Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Personal data is increasingly used by cities to track the behavior of their inhabitants. While the data is often used to mainly provide information to the authorities, it can also be harnessed for providing information to the citizens in real-time. In an on-going research project on increasing the awareness of motorists w.r.t. the environmental consequences of their driving behavior, we make use of sensors, artificial intelligence, and real-time feedback to design an intervention. A key component for successful deployment of the system is data related to the personal driving behavior of individual motorists. Through this outset, we identify challenges and research questions that relate to the use of personal data in systems, which are designed to increase the quality of life of the inhabitants of the built environment.
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The accelerated densification of Western European cities driven by economic growth has major spatial implications for their overall livability. Often, new homes must be built within an existing urban fabric, creating higher density environments. However, the impact on the experience of these high-density environments at eye level remains unstudied and unknown. This chapter reviews two experiments that sought to understand the unconscious reception of streetscapes using eye-tracking technology to investigate the sequence of users’ (visual) experience, their behavior and perception. The research project seeks to establish more ‘evidence-based’ design guidelines for streetscapes in high-rise urban settings.This chapter reviews two experiments that sought to understand the unconscious reception of streetscapes using eye-tracking technology to investigate the sequence of users’ experience, their behavior and perception. Eye-tracking results of Experiment 1 show that the movement of pedestrians, cyclists and cars crossing the street created the most eye fixation for most participants. In general, the eye-tracking results from Experiment 2 show that participants’ eyes followed the length of the facades toward the end of the street and the horizon. The preliminary results suggest that the assessed design principles ‘Active ground floor’ and ‘Ornate facades’ might be important factors in predicting dominant eye patterns. The chapter explores the application of eye-tracking technology in urban design to gain a deeper understanding of the physical-behavioral interrelationship of streetscapes in European high-density built environments. The accelerated densification of Western European cities driven by economic growth has major spatial implications for their overall livability.
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In the Netherlands approximately 2 million inhabitants have one or more disabilities. However, just like most people they like to travel and go on holiday.In this project we have explored the customer journey of people with disabilities and their families to understand their challenges and solutions (in preparing) to travel. To get an understanding what ‘all-inclusive’ tourism would mean, this included an analysis of information needs and booking behavior; traveling by train, airplane, boat or car; organizing medical care and; the design of hotels and other accommodations. The outcomes were presented to members of ANVR and NBAV to help them design tourism and hospitality experiences or all.
In Amsterdam's neighbourhoods, much of the waste that is disposed has the potential of becoming something else by means of recycling or upcycling. Zero Waste lab –which is part of the organization De Gezonde Stad- is a place where inhabitants can bring their own separated waste in exchange for value coins. Now, Zero Waste Lab now wants to take this a step forward and further develop their own project: from recycling to upcycling waste. In this endeavour, HvA will collaborate by researching the possibilities for upcycling a local waste stream by means of digital production pro-cesses, as well as ways of involving the neighbourhood. Because it is of vital importance for the project not only to be technically possible, but also scalable and economically feasible, Zero Waste Lab and HvA have asked for partnership to the company Verdraaid Goed. This partnership and specific case study, presented here as ‘Wood for the neighborhood’ can be summa-rized in four main goals: • (Production) Explore the design and manufacturing possibilities of using digital production to upcycle a local wood waste stream (with an industrial robotic arm) • (Design) Show how explorative research, when carried on from the beginning of the de-sign process, can bring great added value to the development of project concepts. • (Social) Demonstrate that involving stakeholders early in the process of reusing and de-signing with waste materials can shape the future in new directions • (All three) Highlight how this case study is relevant and fits the principles of the circular economy
In Amsterdam's neighbourhoods, much of the waste that is disposed has the potential of becoming something else by means of recycling or upcycling. Zero Waste lab –which is part of the organization De Gezonde Stad- is a place where inhabitants can bring their own separated waste in exchange for value coins. Now, Zero Waste Lab now wants to take this a step forward and further develop their own project: from recycling to upcycling waste. In this endeavour, HvA will collaborate by researching the possibilities for upcycling a local waste stream by means of digital production pro-cesses, as well as ways of involving the neighbourhood. Because it is of vital importance for the project not only to be technically possible, but also scalable and economically feasible, Zero Waste Lab and HvA have asked for partnership to the company Verdraaid Goed. This partnership and specific case study, presented here as ‘Wood for the neighborhood’ can be summa-rized in four main goals: • (Production) Explore the design and manufacturing possibilities of using digital production to upcycle a local wood waste stream (with an industrial robotic arm) • (Design) Show how explorative research, when carried on from the beginning of the de-sign process, can bring great added value to the development of project concepts. • (Social) Demonstrate that involving stakeholders early in the process of reusing and de-signing with waste materials can shape the future in new directions • (All three) Highlight how this case study is relevant and fits the principles of the circular economy
