Climate adaptation, mitigation, and protecting strategies are becoming even more important as climate change is intensifying. The impacts of climate change are especially tangible in dense urban areas due to the inherent characteristics of urban structure and materiality. To assess impacts of densification on urban climate and potential adaptation strategies a densely populated Viennese district was modeled as a typical sample area for the city of Vienna. The case study analyzed the large-scale densification potential and its potential effects on microclimate, air flow, comfort, and energy demand by developing 3D models of the area showing the base case and densification scenarios. Three methods were deployed to assess the impact of urban densification: Micro-climate analysis (1) explored urban heat island phenomena, wind pattern analysis (2) investigated ventilation and wind comfort at street level, and energy and indoor climate comfort analysis (3) compared construction types and greening scenarios and analyzed their impact on the energy demand and indoor temperatures. Densification has negative impacts on urban microclimates because of reducing wind speeds and thus weakening ventilation of street canyons, as well as accelerating heat island effects and associated impact on the buildings. However, densification also has daytime cooling effects because of larger shaded areas. On buildings, densification may have negative effects especially in the new upper, sun-exposed floors. Construction material has less impact than glazing area and rooftop greening. Regarding adaptation to climate change, the impacts of street greening, green facades, and green roofs were simulated: The 24-h average mean radiant temperature (MRT) at street level can be reduced by up to 15 K during daytime. At night there is only a slight reduction by a few tenths of 1 K MRT. Green facades have a similar effect on MRT reduction, while green roofs show only a slight reduction by a few tenths of 1 K MRT on street level. The results show that if appropriate measures were applied, negative effects of densification could be reduced, and positive effects could be achieved.

Urbanization has amplified the importance of three‐dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, andhow), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine‐grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions.

Global increase of urban population has brought about a growing demand for more dwelling space, resulting in various negative impacts, such as accelerated urbanization, urban sprawl and higher carbon footprints. To cope with these growth dynamics, city authorities are urged to consider alternative planning strategies aiming at mitigating the negative implications of urbanization. In this context, the present contribution investigates the potential of urban densification to mitigate the heat island effects and to improve outdoor thermal conditions. Focusing on a quite densely urbanized district in Vienna, Austria, we carried out a set of simulations of urban microclimate for pre- and post-densification scenarios using the parametric modelling environment Rhinoceros 3D and a set of built-in algorithms in the Rhino’s plug-in Grasshopper. The study was conducted for a hot summer period. The results revealed a notable solar shielding effect of newly introduced vertical extensions of existing buildings, promoting temperature decrease and improved thermal conditions within more shaded urban canyons and courtyards. However, a slight warming effect was noted during the night-time due to the higher thermal storage and lower sky view factor.

Recent challenges in the realm of urban studies concern better understanding of microclimatic conditions. Changes in urban climate affect cities at local and global scales, with consequences for human health, thermal comfort, building energy use, and anthropogenic emissions. The extent of these impacts may vary due to different morphologies and materials of the built environment. The present contribution summarizes the results of a multi-year effort concerned with the extent and implications of urban heat in Vienna, Austria. For this purpose, high-resolution weather data across six locations are obtained and analyzed. This allowed for an objective assessment of urban-level climatic circumstances across distinct low-density and high-density typologies. Subsequently, a systematic framework was developed for identification of essential properties of the built environment (geometric and material-related) that are hypothesized to influence microclimate variation. Results point to a number of related (positive and negative) correlations with microclimatic tendencies. Additionally, the impact of this location-specific weather data on building performance simulation results is evaluated. The results suggest that buildings' thermal performance is significantly influenced by location-specific microclimatic conditions with variation of mean annual heating load across locations of up to 16.1 kWhm⁻²·a⁻¹. The use of location-independent weather data sources (e.g., standardized weather files) for building performance estimations can, thus, result in considerable errors.