Airports have undergone a significant digital evolution over the past decades, enhancing efficiency, effectiveness, and user-friendliness through various technological advancements. Initially, airports deployed basic IT solutions as support tools, but with the increasing integration of digital systems, understanding the detailed digital ecosystem behind airports has become crucial. This research aims to classify technological maturity in airports, using the access control process as an example to demonstrate the benefits of the proposed taxonomy. The study highlights the current digital ecosystem and its future trends and challenges, emphasizing the importance of distinguishing between different levels of technological maturity. The role of biometric technology in security access control is examined, highlighting the importance of proper identification and classification. Future research could explore data collection, privacy, and cybersecurity impacts, particularly regarding biometric technologies in Smart Access Level 4.0. The transition from Smart Access Level 3.0 to 4.0 involves process automation and the introduction of AI, offering opportunities to increase efficiency and improve detection capabilities through advanced data analytics. The study underscores the need for global legislative frameworks to regulate and support these technological advancements.
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The coronavirus pandemic highlighted the vital role urban areas play in supporting citizens’ health and well-being (Ribeiro et al., 2021). In times of (personal) vulnerability, citizens depend on their neighbourhood for performing daily physical activities to restore their mental state, but public spaces currently fall short in fulfilling the appropriate requirements to achieve this. The situation is exacerbated by Western ambitions to densify through high-rise developments to meet the housing demand. In this process of urban densification, public spaces are the carriers where global trends, local ambitions and the conditions for the social fabric materialise (Battisto & Wilhelm, 2020). High-rise developments in particular will determine users’ experiences at street-level. Consequently, they have an enduring influence on the liveability of neighbourhoods for the coming decades but, regarding the application of urban design principles, their impact is hard to dissect (Gifford, 2007).Promising emerging technologies and methods from the new transdisciplinary field of neuroarchitecture may help identify and monitor the impact of certain physical characteristics on human well-being in an evidence-based way. In the two-year Sensing Streetscapes research study, biometric tools were tested in triangulation with traditional methods of surveys and expert panels. The study unearthed situational evidence of the relationship between designed and perceived spaces by investigating the visual properties and experience of high-density environments in six major Western cities. Biometric technologies—Eye-Tracking, Galvanic Skin Response, mouse movement software and sound recording—were applied in a series of four laboratory tests (see Spanjar & Suurenbroek, 2020) and one outdoor test (see Hollander et al., 2021). The main aim was to measure the effects of applied design principles on users’ experiences, arousal levels and appreciation.Unintentionally, the research study implied the creation of a 360° built-environment assessment tool. The assessment tool enables researchers and planners to analyse (high-density) urban developments and, in particular, the architectural attributes that (subliminally) affect users’ experience, influencing their behaviour and perception of place. The tool opens new opportunities for research and planning practice to deconstruct the successes of existing high-density developments and apply the lessons learned for a more advanced, evidence-based promotion of human health and well-being.ReferencesBattisto, D., & Wilhelm, J. J. (Eds.). (2020). Architecture and Health Guiding Principles for Practice. Routledge, Taylor & Francis Group. Gifford, R. (2007). The Consequences of Living in High-Rise Buildings. Architectural Science Review, 50(1), 2–17. https://doi.org/https://doi.org/10.3763/asre.2007.5002 Hollander, J. B., Spanjar, G., Sussman, A., Suurenbroek, F., & Wang, M. (2021). Programming for the subliminal brain: biometric tools reveal architecture’s biological impact. In K. Menezes, P. de Oliveira-Smith, & A. V. Woodworth (Eds.), Programming for Health and Wellbeing in Architecture (pp. 136–149). Routledge, Taylor & Francis Group. https://doi.org/https://doi.org/10.4324/9781003164418 Ribeiro, A. I., Triguero-Mas, M., Jardim Santos, C., Gómez-Nieto, A., Cole, H., Anguelovski, I., Silva, F. M., & Baró, F. (2021). Exposure to nature and mental health outcomes during COVID-19 lockdown. A comparison between Portugal and Spain. Environment International, 154, 106664. https://doi.org/https://doi.org/10.1016/j.envint.2021.106664 Spanjar, G., & Suurenbroek, F. (2020). Eye-Tracking the City: Matching the Design of Streetscapes in High-Rise Environments with Users’ Visual Experiences. Journal of Digital Landscape Architecture (JoDLA), 5(2020), 374–385. https://gispoint.de/gisopen-paper/6344-eye-tracking-the-city-matching-the-design-of-streetscapes-in-high-rise-environments-with-users-visual-experiences.html?IDjournalTitle=6
MULTIFILE
Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency & explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency & explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling.Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.
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The increasing concentration of people in urban environments in an era of globalisation means that social, economic, and environmental resources for living and working are under pressure. Urban communities experience increased stress levels due to inadequate and overburdened infrastructure and services, challenges due to ethnic and cultural diversity, socio-economic inequalities as well as the impact of environmental degradation. For these communities to build resilience under these circumstances therefore requires a multipronged approach. The underlying question this project will answer is: “What are the key characteristics of experiencescapes that contribute to resilience-building in communities?” The project will dive into the identification of building blocks of experiencescapes and roles of relevant actors that can support communities in building resilience. Within the context of a multidisciplinary approach, this project applies a range of qualitative research methods, such as in-depth interviews, focus groups, participant observation, storytelling techniques, life stories, as well as various biometric quantitative methods, available through the experience lab of BUas. The outcome of the project will enable practitioners and researchers alike in various sectors to understand what and how they can contribute to creating an environment in which people can meaningfully interact in a way that builds resilience in communities. This outcome is communicated not only through academic publications and conference contributions, but also through public reports and a handbook for practitioners and students. These reports and handbooks support identification and application of building blocks of experiencescapes that support building resilience in communities. Finally, the knowledge generated in the project will contribute to the development of curricula of various educational programmes at Breda University of Applied Sciences by expanding the scope of experience design into the area of people-to-people relationships.
