The current architectural design bases its project configuration on functional aspects typical of the type of architecture to be built, and on formal aspects typical of a design idealized by the designer architect. The differentiating concept of this type of architectural projection is emotional design, which sets the tone for design centered on the user and their subjective and collective conception, which will guarantee a structuring of the proposal that integrates the architectural design with the sensations experienced by the user when faced with certain elements or situations converging in the living space. The kansei methodology is one of the pioneering and most complete methodologies in the field of emotional design. As (Nagamachi, 1995) indicates, this engineering consists of capturing users’ expressions and translating them into correct design elements. The term Kansei is a Japanese word, whose meaning is related to the words “sensation”, “emotion” or “feeling”. It is used to denote the qualities that an object has to transmit pleasant emotions when using it. Therefore, Kansei is the impression or stimulus that a person may have when faced with a certain product, environment, or situation, when they use their senses. From this, it can be said that Kansei Engineering (KE) is a tool that allows us to capture the emotional needs of users and establish mathematical prediction models to relate the characteristics of products with these emotional needs. “In this regard, Kansei Design (KD) is a novel holistic approach to users’ perceptions at cognitive level which seems appropriate to applications in architectural design.” (Carateli & Misuri, 2020, p.1)
The emergence of these sensory and emotional responses are intertwined with their sociocultural context, from which precisely this value of identity is created between people, societies and, why not, designs. These cultural identity implications play a key role in the spatial and formal conception, since they generate symbolic representations that modify their perception, which is, in turn, the presupposition of these representations. The conceptualization of the problem sets a clearly defined pattern, as a consequence of a superficial assessment of thoughts based on preceding ideologies, where the systematic approach to design development has as its genesis the experiences and situations of daily life, not from advanced visuals impressions. All of this configures a thought where the intrinsic senses of the human being provide a nuance of conceptual projection referred to the creation of architectural spaces. In this context (Fuentes, 2018) points out that the qualitative concepts of people mark a correlation between the experiences lived and perceived in the city, with conceptions of a formal nature, such as defining architectural forms as “heavy” or “light”, etc. And where “the criticism of key concepts that such as identity urban imaginaries, refer to the point of view of the city’s actors, as an indispensable factor to understand the “complex nature of design” (Fuentes, 2018, p . 1). At this point is where architects are called to establish a methodology as a basis for their design that synthesizes their own life experience as an urban-social actor, in order to interpret and understand the different points of view of others. In this way, an architecture is configured that applies values of cultural identity, as a conception of the urban imaginaries of its inhabitants, in order to establish multisensory designs adaptable to its inhabitants, and thus generate a progressive appropriation of the built space, as Fuentes points out.
Applying Kansei Engineering to urban imaginaries could involve understanding how people emotionally respond to different aspects of urban spaces. For example, it could be used to understand how people feel about public transportation, parks, buildings, and other elements of the urban environment. This information could then be used to design urban spaces that better meet the needs and desires of their inhabitants. Although this methodology gives us a clear vision and abstraction of the user’s perceptions, these results are still theoretical and linguistic, and “cannot directly generate products, which is a fatal defect of product form design” (Quan et. Al, 2018). Therefore, the use of a methodology that allows the user’s responses to be transferred to visible and utilitarian elements is incredibly necessary. Deep learning could further enhance this process by providing tools for analyzing large amounts of data. For instance, as (James, 2011) propose and explain the possibility of capturing patterns of individual perception, interaction and sensation in a determinate space environment, accessing through data that are publicly accessible via application programming interfaces like (APIs), mine text, or web scrapping techniques. This data sets can be retrieved from social media posts, ‘meta-geosocial’ services like MyCityWay4 and Localicious in order to gain insights into how different people perceive and experience urban spaces, James explained. Deep learning have being used in different fields, one of them in the recognition and detection of features and patterns within an image and style migration. About the last one, there is an approach that “enables arbitrary style transfer in real-time” (Huang et. Al, 2017), adaptive instance normalization, adjusting the mean and variance of the features of a content image to match those of a style image.
The main idea of this paper is to collect different linguistic expressions that symbolize and represent the sensations and perceptions that people experience within the city, expressions that denote subjective and collective thought such as urban imaginaries. This information can be obtained from geosocial data which “consist of point locations that have been created and tagged by participants with short statements about their perceptions and/or experiences” (James, 2011, p. 1), using different techniques such as web scraping, etc. Then with the use of the text mining technique you can “obtain information, text analysis, information extraction, categorization, clustering (…)” (Dang & Ahmad, 2014, p. 1). This information will make up the necessary elements for the formation of the kansei methodology, which as Quan explains are: The product domain, the semantic space, and the property space. Then, through this kansei methodology, the co-relations of the semantic expressions and properties, in this case architectural and urban, will be established. Finally, with these relationships, it will be possible to generate precise images that symbolize the expressions of the users and thus have a database that will serve to feed Deep learning. Using the methodology proposed by Quan, et. al for the generation of a “BP mapping model between product properties and semantics. Through the BP model, we obtain the semantics of the selected content image and use it to guide the selection of the style image” (Quan, et. al, 2018, p.2)
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