Topological data analysis and Network analysis approach for sustainable mobility in cities

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.

数据集说明
本数据集涵盖2023年覆盖各大洲的65座城市的城市网络结构详细信息与社会人口统计学变量。本次数据采集与处理旨在探究网络拓扑结构与城市出行就绪度（urban mobility readiness, UMRi）之间的关联。本数据集包含多项核心指标，如图熵、节点度、聚类系数、图直径、人均GDP、人口密度等。

本数据集采用多列结构，同时涵盖网络衍生变量与社会经济指标。本数据集的核心目标是为全球城市的网络结构如何影响出行效率与可持续性的相关分析提供全面支撑。

研究人员与城市规划者可借助本数据集进一步探究城市出行的复杂动态机制，并为快速扩张的城区制定交通系统优化策略。本数据集采用结构化格式，适配各类统计软件与网络分析工具的使用需求。

主要数据源：
https://www.oliverwymanforum.com/mobility/urban-mobility-readiness-index.html

本数据集附带一篇处于修订阶段的论文，用于对研究背景、目标与范围进行一般性介绍，其完整引用信息如下：
Herrera-Acevedo, D. D., & Sierra-Porta, D. (2025). 《网络结构与城市出行可持续性：基于城市出行就绪指数的城市拓扑分析》，《可持续城市与社会（Sustainable Cities and Society）》，119卷，106076。DOI: 10.1016/j.scs.2024.106076

可复现性说明
为保障研究透明度与可复现性，本数据集的数据采集与处理所用的全部脚本与笔记文件均已公开至Mendeley Data数据集仓库。该仓库包含：(1) 可执行Python脚本Network.py，通过OSMnx从OpenStreetMap获取道路网络，并将其存储为.graphml与.npy格式；(2) Jupyter笔记本Preparing_data_v2.ipynb，借助NetworkX与igraph库函数计算本文提及的拓扑指标。所有分析均基于Python 3.11.4环境完成，所使用的依赖库版本包括：OSMnx 1.3.0、NumPy 1.25.2、NetworkX 3.2.1、igraph 0.10.8、Pandas 2.0.3，以及用于并行计算的joblib 1.3.2。除另有说明外，参数配置均采用默认值，例如卢万社区检测（Louvain community detection）的分辨率参数γ=1.0。用户可参阅仓库内的README文件获取详细操作指南与环境配置说明。