Intervention methods to establish commitment to (collaborative) action are of potential interest to researchers and policymakers intent upon including stakeholder perspectives in natural risk governance (Scolobig, Nat Hazards 81:27–43, 2016). In this paper, a 6-step co-design method for engaging with local people in collaboratively envisioning nature-based solutions for flood defence is described. The problem structuring base of the participatory method is extended to accommodate the multi-actor situation and the local context of flood risk management. The intervention method is applied in a workshop in the Houston–Galveston Bay area in October 2014. At that time there was strong contestation surrounding the proposed Ike Dike with alternative combinations of nature-based and smaller conventional engineering solutions being proposed. The results indicate that the local participants were able to envision a wide range of future outcomes for the bay and were able to use the insights on nature-based solutions and the social contacts that they acquired at the transdisciplinary workshop to mobilize commitment to joint action. This action focused on collaboration rather than specifying ecological or technical infrastructural requirements and was instrumental in initiating more open discourse on flood defence options for the Houston–Galveston Bay area. The paper concludes that the generic applicability of the co-design method is limited by the requirement to understand and accommodate local circumstances and participants’ insights within the workshop.

Public transit networks in cities are crucial in addressing the transforming mobility needs of citizens for work, services and leisure. The rapid changes in urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations, modelling or population data that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variable demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of ≈4.6 million daily transit traces of the primary mode of underground services from the Greater London region reveals distinct commuting profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our results also suggest that heavily used stations that exhibit mixed-use commuting patterns are generally located in the cluster of the central business district and stations away from the centre of the city are largely single use residential areas. Overall, identifying mixed temporal and spatial use of stations diverging from macro mobility patterns in public transit indicates that our approach may be useful in a detailed understanding of integrated transit planning for heterogeneous needs of travellers.

Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.

This study addresses the problem of rumour scarcity versus non-rumour abundance in automatic rumour detection. To tackle this issue, we portray rumour as an anomaly by showing how disproportionate is the number of rumours versus non-rumours. This imbalance is scrutinized by comparing the rate of news production versus rate of fact-check production. Then, we exploit one-class classification approach to distinguish rumour from non-rumour. One-class classification separates rumour from non-rumour via training the classifier with only non-rumour. To train the one-class classifier, we extract 33 short-term features, regarding the purpose of this research in early detection of rumours. We evaluate the performance of our model by accuracy and F-score. In terms of F-score, our model outperforms the state-of-the-art and reaches to very close proximity of highest accuracy on the same dataset.

The spread of false and unverified information has the potential to inflict damage by harming the reputation of individuals or organisations, shaking financial markets, and influencing crowd decisions in important events.This phenomenon needs to be properly curbed, otherwise it can contaminate other aspects of our social life. In this regard, academia as a key institution against false and unverified information is expected to play a pivotal role. Despite a great deal of research in this arena, the amount of progress by academia is not clear yet. This can lead to misjudgements about the performance of the topic of interest that can ultimately result in wrong science policies regarding academic efforts for quelling false and unverified information. In this research, we address this issue by assessing the readiness of academia in the topic of false and unverified information. To this end, we adopt the emergence framework and measure its dimensions (novelty, growth, coherence, and impact) over more than 21,000 articles published by academia about false and unverified information. Our results show the current body of research has had organic growth so far, which is not promising enough for confronting the problem of false and unverified information. To tackle this problem, we suggest an external push strategy that, compared to the early stages of the topic of interest, reinforces the emergence dimensions and leads to a higher level in every dimension.

The development and deployment of technologies depend upon collaborations concurrently relying on proximity between partners. By employing publication data of German nanotechnology, we augment former findings on the relationship between proximity and collaboration in three ways. First, we shed light on how the various forms of proximity affect different stages of collaboration. Particularly, we split geographical proximity into pure physical and systemic proximity. By doing so, we can show that pure physical proximity plays a role early on, as it positively influences the formation of collaborations. In contrast, systemic proximity affects collaborations later on by inducing higher output. Second, innovation systems shape collaboration networks. We learn that specific features of publicly funded German research organizations influence the formation and output of collaborations via organizational proximity or a lack thereof. Third, cognitive proximity has by far the strongest magnitude of effect on both the formation and the output of collaborations. Particularly, existing partnership being cognitively diverse have a lot of potential. Therefore, research policy and university management might consider to stimulating current partnerships, being cognitively different.

Blockchain technology has the potential to provide public services directly to the public. This challenges the need for public organizations, who traditionally provided these services. Much of the current work is focused on the technology, whereas the influence on public administration structure has gained less attention. The goal of this paper is to investigate the impact of blockchain technology on the governance of public service provision. For this, we performed a case study of an EU-wide system that monitors the movement of excise goods under duty suspension. We developed two scenarios for blockchain technology’s use based on a permissionless blockchain architecture on the one hand and a permissioned one on the other. The scenarios were evaluated based on their impact on transaction validation, data quality and governance. The findings show that blockchain technology alone cannot be an alternative for the current data quality controls, equal access assurances and adaptations to legislation conducted by public administrations. As such, governments will remain playing a key role in registration of documents and assets, however, the governance will likely change depending on the type of blockchain architecture.

