Topological data analysis and Network analysis approach for sustainable mobility in cities
Description
Data Description This dataset contains detailed information on urban network structures and socio-demographic variables for 65 cities across various continents for the year 2023. The data were collected and processed to explore the relationship between network topology and urban mobility readiness (UMRi). The dataset includes key metrics such as graph entropy, node degree, clustering coefficient, graph diameter, GDP per capita, population density, and more. The dataset is organized into multiple columns representing both network-derived variables and socio-economic indicators. The primary objective of this dataset is to provide a comprehensive basis for analyzing how urban network structures influence mobility efficiency and sustainability in cities worldwide. Researchers and urban planners can use this dataset to further explore the complex dynamics of urban mobility and develop strategies for improving transportation systems in growing urban areas. The data are provided in a structured format, suitable for use in statistical software and network analysis tools. Main Source primary data: https://www.oliverwymanforum.com/mobility/urban-mobility-readiness-index.html A paper (in revision stage!) is included in this dataset for a general introduction, aims and scope. The full citation for that is: Herrera-Acevedo, D. D., & Sierra-Porta, D. (2025). Network structure and urban mobility sustainability: A topological analysis of cities from the urban mobility readiness index. Sustainable Cities and Society, 119, 106076. https://doi.org/10.1016/j.scs.2024.106076 Reproducibility: For transparency and reproducibility, all scripts and notebooks used in the data collection and processing are openly available in the Mendeley Data repository. The repository includes (i) an executable Python script (Network.py) that retrieves the street networks from OpenStreetMap via OSMnx and stores them in .graphml and .npy formats, and (ii) a Jupyter notebook (Preparing_data_v2.ipynb) that implements NetworkX and igraph functions to compute the topological metrics reported in this article. All analyses were performed in Python 3.11.4, using OSMnx 1.3.0, NumPy 1.25.2, NetworkX 3.2.1, igraph 0.10.8, Pandas 2.0.3, and joblib 1.3.2 for parallelization. Parameterization followed default values unless otherwise specified, e.g., Louvain community detection was executed with resolution parameter γ = 1.0. Users are referred to the repository’s README file for detailed instructions and environment specifications.