Cultural Stations Survey – Statistical Analysis Reproducibility Package
This item contains a reproducibility package for the Python-based statistical and machine-learning analyses of the Cultural Stations survey conducted as part of a doctoral dissertation.The package documents both the conventional statistical workflow and three complementary machine-learning analyses examining perceived urban significance, local versus non-local behavioural choice, and heterogeneous user typologies. It includes the relevant datasets, Jupyter Notebooks, software requirements, reproduced statistical outputs, model-evaluation results, generated figures, and a README file describing the analytical procedures, variables, file dependencies, and software environment.The statistical analyses were conducted in Python using Jupyter Notebook. The machine-learning component includes multi-class classification of perceived urban significance, binary classification of behavioural choice, and unsupervised clustering of user profiles. A fixed random seed was used in the machine-learning workflows to support reproducibility.The files are provided to document the complete analytical workflow and to support transparency, verification, and reproducibility of the dissertation results. This deposit is intended as supplementary research material rather than as a standalone research article.