There is an increasing practice of combining various sorts of data from the city to obtain a comprehensive operating picture for urban managers. The sorts of data being combined includes administrative data, sensor and transactional data, and social media data. In order for these practices to fulfil their full promises it is important that practitioners build systems which measure the correct things. It is also important that theorists develop systematic explanations for complex urban phenomena. This paper confronts the challenges of building better concepts and constructs for urban data analysis and sensing.

Rhythm is fundamental to life. Rhythm can be perceived in the movement of the sun, the moon and the stars. Rhythm makes our hearts tick and defines our breath, in and out. And even the smallest particle in a microbe is part of rhythmic movements. Rhythm in activities is important for culture, for religion, and for sports, schools and hospitals for example. Yet in social situations, social analyses and in social policymaking, rhythm is not considered as a space of analyses or a space of design. City Rhythm explores the potential of using rhythm analyses in the physical world and related data domain for enhancing social safety in neighbourhoods in the Netherlands. Rhythm in the physical world happens both in space as well as in time. Rhythm in data can connect to location (instead of persons), thus circumventing the issue of privacy. However, because the data addresses specific times and places, nonetheless the data still addresses significant social issues.

Data Envelopment Analysis (DEA) with a large amount of variables and little DMUs is problematic. Such a configuration with high variable dimensionality yields too much 100 % efficient DMUs, hence making results invaluable. In this paper a new method is provided, which applies Principal Component Analysis (PCA) as a post-processing tool, to test and select valuable DEA scenarios. DEA efficiencies are calculated for all possible scenarios, which then are analyzed with PCA. Additionally, this method is applied on a dataset with both quantitative and qualitative data and an input- and output-oriented approach. It is apparent that scenarios with high loadings on particular Principal Components yield valuable results with both efficient and inefficient DMUs with different model configurations. Therefore, the new PCA-DEA method has the advantage to work with both DEA orientations and a mixed dataset, with both quantitative and qualitative data. Also it is shown that the method's results can be incorporated in a cash cow diagram, in order to interpret a benchmarking case.

While some studies have observed the beneficial impact of mergers and acquisitions (M&As) on a firm's innovation performance in developed countries, others have found the consequences to be neutral or even negative. This article develops an integrated framework to elucidate how the combination of technological relatedness and product relatedness between acquiring and target firms affects post-innovation performance of technology-driven M&As. This performance is investigated by using a set of parameters, namely R&D input, patent and product activity, and the financial results from commercialisation. We conducted case studies on China's high-tech firms derived from three diverse industry sectors, and the empirical results indicate that both types of relatedness between the partners of technology-driven M&As are conducive to the intensification of R&D expenditures. The acquisition of similar technologies and products has more significant effects on R and D input and output, and M&As without technology relatedness have better financial performance, since they lead acquirers to new technology sectors or sub-sectors. In comparison, M&As with technological complementarity and product complementarity have negative effects on related innovation processes in the short term.

What are the implications of big data in terms of big impacts? Our research focuses on the question, 'How are data analytics involved in policy analysis to create complementary values?' We address this from the perspective of bibliometrics. We initially investigate a set of articles published in Nature and Science, seeking cutting-edge knowledge to sharpen research hypotheses on what data science offers policy analysis. Based on a set of bibliometric models (e.g., topic analysis, scientific evolutionary pathways, and social network analysis), we follow up with studies addressing two aspects: (1) we examine the engagement of data science (including statistical, econometric, and computing approaches) in current policy analyses by analyzing articles published in top-level journals in the areas of political science and public administration; and (2) we examine the development of policy analysis-oriented data analytic models in top-level journals associated with computer science (including both artificial intelligence and information systems). Observations indicate that data science contribution to policy analysis is still an emerging area. Data scientists are moving further than policy analysts, due to technical difficulties in exploiting data analytic models. Integrating artificial intelligence with econometrics is identified as a particularly promising direction.

This chapter analyzes funding patterns and their evolution in two medical research topics: breast cancer and ovarian cancer, taking into account cross-agency and cross-national co-funding. A bibliometric analysis of 355,463 papers from PubMed (273,526 on breast cancer and 81,937 on ovarian cancer) brought out 91 funding agencies involved in breast cancer and 65 in ovarian cancer. Additionally, the study examined the evolution of Medical Subject Headings (MESH) funded by agencies. An analysis of patterns in funding, co-funding, MESH, and their evolution, was carried out using Social Network Analysis (SNA) methodology. The results show the importance of the National Cancer Institute (NCI) in both breast and ovarian cancer. The NCI achieves its policy goals by co-funding its programs with both national and cross-national agencies. Moreover, the MESH that agencies co-funded in the two years studied coincided; however, it must be said that the number of agencies which participated in research funding also increased.

