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Regular physical activity is essential for the healthy development of children, and sports clubs are one of the main drivers of regular exercise. Previous studies have demonstrated that public subsidies can increase participation rates in sports clubs. The effectiveness of funding in increasing participation rates depends on multiple factors, such as geographic location, the size of the sports club, and the socio-economic conditions of the population. Here, we show how an optimal allocation of government funds to sports facilitators (e.g., sports clubs) can be achieved using a data-driven simulation model that maximizes children’s access to sports facilities. We compile a dataset for all 1,854 football clubs in Austria, including estimates for their budgets, geolocations, tallies, and the age profiles of their members. We find a characteristic sublinear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the club’s municipality. In the model, where we assume this relationship to be causal, we evaluate different funding strategies. We show that an optimization strategy, where funds are distributed based on regional socio-economic characteristics and club budgets, outperforms a naive approach by up to 117% in attracting children to sports clubs with 5 million euros of additional funding. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.

Crime is an ubiquitous part of society. The way people express their concerns about crimes has been of particular interest to the scientific community. Over time, the numbers and kinds of available communication channels have increased. Today, social media services, such Twitter, present a convenient way to express opinions and concerns about crimes. The main objective of this study is to explore people's perception of homicides, specifically, how the characteristics and proximity of the event affect the public's concern about it. The analysis explores Twitter messages that refer to homicides that occurred in London in 2012. In particular, the dependence of tweeting propensity on the proximity, in space and time, of a crime incident and of people being concerned about that particular incident are examined. Furthermore, the crime characteristics of the homicides are analysed using logistic regression analysis. The results show that the proximity of the Twitter users' estimated home locations to the homicides' locations impacts on whether the associated crime news is spread or not and how quickly. More than half of the homicide related tweets are sent within the first week and the majority of them are sent within a month of the incident's occurrence. Certain crime characteristics, including the presence of a knife, a young victim, a British victim, or a homicide committed by a gang are predictors of the crime-tweets posting frequency.

This literature review is dedicated to the subject of agent-based modelling for the system of international migration, and of the modelling of policies that are known to aid in its management. The reason for the selection of agent-based modelling as a framework for studying international migration is that the system of international migration presents the characteristics of a complex system: notably, its property of emergence, which therefore imposes the usage of a methodology for its modelling that is capable of reflecting its emergent traits. The policies that we study are those that intervene in the country of origin of emigrants and that are aimed at decreasing the aggregate volume of emigrants from that country. The reason for this choice is that policies in the countries of origin have become particularly attractive today, especially in European countries, under the assumption that it should be possible to prevent the migrants from reaching the point of destination of their journey if some kind of action is undertaken before the migrants arrive. We start by discussing the theoretical constraints that suggest how this approach may only partially be valid. Then, to assist the development of future agent-based models that study migration, we identify via topic mining the ten topics that are most commonly discussed in the literature on the application to the international migration of agent-based models; this lets us highlight the characteristics of an agent-based model that should be included when the research task relates to the usage of ABM to study international migration and its associated policies. Finally, we indicate why the existing literature on the modelling of international migration is missing a key aspect that is required to correctly model policies: the integration between agent-based approaches and systems dynamics.

In this paper, we study the problem of agenda setting by news media in relation to the political discourse by politicians at the time of local elections. We first evaluate the applicability of the agenda-setting theory against the theory of policy agenda building to determine the possible alternative directions for constructing a political agenda at the time of elections. Namely, we identify a non-linear interaction between news organizations, politicians, and the general public during the electoral campaign. This interaction, in turn, shapes the dynamic evolution of the public discourse concerning politics, and it is characterized by high sensitivity to initial conditions and non-linearity. Then, we attempt to identify the presence of an evolutionary trajectory of the political discourse in Lower Austria at the time of elections by observing whether, as the time of an election approaches, the interaction between news organizations and politicians flattens and becomes more linear without the news or the politicians causing the agenda of the other to be set accordingly. Finally, we provide a new methodology for identifying the topics contained in such an agenda so that empirical verification of the proposed hypothesis becomes possible.

The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.

