The present paper, which composes a broader research agenda developed by Data Privacy Brasil Research Association, aims to conduct a descriptive and qualitative study of the Brazilian National Identity System through a data protection and data justice perspective. For this purpose, a desk research and legislation analyses were conducted to answer the following question: is the Brazilian National Civil Identification System (Identificação Civil Nacional or ICN) framework adequate with the Brazilian data protection general legislation and its principles—especially regarding its information architecture? As a result, we found that the current information architecture of the ICN, as well as its current use, may reveal a set of concerns related to data protection and data justice.

The collection of data, its analysis, and the publication of insights from data promise a range of benefits, but can carry risks for individuals and organizations. This paper sets out considerations regarding the potential role for technologies in governance of data use, and some key limitations. The paper examines the potential of Privacy Enhancing Technologies (PETs) to support organizations and institutions that handle data in governing data use, and considers their role based on their current state of development and the trajectory of technological development. This involves consideration both of how these technologies can potentially enable governments and others to unlock the value of data, and also recognition of both contingent and in principle limitations on the role of PETs in ensuring well-governed use of data.

Understanding the spatio-temporal patterns of users’ travel behavior on public transport (PT) systems is essential for more assertive transit planning. With this in mind, the aim of this article is to diagnose the spatial and temporal travel patterns of users of Fortaleza’s PT network, which is a trunk-feeder network whose fares are charged by a tap-on system. To this end, 20 databases were used, including global positioning system, user registration, and PT smart card data from November 2018, prior to the pandemic. The data set was processed and organized into a database with a relational model and an Extraction, Transformation, and Loading process. A data mining approach based on Machine Learning models was applied to evaluate travel patterns. As a result, it was observed that users’ first daily use has a higher percentage of spatial and temporal patterns when compared to their last daily use. In addition, users rarely show spatial and temporal patterns at the same time.

While many Latin American countries have a tradition of receiving migrants, including the countries selected as case studies, there are no institutionalized mechanisms for the integration and settlement of migrants. The objective of this article is to explore how to improve migration data collection and management in a region that does not have many migration integration policies in place. I assess the state of migration data collection and management in three case studies: the city of Cucuta in Colombia, the North Huetar Region in Costa Rica, and the city of Monterrey in Mexico. The three countries publish data exclusively at the national level, rather than the local or municipal. Despite all case studies having a variety of administrative data, mainly in the form of entries and exits by nationality, these data are not enough to properly identify the sociodemographic characteristics of migrant populations in a country, and much less in specific cities. I make recommendations divided into three main themes to improve migration data in Latin America.

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 Internet of Things (IoT) is currently developing fast and its potential as driver of innovative solutions is increasing, pushed by technologies, networks, communication, and computing power, and has the potential to drive the development of technological ecosystems, such as innovation clusters. Innovation clusters are agglomeration of enterprises and research organizations, which cooperate, interact and compete, generating innovation and driving the growth of ecosystems. The narrative around innovation clusters has been developing since many years and policy-makers seek to use such clusters as a policy instrument to support the growth of technology on the one hand and regional and sectoral development on the other hand. This policy paper expands an empirical study on IoT innovation clusters in Europe and places it within the current debate around clusters and innovation clusters to provide evidence-based advice to policy-makers on what may and may not work as public policy measures. The paper highlights the findings of the interaction with several hundred European IoT innovation clusters and points out their points of view on their own creation factors, operational characteristics, and success stories, as well as their expectations in respect to policy interventions for IoT and for clusters. Suggestions for IoT policy-making are provided. The paper has also undertaken an extensive review of up-to date research on innovation cluster creation and performance, thoroughly analyzing the real possibility to define causal relationships between clusters, productivity and economic growth, and business performance, and providing suggestions for policy-makers on the approach to cluster policy.

The recent surge of data-driven methods in social policy have created new opportunities to assess existing poverty programs. The expectation is that the combination of advanced methods and more data can calculate the effectiveness of public interventions more accurately and tailor local initiatives accordingly. Specifically, nonmonetary indicators are increasingly being measured at micro levels in order to target social exclusion in combination with poverty. However, the multidimensional character of poverty, local context, and data matching pose challenges to data-driven analyses. By linking Dutch household-level data with policy-initiative-specific data at local level, we present an explorative study on the uptake of a local poverty pass. The goal is to unravel pass usage in terms of household income and location as well as the age of users. We find that income and age play a role in whether the pass is used, and usage differs per neighborhood. With this, the paper feeds into the discourse on how to operationalize and design data matching work in the multidimensional space of poverty and nonmonetary government initiatives.

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.

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.

The “budgeting for SDGs”–B4SDGs–paradigm seeks to coordinate the budgeting process of the fiscal cycle with the sustainable development goals (SDGs) set by the United Nations. Integrating the goals into public financial management systems is crucial for an effective alignment of national development priorities with the objectives set in the 2030 Agenda. Within the dynamic process defined in the B4SDGs framework, the step of SDG budget tagging represents a precondition for subsequent budget diagnostics. However, developing a national SDG taxonomy requires substantial investment in terms of time, human, and administrative resources. Such costs are exacerbated in least-developed countries, which are often characterized by a constrained institutional capacity. The automation of SDG budget tagging could represent a cost-effective solution. We use well-established text analysis and machine learning techniques to explore the scope and scalability of automatic labeling budget programs within the B4SDGs framework. The results show that, while our classifiers can achieve great accuracy, they face limitations when trained with data that is not representative of the institutional setting considered. These findings imply that a national government trying to integrate SDGs into its planning and budgeting practices cannot just rely solely on artificial intelligence (AI) tools and off-the-shelf coding schemes. Our results are relevant to academics and the broader policymaker community, contributing to the debate around the strengths and weaknesses of adopting computer algorithms to assist decision-making processes.