For the period surrounding the 2018 Dutch municipal elections, a team of researchers from the Delft University of Technology investigated the effect of the digital environment on parliamentary democracy. An interdisciplinary group of researchers combined expertise on digital ethics, political theory, big data analytics, the economics of privacy and security, epistemology, media studies and computer science. This report presents the main findings, which are grouped around two main themes: political micro-targeting and ICT media. Societal themes that came to prominence over the research period, such as the debate over ‘fake news’ and the leaks of personal information that were used for political purposes by Facebook, as well as the implementation of new EU privacy regulation helped to put the research in a larger political context. The main findings provide a qualified picture. The influence of the digital revolution on democratic politics is already revolutionary, and the weaknesses of online platforms provide ample opportunities for derailing liberal democracy. Digital platforms are too closed-off, not mindful enough of individual digital rights, and biased in their (re)presentation of political pluralism. But the Netherlands has proven to be one of the few democracies that is relatively resilient, with an open multi-party system receptive to the political fragmentation that ICT developments encourage, and relatively high trust between citizens, in shared media organizations, and between political parties. In order not to be complacent in the face of fundamental challenges, the report provides several urgent recommendations. Next to several ‘reactive’ recommendations, which seek to remedy the weaknesses and dangers the digital environment poses to democracy, it also outlines an example of how the digital environment might be proactively redesigned in order to positively enhance the quality of the Dutch parliamentary system.

The chapter concerns the measurement and forecasting of technological change, a topic relevant to many high-tech organizations and their customers. We revisit recent and classic data sets from technology forecasting data envelopment analysis (TFDEA) research and technometrics in light of a new visualization technique known as t-Distributed Stochastic Neighbor Embedding (t-SNE). The technique is a nonlinear visualization technique for preserving local structure in high-dimensional spaces of data. The technique may be classified as a form of topological data analysis. Specifically, each point in the space represents a potential technological design or implementation, and each line segment in the space represents a local measure of technological improvement or degradation. We hypothesize six distinct kinds of performance development in technology within this space, including the frontier, the fold, the salient, the soliton, and the lock-in. Then we examine the spaces to determine which kinds of development are the best explanations for observed development. The technique is not extrapolative and therefore cannot fully supplant the existing technometric methods. Nonetheless the approach offers a useful diagnostic to the existing technometric methods and may help advance theories of technological development.

A practical how-to guide for more effective planning through multi-actor modelling. Careful planning is the cornerstone of a successful initiative, and any plan, policy, or business strategy can only be successful if it has the support of different actors. These actors may be actively pursuing their own agendas, so the plan must not only offer an optimal solution to the problem, but must also fit the needs and abilities of the actors involved. Actor and Strategy Models: Practical Applications and Step-wise Approaches provides a primer on multi-actor modelling, based on the fundamental premise that actor strategies are explained
by investigating what actors can do, think, and want to achieve. Covering a variety of models with detailed background and case examples, this book focuses on practical application. Step-by-step instructions for each approach provide immediately actionable insight, while a general framework for actor and strategy modelling allows the reader to tailor any approach as needed to optimize results in terms of situation-specific planning. From game theory and argumentative analysis, through social network analysis, cognitive mapping, and beyond, this book provides valuable insight for more effective planning.

The incidence of natural disasters worldwide is increasing. As a result, a growing number of people is in need of humanitarian support, for which limited resources are available. This requires an effective and efficient prioritization of the most vulnerable people in the preparedness phase, and the most affected people in the response phase of humanitarian action. Data-driven models have
the potential to support this prioritization process. However, the applications of these models in a country requires a certain level of data preparedness. To achieve this level of data preparedness on a large scale we need to know how to facilitate, stimulate and coordinate data-sharing between humanitarian actors. We use a data ecosystem perspective to develop success criteria for establishing a “humanitarian data ecosystem”. We first present the development of a general framework with data ecosystem governance success criteria based on a systematic literature review. Subsequently, the applicability of this framework in the humanitarian sector is assessed through a case study on the “Community Risk Assessment and Prioritization toolbox” developed by the Netherlands Red Cross. The empirical evidence led to the adaption the framework to the specific criteria that need to be addressed when aiming to establish a successful humanitarian data ecosystem.

The paper concerns the measurement and forecasting of technological change, a topic relevant to many high-tech organizations and their customers. We revisit recent and classic datasets from technology forecasting data envelopment analysis (TFDEA) research and technometrics in light of a new visualization technique known as t-distributed stochastic neighbor embedding (t-SNE). The technique is a nonlinear visualization technique for preserving local structure in high-dimensional spaces of data. The technique may be classified as a form of topological data analysis. Specifically, each point in the space represents a potential technological design or implementation, and each line segment in the space represents a local measure of technological improvement or degradation. We hypothesize five distinct kinds of performance development in technology within this space including the frontier, the fold, the salient, the soliton, and the lock-in. Then we examine the spaces to determine which kinds of development are the best explanations for the observed development. The technique is not extrapolative, and therefore cannot fully supplant existing technometric methods. Nonetheless, the approach offers a useful diagnostic to existing technometric methods, and may help advance theories of technological development.