Grand societal issues such as climate change and technological disruption challenge all industry sectors, including tourism. To cope with these challenges, new sustainable business models that not only rely on data-driven technologies but also require new ways of collaboration beyond disciplines and sectors by facilitating the overall conception of transdisciplinarity are essential. One potential way to combine all these requirements is computational social sciences. As a discipline-crossing approach, it should be anchored within tourism education to train the future workforce and experts necessary to realize the needed transformation. Thus, this study explores the status quo of tourism curricula in higher educational institutions in Austria through the lens of computational social sciences. In doing so, a set of core modules of computational social sciences content was developed as an analytical framework. The results show that there is still a significant gap between the demands of the tourism industry and the offered educational programs in Austria. The article concludes with insights on how to close the existing gap and some suggestions for possible foundational steps to support the transformation.

The concepts of “Smart Cities” and “Smart Manufacturing” are different data-driven domains, although both rely on intelligent information technology and data analysis. With the application of linked data and affordance-based approaches, both domains converge, paving the way for new and innovative viewpoints regarding the comparison of urban tasks with indoor manufacturing tasks. The present study builds on the work, who state that cities are scaled versions of each other, by extending this thesis towards indoor manufacturing environments. Based on their structure and complexity, these environments are considered to form ecosystems of their own, comparable to “small cities”. This conceptual idea is demonstrated by examining the process of human problem-solving in transportation situations from both perspectives (i.e., city-level and manufacturing-level). In particular, the authors model tasks of human operators that are used to support transportation processes in indoor manufacturing environments based on affordances and spatial-temporal data. This paper introduces the fundamentals of the transformation process of outdoor tasks and process planning activities to indoor environments, particularly to semiconductor manufacturing environments. The idea is to examine the mapping of outdoor tasks and applications to indoor environments, and vice-versa, based on an example focusing on the autonomous transportation of production assets in a manufacturing environment. The approach is based on a spatial graph database, populated with an indoor navigation ontology and instances of indoor and outdoor objects. The results indicate that human problem-solving strategies can be applied to indoor manufacturing environments to support decision-making in autonomous transportation tasks.

Sustainability is gaining importance in society, government, and the economy, particularly during today’s rapidly changing environment, due to digitalization and digital transformation. Awareness, as well as systematic and critical thinking, are crucial to address the great societal challenges postulated within the SDGs, and thus should be reflected in contemporary education. Consequently, higher educational institutions face a high level of responsibility to prepare their students properly. Postgraduate programmes for professional training, in particular, have great potential, as the in-depth work experience of students can be leveraged to engage with them as co-leaders towards sustainable solutions in the digital age, from a transdisciplinary perspective. Thus, this paper introduces a teaching framework for digital sustainability in higher education under the light of transdisciplinarity. The framework and its inherent methods are discussed, followed by an exploratory analysis, covering the experiences of over 100 students over the course of two years in a postgraduate master’s programme. We present the results of the students’ learning and ideation process towards digital products/services to tackle challenges within the SDGs. In addition, we provide a critical reflection of prerequisites for teaching the framework, challenges experienced during teaching, and potential solutions, as well as ideas towards the future expansion of the framework.

Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings into “Residential/Small Buildings”, “Apartment Buildings”, and “Industrial and Factory Building” classes by means of domain ontology and machine learning techniques. The buildings objects are classified using exclusively the information computed from the ALS data. To select the relevant features for predicting the classes of interest, the Random Forest classifier has been applied. The ontology-based classification yielded convincing results for the “Residential/Small Buildings” class (F-Measure 97.7%), whereas the “Apartment Buildings” and “Industrial and Factory Buildings” classes achieved less accurate results (F-Measure 60% and 51%, respectively).

The percentage of elderly people in European countries is increasing. Such conjuncture affects socio-economic structures and creates demands for resourceful solutions, such as Ambient Assisted Living (AAL), which is a possible methodology to foster health care for elderly people. In this context, sensor-based devices play a leading role in surveying, e.g., health conditions of elderly people, to alert care personnel in case of an incident. However, the adoption of such devices strongly depends on the comfort of wearing the devices. In most cases, the bottleneck is the battery lifetime, which impacts the effectiveness of the system. In this paper we propose an approach to reduce the energy consumption of sensors’ by use of local sensors’ intelligence. By increasing the intelligence of the sensor node, a substantial decrease in the necessary communication payload can be achieved. The results show a significant potential to preserve energy and decrease the actual size of the sensor device units.