Three-dimensional city models are widely used for simulation and analysis, yet their creation remains constrained by data availability, geometric errors, and the labour-intensive nature of manual modelling. Procedurally generated cities offer a practical alternative, but existing methods lack plausibility because they rely on generalised rule-based approaches rather than real urban data. In this paper, we propose the CityStack framework, a three-step pipeline for the analysis, encoding, and generation of plausible cities grounded in real-world morphological data. The framework decomposes urban form into hierarchical layers; in this paper, we focus on the minor road network and the building footprint layers. Using publicly available geospatial data from 43 cities worldwide, we introduce new morphological metrics for characterising street network patterns and apply a Gaussian Mixture Model to identify 13 global road typologies, alongside a supervised gradient boosting classifier that assigns one of 9 building footprint typologies. Urban form is encoded in a typology grid, a raster representation in which each 100 m × 100 m cell stores the dominant typology, serving as a compact, comparable intermediate format between analysis and generation. New city variants are synthesised by adapting typology grids via simulated annealing and generating roads and buildings from typology statistics. All components are implemented in an open-source command-line tool, making the full pipeline reproducible and accessible for a range of urban modelling applications.