons can vary enormously, so policymakers should be cautious about assuming that aggregate evidence on migrants or migration will be relevant to the specific routes on which they are taking decisions. This article illustrates some of these problems in practice showing how official survey data in the United Kingdom have been unable to answer one of the key questions facing the government, namely how many and which EU citizens need to apply to secure their residence rights after Brexit.

The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System (ICEWS). The performance of the ARFIMA model is compared to that of a naïve model in reference to two common relevant hypotheses: the ARFIMA model would outperform a naïve model and the rate of outperformance would deteriorate the higher the level of spatial aggregation. This analytical strategy is used to predict political violent events in Afghanistan. The analysis consists of three parts. The first is a replication of Yonamine’s study for the period beginning in April 2010 and ending in March 2012. The second part compares the results to those of Yonamine. The comparison was used to assess the validity of the conclusions drawn in the original study, which was based on the Global Database of Events, Language, and Tone, for the implementation of this approach to ICEWS data. Building on the conclusions of this comparison, the third part uses Yonamine’s approach to predict violent events in Afghanistan over a significantly longer period of time (January 1995–August 2021). The conclusions provide an assessment of the utility of short-term localized forecasting.

Internet and Communication Technology/electrical and electronic equipment (ICT/EEE) form the bedrock of today’s knowledge economy. This increasingly interconnected web of products, processes, services, and infrastructure is often invisible to the user, as are the resource costs behind them. This ecosystem of machine-to-machine and cyber-physical-system technologies has a myriad of (in)direct impacts on the lithosphere, biosphere, atmosphere, and hydrosphere. As key determinants of tomorrow’s digital world, academic institutions are critical sites for exploring ways to mitigate and/or eliminate negative impacts. This Report is a self-deliberation provoked by the question How do we create more resilient and healthier computer science departments: living laboratories for teaching and learning about resource-constrained computing, computation, and communication? Our response for University College London (UCL) Computer Science is to reflect on how, when, and where resources—energy, (raw) materials including water, space, and time—are consumed by the building (place), its occupants (people), and their activities (pedagogy). This perspective and attendant first-of-its-kind assessment outlines a roadmap and proposes high-level principles to aid our efforts, describing challenges and difficulties hindering quantification of the Department’s resource footprint. Qualitatively, we find a need to rematerialise the ICT/EEE ecosystem: to reveal the full costs of the seemingly intangible information society by interrogating the entire life history of paraphernalia from smartphones through servers to underground/undersea cables; another approach is demonstrating the corporeality of commonplace phrases and Nature-inspired terms such as artificial intelligence, social media, Big Data, smart cities/farming, the Internet, the Cloud, and the Web. We sketch routes to realising three interlinked aims: cap annual power consumption and greenhouse gas emissions, become a zero waste institution, and rejuvenate and (re)integrate the natural and built environments.

This commentary explores the potential of private companies to advance scientific progress and solve social challenges through opening and sharing their data. Open data can accelerate scientific discoveries, foster collaboration, and promote long-term business success. However, concerns regarding data privacy and security can hinder data sharing. Companies have options to mitigate the challenges through developing data governance mechanisms, collaborating with stakeholders, communicating the benefits, and creating incentives for data sharing, among others. Ultimately, open data has immense potential to drive positive social impact and business value, and companies can explore solutions for their specific circumstances and tailor them to their specific needs.

The coronavirus disease-2019 (COVID-19) pandemic and the mobility restrictions governments imposed to prevent its spread changed the cities’ ways of living. Transport systems suffered the consequences of the falling travel demand, and readjustments were made in many cities to prevent the complete shutdown of services. In Córdoba, the second largest city in Argentina, the Municipality dictated route cuts and reduced frequencies to sustain the buses and trolleys system. In 2022, Martinazzo and Falavigna assessed potential accessibility to hospitals before (2019) and during the pandemic (2021). Overall, the study indicated that average travel times increased by 20% and that the gap between less vulnerable and more vulnerable population quintiles reached almost 8 points. In this paper, potential accessibility to public hospitals in 2022 and 2023 is calculated using Martinazzo and Falavigna’s (2022) work as a baseline to compare, considering that neither cutting the services during the pandemic nor recovering the service after the pandemic the Municipality performed an accessibility assessment. The main results showed that, despite the system having almost recovered its extension by 2023, it maintained the regressive tendency between less vulnerable and more vulnerable population quintiles, as the difference in average travel time between these two groups reached up to 14 min, while the cumulative opportunities measure for the high-income groups was up to 68% higher than the most vulnerable households.

