Western cities are rapidly densifying, and new building typologies are beinginvented to mitigate high-rise and balance residential, commercial andrecreational functions. This vertical urbanization requires rethinking thetraditional design of public space to promote citizens’ well-being. While the scarce studies on high-rise environments indicate several risks, including social fragmentation and privatization of public functions (Henderson-Wilson 2008; Love et al., 2014), mental stress and undermining attention restoration (Mazumder et al., 2020; Lindal & Hartig 2013), evidence on the potential salutary and mitigating effects of architectural design qualities is limited (Suurenbroek & Spanjar 2023).The Building for Well-being research project combines biometric and socialdata-collection techniques to address this gap. It builds on studies investigatinghow built environments allow user engagement (Mallgrave 2013; Simpson2018) and afford important activities (Gibson 1966). This case study focuseson the experiences of predominant users of the NDSM Wharf in Amsterdamas it is transformed from a post-industrial site into a high-density, mixeduseneighborhood. Using eye-tracking, field and laboratory-based surveys, itexplores how residents, passers-by and visitors visually experience, appreciateand perceive the restorative value of the wharf’s recently developed urbanspaces.Thirty-six university students were randomly recruited as test subjects for thelaboratory test and assigned to one of the three user groups. The residentand passer-by groups were primed for familiarity. Each group was assigneda distinct walking mode and participants were told to imagine they werestrolling (residents), rushing (passers-by) or exploring (visitors). The exposuretime to visual stimuli of participants was five seconds per image. Afterwards,they reported on the perceived restorative quality of ten urban spaces,focusing on: (1) sense of being away, (2) level of complexity-compatibilityand (3) fascination, based on an adapted Restorative Components Scale (RCS,Yin et al. 2022; Laumann et al. 2001). Self-reported appreciation per scenewas measured on a 10-point Likert scale and subjects indicated elements inthe ten urban spaces they liked or disliked (see Figure 1). A semi-structuredon-site survey was also carried out to investigate user experiences furtherand for triangulation. Thirty-one users, consisting of residents, passers-byand visitors to the NDSM Wharf, rated their appreciation of the site and itsperceived restorative and design qualities (following Ewing & Clemente, 2013)on a 10-point Likert scale.The meta-data analysis of RCS statistics, appreciation values, eye-trackingmetrics and heatmaps reveals distinct visual patterns among user groups. Thispoints to the influence of environmental tasks and roles (see Figure 2). Strollingand exploring resulted in a comprehensive visual exploration of scenes with ahigher mean total fixation count and shorter mean total fixation duration thangoal-oriented walking. It suggests that walking mode determines the level ofopenness to the environment and that architectural attributes can also steervisual exploration. Scenes with the highest appreciation scores correlatedwith the RCS outcomes. They displayed coherence and opportunities forsocial engagement, contrasting with scenes with inconsistent industrial andcontemporary features. These findings provide spatial designers with insightsinto the subliminal experiences of predominant user groups to promote wellbeing in urban transformation.
Active transport to school is associated with higher levels of physical activity in children. Promotion of active transport has therefore gained attention as a potential target to increase children’s physical activity levels. Recent studies have recognized that the distance between home and school is an important predictor for active travel among children. These studies did not yet use the promising global positioning system (GPS) methods to objectively assess active transport. This study aims to explore active transport to school in relation to the distance between home and school among a sample of Dutch elementary school children, using GPS. Seventy-nine children, aged 6-11 years, were recruited in six schools that were located in five cities in the Netherlands. All children were asked to wear a GPS receiver for one week. All measurements were conducted between December 2008 and April 2009. Based on GPS recordings, the distance of the trips between home and school were calculated. In addition, the mode of transport (i.e., walking, cycling, motorized transport) was determined using the average and maximum speed of the GPS tracks. Then, proportion of walking and cycling trips to school was determined in relation to the distance between home and school. Out of all school trips that were recorded (n = 812), 79.2% were classified as active transport. On average, active commuting trips were of a distance of 422 meters with an average speed of 5.2 km/hour. The proportion of walking trips declined significantly at increased school trip distance, whereas the proportion of cycling trips (β = 1.23, p < 0.01) and motorized transport (β = 3.61, p < 0.01) increased. Almost all GPS tracks less than 300 meters were actively commuted, while of the tracks above 900 meters, more than half was passively commuted. In the current research setting, active transport between home and school was the most frequently used mode of travel. Increasing distance seems to be associated with higher levels of passive transport. These results are relevant for those involved in decisions on where to site schools and residences, as it may affect healthy behavior among children. https://doi.org/10.1186/1471-2458-14-227 LinkedIn: https://www.linkedin.com/in/sanned/
MULTIFILE
Car use in the sprawled urban region of Noord‐Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High‐Tech Campus (2018) and the TU/e‐campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car‐dependent. Of the non‐car‐dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provi-sion) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.
MULTIFILE