Researchers have encountered many issues while studying rare illnesses such as lack of information, limited sample sizes, difficulty in diagnosis, and more. However, perhaps the biggest challenge is to recruit a large enough sample size for clinical studies; at the same time, obtaining chronological data for these patients is even more difficult. This has urged us to implement a decentralized crowdsourcing medical data sharing platform to obtain chronological rare data for certain diseases, providing both patients and other stakeholders an easier and more secure way of trading medical data by utilizing blockchain technology. This facilitates the obtention of the most elusive types of health data by dynamically allocating extra financial incentives depending on data scarcity. We also provide a novel framework for medical data cross-validation where the system checks the volunteer reviewer count. The review score depends on the count, and the more the reviewers, the bigger the final score. We also explain how differential privacy is used to protect the privacy of individual medical data while enabling data monetization.

Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

This paper investigates how socio-spatial aspects of creativity, operationalized as the causal relations between the built environment and perceived creativity in university campuses’ public spaces, are currently applied in practice. Moreover, it discusses practitioners’ perceptions regarding research-generated evidence on socio-spatial aspects of creativity according to three effectiveness aspects: credibility, relevance, and applicability. The “research-generated evidence” is herein derived from data-driven knowledge generated by multi-disciplinary methodologies (e.g., self-reported perceptions, participatory tools, geospatial analysis, observations). Through a thematic analysis of interviews with practitioners involved in the (re)development of campuses public spaces of inner-city campuses and science parks in Amsterdam, Utrecht, and Groningen. We concluded that socio-spatial aspects of creativity concepts were addressed only at the decision-making level for Utrecht Science Park. Correspondingly, while presented evidence was considered by most practitioners as relevant for practice, perceptions of credibility and applicability vary according to institutional goals, practitioners’ habits in practice, and their involvement in projects’ roles and phases. The newfound interrelationships between the three effectiveness aspects highlighted (a) the institutional fragmentation issues in campuses and public spaces projects, (b) the research-practice gap related to such projects, which occur beyond the university campuses’ context, and (c) insights on the relationship between evidence generated through research-based data-driven knowledge and urban planning practice, policy, and governance related to knowledge environments. We concluded that if research-generated evidence on socio-spatial aspects of creativity is to be integrated into the evidence-based practice of campuses’ public spaces, an alignment between researchers, multiple actors involved, policy framing, and goal achievements are fundamental.

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.

Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

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.

Access to waste management services is crucial for urban sustainability, impacting public health, environmental well-being, and overall quality of life. This study employs logistic regression analysis on survey data collected from 1,032 household heads residing in Nouakchott, the capital of Mauritania. The survey investigated key household factors that determine access to waste management services. The findings reveal a significant interplay among waste service provision, the presence of cisterns, housing type and size, and access to electricity. Socioeconomic disparity in service access, with poorer housing formats like shacks receiving substandard services. In contrast, areas with robust electrification report better service access, although inconsistencies remain amid power outages. The research highlights the challenges faced by Riyadh municipality, particularly rapid growth and inadequate infrastructure, which hinder waste management efficiency. Overall, the results not only illuminate Nouakchott’s unique challenges in service provision but also propose actionable recommendations for a sustainable urban future. These recommendations aim to inform and guide targeted policies for improving living conditions and environmental sustainability in urban Mauritania.

Publicly funded data-driven innovation programmes frequently involve partnerships between small and medium enterprises (SMEs) and municipal authorities utilizing citizen data. The intention of these projects is to benefit citizens. However, few such projects achieve success or impact within the project timeframe. This may result in benefit accruing mainly to the SME partner, who gains both learning and data, engendering questions of data justice around whether citizen data are being exploited without sufficient benefit returning to citizens. Through case studies composed of interviews and document analysis, we examine how benefits for citizens are conceived and achieved in the publicly funded data-driven air quality projects Data Pitch and Smart Cities Innovation Framework Implementation. We find the differences between the programme funders’ policies had a clear influence on the citizen engagement elements. There are also a number of ways in which the desired citizen engagement and benefit becomes diluted, including through misalignment of incentives and focus, a lack of prioritization and ownership, and power imbalances between citizens and the other actors in the quadruple helix model. To retain the focus on ensuring citizens benefit from data-driven innovation programmes using citizen data, we propose the use of data Justice plans. More work is required to specify the content and mechanisms of such plans for application in such programmes.

Research that examines the impact of economic, social, and political factors on political corruption uses expert’ and citizen’ perceptions for measuring corruption and testing arguments. Scholars argue that the perception of corruption is a good proxy for actual corruption because data on actual corruption are limited and not entirely trustworthy. However, perception indexes do not allow for testing separate mechanisms driving citizen’ perceptions of corruption from actual levels of corruption in different government branches. To address this issue, I introduce a new index based on Latin American countries to measure the risk of corruption in political parties. Using a de jure analysis of laws and regulations, the Risk of Corruption (ROC) index evaluates the likelihood of political parties engaging in corrupt activities. Instead of measuring corrupt activities or perception directly, the ROC measures the risks of involving in corruption. The index has important implications for academics and practitioners in anti-corruption issues. First, it allows us to test arguments about the role of political parties and legislatures in reducing political corruption. Second, it helps to understand how political parties could improve their internal organization to decrease the risk of corrupt activities. Finally, it is a valuable instrument for cross-national studies in diverse fields that study political parties.