This thesis introduces the “city stack” framework and the concept of the “typology grid” to analyze and procedurally generate cities using publicly available geospatial data. It builds upon existing urban morphology research and combines existing metrics with new metrics to examine road networks and building footprints across 43 cities worldwide. The resulting unsupervised clustering of road patterns produced mixed outcomes: some global road typologies were consistently identified, while others lacked clarity or validity. Supervised classification of building typologies showed potential for city generation applications but faced challenges related to data quality and validation methods.
To encode the captured urban form, the typology grid was introduced, which allows for straightforward comparison between cities and links the analysis phase with the generation phase. While this approach simplifies complex urban patterns for better understanding, it may oversimplify details needed for effective city regeneration, needing future research into parameterizing the grid.
The simulated annealing optimization technique was applied to generate new city models to produce typology grids resembling those of actual cities. It proved a promising method, with relatively plausible results from just a few shape-based rules in the objective function. Computation time and grid size were a limiting factor, ruling out real-time use. The road and building generation methods based on the typology grid and city stack framework demonstrated the approach’s feasibility but indicated that further refinement is necessary.
Further contributions by this thesis are an open source command line tool for analyzing the urban form real-life cities based on publicly available geospatial data, a proof of concept tool for generating cities, and a publicly available dataset of the analyzed cities.
In conclusion, the city stack framework and typology grids offer a viable method for captur- ing and generating urban form and can be used as a starting point for future research.

While three-dimensional coral reef models are valuable for various applications, existing approaches like photogrammetric scanning and manual modeling require substantial time and expertise, limiting their scalability. Previous algorithmic approaches, particularly Agent-Based Models (ABM), have relied heavily on complex ecological simulations and deep domain knowledge. This thesis explores an alternative data-driven approach to automated coral reef modeling, investigating whether empirical data sources can provide a scalable method for generating ecologically plausible 3D models. Rather than simulating ecological processes from first principles, we develop a pipeline that leverages observational data to inform and constrain procedural generation techniques. Through systematic evaluation of available data sources, including the Global Biodiversity Information Facility (GBIF), CoralNet, the Allen Coral Atlas, the Coral Traits Database and the Smithsonian Institution's 3D coral collection, we identified both opportunities and significant limitations in current data availability. The research developed a modular pipeline implemented in Blender that combines procedural terrain generation with the placement of 3D coral models, integrating species occurrence data aggregated over geomorphic zones. To ensure robust data integration across sources and maintain compatibility with evolving taxonomic standards, the pipeline implements automated species name verification through the World Register of Marine Species (WoRMS) (WoRMS - World Register Of Marine Species, n.d.) API. While the resulting pipeline successfully establishes a foundation for automated coral reef modeling, limitations in available structural data necessitated the use of manually configured parameters for critical aspects such as terrain characteristics and population density. The pipeline's modular structure, standardized taxonomy handling, and integration with standardized classification systems position it well for future iterations as improved data sources become available. This research demonstrates the potential of data-driven approaches to coral reef modeling while highlighting the need for more comprehensive, fine-scale structural data to enable fully automated, ecologically plausible modeling of coral reef environments.

Urban microclimate significantly affects people’s experiences and activities in urban environments by a series of phenomena, among which urban flow is an important factor to be considered. Computational Fluid Dynamics (CFD) method has become a popular tool for studying urban airflow because of its low cost compared with experiment methods. However, flows over urban areas exhibit turbulence nature of being three-dimensional, unsteady, and multi-scale. Additionally, the large computational domain that should be covered and the inherent inhomogeneity of the urban structures make it challenging to do full-scale modelings. Large Eddy Simulation (LES), with the development of computing power, becomes a promising tool to study such flows.

In the campus of Delft University of Technology (TU Delft), a crossroad near the EWI building (the main building of Faculty of Electrical Engineering, Mathematics and Computer Science) is constantly complained for its strong wind. This research tackles such problem using LES, and takes TU Delft campus area itself as case study. The development of this research is composed of three stages.

In the first stage, Vreman eddy viscosity model is implemented into Canonical Navier-Stokes (CaNS), a massively-parallel Navier Stokes solver developed by Costa, (2018). Based on a structured three dimensional Cartesian grid, the subgrid scale (SGS) eddy viscosity model is inserted into the Navier Stokes equation by adding an extra diffusion term. The inserted diffusion term is discretized with second-order difference scheme along with interpolation of the velocity field due to the staggered grid arrangement. The implementation is validated with a turbulent channel flow with friction Reynolds number 𝑅𝑒𝜏 = 360. The good agreement is found and the discrepancy is small.

In the second stage, the solver employs a direct-forcing Immersed Boundary Method (IBM) and is further validated with the flow over periodic cube arrays. Signed-Distance Field (SDF), as a convenient tool, is generated and functions as read-in data for IBM. The IBM processes the effect of the boundary as an added force on the fluid points at the interface. The stair-step approach approximates the structure boundary with the cuboid cells faces. The results match well with the wind tunnel test data from Castro et al., (2006) and a previous LES study by Tomas et al., (2016).

In the last stage, the validated solver is applied to a scaled-down TU Delft campus model. The simulation setup is designed by considering the achievability of a possible future wind tunnel measurement. Three grids are used for a grid convergence analysis by comparing the total IBM force, mean velocity, and Reynolds stress at certain locations. The flow converges with the finest grid with grid number 𝑁𝑥 × 𝑁𝑦 × 𝑁𝑧 = 960 × 880 × 240. Around the EWI building, a high-speed region is found at the cross road location. Behind the building, a wake area is observed, and a clear shear layer is on the top of the building.