Enabling private sector trust stands as a critical policy challenge for the success of the EU Data Governance Act and Data Act in promoting data sharing to address societal challenges. This paper attributes the widespread trust deficit to the unmanageable uncertainty that arises from businesses’ limited usage control to protect their interests in the face of unacceptable perceived risks. For example, a firm may hesitate to share its data with others in case it is leaked and falls into the hands of business competitors. To illustrate this impasse, competition, privacy, and reputational risks are introduced, respectively, in the context of three suboptimal approaches to data sharing: data marketplaces, data collaboratives, and data philanthropy. The paper proceeds by analyzing seven trust-enabling mechanisms comprised of technological, legal, and organizational elements to balance trust, risk, and control and assessing their capacity to operate in a fair, equitable, and transparent manner. Finally, the paper examines the regulatory context in the EU and the advantages and limitations of voluntary and mandatory data sharing, concluding that an approach that effectively balances the two should be pursued.

In the context of the ongoing Russian invasion and the uncertainties surrounding the potential return migration of millions of displaced Ukrainians, this study explores the future of (return) migration through an innovative and inclusive participatory foresight approach, engaging 20 displaced Ukrainians residing in Valencia, Spain, from May to December 2023. The foresight process included workshops, discussions via online messaging groups, interviews, participatory observations, and culminated in an open art exhibition. Through this process, we conducted a collective horizon scanning, identifying weak signals and emerging trends, followed by an examination of critical uncertainties, which led to the development of four distinct scenarios: Exhaustion Return, Energetic Return, Virtual Return, and Disconnection. The insights derived from this foresight exercise hold practical relevance for both Ukrainian and EU migration policymakers, emphasizing the importance of lived experiences in shaping anticipatory migration policies. This study also offers theoretical contributions by applying participatory foresight to the field of return migration, challenging established knowledge paradigms, and fostering a more inclusive and nuanced understanding of migration dynamics and their broader implications.

Open data promises various benefits, including stimulating innovation, improving transparency and public decision-making, and enhancing the reproducibility of scientific research. Nevertheless, numerous studies have highlighted myriad challenges related to preparing, disseminating, processing, and reusing open data, with newer studies revealing similar issues to those identified a decade prior. Several researchers have proposed the open data ecosystem (ODE) as a lens for studying and devising interventions to address these issues. Since actors in the ecosystem are individually and collectively impacted by the sustainability of the ecosystem, all have a role in tackling the challenges in the ODE. This paper asks what the contributions of open data intermediaries may be in addressing these challenges. Open data intermediaries are third-party actors providing specialized resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders. They are critical in ensuring the flow of resources within the ODE. Through semi-structured interviews and a validation exercise in the European Union context, this study explores the potential contribution of open data intermediaries and the specific ODE challenges they may address. This study identified 20 potential contributions, addressing 27 challenges. The findings of this study pave the way for further inquiry into the internal incentives (viable business models) and external incentives (policies and regulations) to direct the contributions of open data intermediaries toward addressing challenges in the ODE.

This study uses anonymized GPS traces to explore travel patterns within six suburban zones and a central area in Mexico City. The descriptive analysis presented in this paper profiles trips by distance and investigates their distribution within each zone. It examines the prevalence of local trips, walkability, and the availability and spread of entertainment sites within 15-min isochrones accessible by foot, bicycle, transit, and private vehicle. Notably, the central zone boasts diverse entertainment offerings, commendable walkability, and a substantial proportion of short and long trips. It is found that GPS traces are within their home. However, the share of long trips for the inhabitants of central zones is considerably more significant than that for the suburbs. The study highlights suburban zones that could benefit from governmental intervention to enhance transportation and pedestrian conditions. Additionally, it identifies other suburban zones that resemble the central areas in terms of walkability, trip distribution by distances, and the accessibility of entertainment places.

