Now, that the European cities are overcoming the recent economic challenges, they accelerate the development of major housingschemes to accommodate their growing urban population. Amsterdam for instance, sets out to construct 50,000 new homes by 2025. Parallel to this, the City Council presented a new regeneration and urban optimisationprogram in 2017, to reinforce existingneighbourhoods with relatively weak socio-economic status. If these housing policies are to maximise on opportunities, they need to anticipate the 2030 Agenda for Sustainable Development, the Paris Climate Agreement, and local socio-environmental challenges, into a single cohesive, sustainable solution. Currently, literature indicates that large scale spatial developments, have a tendency to move away from social and ecological ambitions during the course of the planning process. Moreover, ambitions tend to be short term “fixes” where they could be striving for long-term systemic solutions. What is needed, are practice proven comprehensive development strategies tosecure pathways for inclusive and integrated development. Those strategies are spatial and programmatic governance arrangements. Employing a comparative analysis method, we follow and compare the redevelopment of three deprived boroughs across Amsterdam. In collaboration with communities, we are able to construct a “Design Thinking” approach for urban spatial development, using different types of arrangements. This is in reflection and collaboration with the municipality of Amsterdam and a wide variety of skilled experts. The arrangements are tested in practice, following a plan-do-check-act cycle. The research project takes an in-depth look at the Amsterdam case and presents the first set of arrangements for planning more cohesive, urban spatial development and the preliminary strategies we see emerging.
The design of a spatial distribution structure is of strategic importance for companies, to meet required customer service levels and to keep logistics costs as low as possible. Spatial distribution structure decisions concern distribution channel layout – i.e. the spatial layout of the transport and storage system – as well as distribution centre location(s). This paper examines the importance of seven main factors and 33 sub-factors that determine these decisions. The Best-Worst Method (BWM) was used to identify the factor weights, with pairwise comparison data being collected through a survey. The results indicate that the main factor is logistics costs. Logistics experts and decision makers respectively identify customer demand and service level as second most important factor. Important sub-factors are demand volatility, delivery time and perishability. This is the first study that quantifies the weights of the factors behind spatial distribution structure decisions. The factors and weights facilitate managerial decision-making with regard to spatial distribution structures for companies that ship a broad range of products with different characteristics. Public policy-makers can use the results to support the development of land use plans that provide facilities and services for a mix of industries.
This paper explores the impact of the physical and social dimensions of the work environment on satisfaction and perceived productivity of knowledge workers in Dutch universities of applied sciences. The approach took the form of a literature review, multiple case study of six research centres using interviews and logbook analysis, and web-based survey (N = 188). Optimally facilitating knowledge production requires both space for concentration (to support internalisation of knowledge) and space for interaction (to support externalisation of knowledge). None of the work environments involved in the study adequately supported all the phases of knowledge development adequately. Cellular offices with personal desks are preferred for solo work and, whereas new workplace designs with a focus on the office as a meeting place support interaction and collaboration. Spatial layout and interaction have a stronger impact than comfort and absence of distraction. The spatial layout should support both in-depth concentration and communication, fit the internalisation/externalisation ratio of activities, and accommodate the proximity essential for collaborative knowledge development. Being able to choose is the key to success. In terms of research limitations, knowledge workers’ productivity was measured by self-assessment, but only a limited number of diaries were collected. The lessons learned can be used as inputs to decision-making processes regarding the design, implementation and management of working environments in higher education settings. Few studies have been conducted concerning the spatial preferences and needs of knowledge workers in universities of applied sciences. The results show that the physical dimension (comfort and layout) is more important for collective productivity, whereas individual productivity is more strongly influenced by the social dimension (interaction and distraction).
MULTIFILE
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
The livability of the cities and attractiveness of our environment can be improved by smarter choices for mobility products and travel modes. A change from current car-dependent lifestyles towards the use of healthier and less polluted transport modes, such as cycling, is needed. With awareness campaigns, cycling facilities and cycle infrastructure, the use of the bicycle will be stimulated. But which campaigns are effective? Can we stimulate cycling by adding cycling facilities along the cycle path? How can we design the best cycle infrastructure for a region? And what impact does good cycle infrastructure have on the increase of cycling?To find answers for these questions and come up with a future approach to stimulate bicycle use, BUas is participating in the InterReg V NWE-project CHIPS; Cycle Highways Innovation for smarter People transport and Spatial planning. Together with the city of Tilburg and other partners from The Netherlands, Belgium, Germany and United Kingdom we explore and demonstrate infrastructural improvements and tackle crucial elements related to engaging users and successful promotion of cycle highways. BUas is responsible for the monitoring and evaluation of the project. To measure the impact and effectiveness of cycle highway innovations we use Cyclespex and Cycleprint.With Cyclespex a virtual living lab is created which we will use to test several readability and wayfinding measures for cycle infrastructure. Cyclespex gives us the opportunity to test different scenario’s in virtual reality that will help us to make decisions about the final solution that will be realized on the cycle highway. Cycleprint will be used to develop a monitoring dashboard where municipalities of cities can easily monitor and evaluate the local bicycle use.
The IMPULS-2020 project DIGIREAL (BUas, 2021) aims to significantly strengthen BUAS’ Research and Development (R&D) on Digital Realities for the benefit of innovation in our sectoral industries. The project will furthermore help BUas to position itself in the emerging innovation ecosystems on Human Interaction, AI and Interactive Technologies. The pandemic has had a tremendous negative impact on BUas industrial sectors of research: Tourism, Leisure and Events, Hospitality and Facility, Built Environment and Logistics. Our partner industries are in great need of innovative responses to the crises. Data, AI combined with Interactive and Immersive Technologies (Games, VR/AR) can provide a partial solution, in line with the key-enabling technologies of the Smart Industry agenda. DIGIREAL builds upon our well-established expertise and capacity in entertainment and serious games and digital media (VR/AR). It furthermore strengthens our initial plans to venture into Data and Applied AI. Digital Realities offer great opportunities for sectoral industry research and innovation, such as experience measurement in Leisure and Hospitality, data-driven decision-making for (sustainable) tourism, geo-data simulations for Logistics and Digital Twins for Spatial Planning. Although BUas already has successful R&D projects in these areas, the synergy can and should significantly be improved. We propose a coherent one-year Impuls funded package to develop (in 2021): 1. A multi-year R&D program on Digital Realities, that leads to, 2. Strategic R&D proposals, in particular a SPRONG/sleuteltechnologie proposal; 3. Partnerships in the regional and national innovation ecosystem, in particular Mind Labs and Data Development Lab (DDL); 4. A shared Digital Realities Lab infrastructure, in particular hardware/software/peopleware for Augmented and Mixed Reality; 5. Leadership, support and operational capacity to achieve and support the above. The proposal presents a work program and management structure, with external partners in an advisory role.
