Routes from left to right: Route 1-Bactar Street Axe, Route 4-Harapcesme Street Axe, Route 5-Haskoy Mektebi Street Axe, Route 7-Merhamet Street Axe.
CITE: Koseoglu, E., 2016, A SENSORY EXPERIENCE IN AN URBAN ENVIRONMENT: SOUND-WALKS AND SOUND- MAPS, ArchDesign'16 / III. International Architectural Design Conference, Ed. Berfu Ayhan, Dakam Publishing, ISBN: 978-605-9207-26-3, 17-18 June 2016, Istanbul.http://www.dakamconferences.org/#!archdesign/s5otu Sensory studies are important to develop a...
... A soundwalk focuses on the acoustic environment and a smellwalk focuses on the olfactory environment. All are methods for onsite data collection that allow researchers to systematically investigate how people experience, understand, and utilise spaces (Henshaw et al., 2009(Henshaw et al., , 2010Bruce et al., 2015;Koseoglu, 2016;McLean, 2017;Xiao et al., 2020). The methodology originates in soundscape studies (Schafer, 1977) and has been broadened out to consider multimodal aspects of environments (Bruce et al., 2015, p. 100;Quercia et al., 2015). ...
The smellscape is the olfactory environment as perceived and understood, consisting of odours and scents from multiple smell sources. To what extent can audiovisual information evoke the smells of a real, complex, and multimodal environment? To investigate smellscape imagination, we compared results from two studies. In the first, onsite participants ( N = 15) made a sensory walk through seven locations of an open-air market. In the second, online participants ( N = 53) made a virtual walk through the same locations reproduced with audio and video recordings. Responses in the form of free-form verbal annotations, ratings with semantic scales, and a ‘smell wheel’, were analysed for environmental quality, smell source type and strength, and hedonic tone. The degree of association between real and imagined smellscapes was measured through canonical correlation analysis. Hedonic tone, as expressed through frequency counts of keywords in free-form annotations was significantly associated, suggesting that smell sources might generally be correctly inferred from audiovisual information, when such imagination is required. On the other hand, onsite ratings of olfactory quality were not significantly associated with online ratings of audiovisual reproductions, when participants were not specifically asked to imagine smells. We discuss findings in the light of cross-modal association, categorisation, and memory recall of smells.
Mapping individuals’ sense of hearing in the urban environment helps urban managers and planners accomplish goals such as creating a favorable urban environment for the citizens. The present study has been conducted to address the lack of modeling and compilation of a sense of hearing potential map in the urban environment and can help urban managers and planners make decisions in this regard. The present study aims for spatial modeling of people's hearing senses and developing potential maps for various hearing states in Tehran, Iran, using a random forest (RF) machine learning algorithm. The four various states, including pleasant sound, annoying sound, normal sound, and stressful sound, have been considered in the present study for the sounds that can be heard in the city. First, a spatial database made up of dependent, and independent data was built. Dependent data included people's four states of hearing in the urban environment, and the respective data was collected through a questionnaire from 657 people. Independent data were categorized into four groups. The first group was traffic related noises including the criteria of traffic volume and equivalent continuous sound level (Leq), the second group included land use related criteria of distance to cemetery, distance to sports areas, distance to commercial areas, distance to primary streets, distance to secondary streets, distance to park, and distance to industrial areas. Furthermore, the public facilities related group includes the distance to airports and distance to public transportation stations, and the population related group includes population density. 70% of the data were used for training, and 30% were set aside for validation. The spatial database was used to develop the potential map for the four states of the hearing sense in the urban environment using the RF algorithm. The potential hearing sense map was evaluated using the receiver operating characteristic (ROC) curve and the respective area under the curve (AUC). The AUC values were 0.930, 0.957, 0.950, and 0.903 for each of the pleasant, annoying, normal, and stressful states, respectively. There was a higher potential for pleasant sounds in the northern districts and some of the central ones, for annoying sounds mainly in the central districts, for normal sounds in central and southern districts, and stressful sounds in some parts of the central and mainly southern districts.