This article uses data from several publicly available databases to show that the distribution of intellectual property for frontier technologies, including those useful for sustainable development, is very highly skewed in favor of a handful of developed countries. The intellectual property rights (IPR) regime as it exists does not optimize the global flow of technology and know-how for the attainment of the sustainable development goals and is in need of updating. Some features of the Fourth Industrial Revolution imply that the current system of patents is even more in need of reform than before. COVID-19 vaccines and therapies and the vast inequality in access to these has highlighted the costs of inaction. We recommend several policy changes for the international IPR regime. Broadly, these fall into three categories: allowing greater flexibility for developing countries, reassessing the appropriateness of patents for technologies that may be considered public goods, and closing loopholes that allow for unreasonable intellectual property protections.

The REDATAM (retrieval of data for small areas by microcomputer) statistical package and format, developed by ECLAC, has been a critical tool for disseminating census data across Latin America since the 1990s. However, significant limitations persist, including its proprietary nature, lack of documentation, and restricted flexibility for advanced data analysis. These challenges hinder the transformation of raw census data into actionable information for policymakers, researchers, and advocacy groups. To address these issues, we developed Open REDATAM, an open-source and multiplatform tool that converts REDATAM data into widely supported CSV files and native R and Python data structures. By providing integration with R and Python, Open REDATAM empowers users to work with the tools they already know and perform data analyses without leaving their R or Python window. Our work emphasizes the need for a REDATAM official format specification to further enable informed policy debates that can improve policy processes’ implementation and feedback.

Digital identity systems are promoted with the promise of great benefit and inclusion. The case of the Ugandan digital identity system demonstrates that the impact of digital identity systems is not only positive but also has negative impacts, significantly affecting human lives for the worse. The impact on the human lives of digital identity systems can be assessed by multiple frameworks. A specific framework that has been mentioned is the capabilities approach (CA). This article demonstrates that the CA is a framework to assess the impact on human lives that can be operationalized for technology and information and communication technology, including digital identity systems. Further research is required to compare the CA with other candidate evaluation frameworks.

The “la Caixa” Foundation has been experimenting with artificial intelligence (AI)-assisted decision-making geared toward alleviating the administrative burden associated with the evaluation pipeline of its flagship funding program, piloting an algorithm to detect immature project proposals before they reach the peer review stage, and suggest their removal from the selection process to a human overseer. In this article, we explore existing uses of AI by publishers and research funding organizations to automate their selection pipelines, in addition to analyzing the conditions under which the focal case corresponds to a responsible use of AI and the extent to which these conditions are met by the current implementation, highlighting challenges and areas of improvement.

The advent of smart and digital cities is bringing data to the forefront as a critical resource for addressing the multifaceted transitions faced by African cities from rapid urbanization to the climate crisis. However, this commentary highlights the formidable considerations that must be addressed to realize the potential of data-driven urban planning and management. We argue that data should be viewed as a tool, not a panacea, drawing from our experience in modeling and mapping the accessibility of transport systems in Accra and Kumasi, Ghana. We identify five key considerations, including data choice, imperfections, resource intensity, validation, and data market dynamics, and propose three actionable points for progress: local data sharing, centralized repositories, and capacity-building. While our focus is on Kumasi and Accra, the considerations discussed are relevant to cities across the African continent.

Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.

Donor organizations and multilaterals require ways to measure progress toward the goals of creating an open internet, and condition assistance on recipient governments maintaining access to information online. Because the internet is increasingly becoming a leading tool for exchanging information, authoritarian governments around the world often seek methods to restrict citizens’ access. Two of the most common methods for restricting the internet are shutting down internet access entirely and filtering specific content. We conduct a systematic literature review of articles on the measurement of internet censorship and find that little work has been done comparing the tradeoffs of using different methods to measure censorship on a global scale. We compare the tradeoffs between measuring these phenomena using expert analysis (as measured by Freedom House and V-Dem) and remote measurement with manual oversight (as measured by Access Now and the OpenNet Initiative [ONI]) for donor organizations that want to incentivize and measure good internet governance. We find that remote measurement with manual oversight is less likely to include false positives, and therefore may be more preferable for donor organizations that value verifiability. We also find that expert analysis is less likely to include false negatives, particularly for very repressive regimes in the Middle East and Central Asia and therefore these data may be preferable for advocacy organizations that want to ensure very repressive regimes are not able to avoid accountability, or organizations working primarily in these areas.

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored.

The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level.