As urbanization and climate change intensify, managing the urban microclimate becomes increasingly challenging, affecting outdoor thermal comfort. The practical integration of urban microclimate research into urban design remains limited, in part due to the complexity and inaccessibility of existing tools. To support early-stage, climate-sensitive urban design at the neighbourhood scale, I present SOLWEIG For Design (SOLFD): a computation framework that builds on the existing SOLWEIG tool. SOLFD enables urban designers to visualize current microclimatic conditions and assess the impact of design interventions on outdoor thermal comfort. In particular, it focuses specifically on (in)direct solar radiation and its effect as quantified by the mean radiant temperature. Key contributions include: (1) extending SOLWEIG’s 2.5D model to a layered 3D representation for improved accuracy in complex urban geometries; (2) automating the data pipeline using open Dutch geospatial datasets; (3) enabling the modification of the existing urban scene; (4) enhancing output usability through temporally grouped mean radiant temperature maps, derived physiological equivalent temperature maps, and comparison statistics; and (5) significantly reducing simulation time with GPU acceleration. The accuracy of SOLFD was validated using sensor data, achieving an RMSE of 5.39°C. Underneath structures, the RMSE increases to 5.83 °C. The potential of SOLFD is further demonstrated with a case study across various Dutch urban typologies. By laying the foundation for an accessible decision-support tool for outdoor thermal comfort, SOLFD takes a step toward integrating climate-responsive strategies into the urban design process.

By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.

To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.

The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.

In Nederland en in de stad Delft wordt geluidhinder door autowegen ervaren door bewoners. Dit kan leiden tot een verminderde gezondheid. Het doel van dit onderzoek is uitvinden waar de geluidsnorm wordt overschreden en hoe dit verminderd zou kunnen worden. Hiervoor is de volgende onderzoeksvraag opgesteld: Waar in Delft wordt de geluidsnorm voor verkeersgeluid overschreden, welke fysische factoren van de wegen hebben effect op de geluidsoverschrijding en welke maatregelen zouden genomen kunnen worden? Om deze vraag te beantwoorden is een literatuurstudie gedaan en een geodata-analyse uitgevoerd. De verkregen geodata is omgezet in kaarten, vervolgens zijn hier ruimtelijke analyses op uitgevoerd. De conclusie is dat het overgrote deel van de wegen een te hoge geluidsbelasting heeft. De maximale snelheid of het wegdek zou hiervoor kunnen worden aangepast.