The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and ➂ protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

Determining emotions of people during different activities (e.g. surfing the web or walking and driving in urban areas) is of high interest to many industries such as the advertising and marketing industry (Imotions, 2022) or city developers and planners (Zeile et al., 2015). However, as the authors have explained in their previous works (Schneider et al., 2020, Dastageeri et al., 2019, Kohn et al., 2018), it is very difficult to determine emotions using surrogate measurements. This is compounded by the fact that many people have trouble to name their emotions correctly as they are often mixed and hardly ever occur as a single and distinct emotion. In order to improve the attractiveness of cities not just based on general presumptions about how citizens would react to certain changes in the urban environment, but based on physical measurements, previous works have shown approaches to do that (Schneider et al., 2020, Dastageeri et al., 2019). They have developed a first attempt to correlate measured physical parameters such as heart rate and skin conductivity (among others) which are triggered by location and environment to emotional states using machine learning. To correlate locations to emotions is an important aspect for city planners, as a person’s emotion for a location defines the personal relationship to a place which can help to gauge the attractiveness of a place and give indicators about where to improve the city or place.