Abstract
Building-based infrastructure, encompassing social, economic, and environmental components, forms the cornerstone of sustainable cities and communities. However, the current targets and indicators of the UN’s Sustainable Development Goal 11 (SDG 11) focus on individual infrastructure types, leaving a knowledge gap regarding the interlinkage between the diversity of infrastructure types and SDG 11. Here, we integrate crowdsourced data from OpenStreetMap and machine learning via AutoGluon to measure building-based infrastructure diversity across scales, from community grids (1-km grid) to city levels, and assess inequality across 482 global cities (2017–2025). Our result reveals that the advantage of diversity in the Global North relative to the Global South is more pronounced at the community scale (31.07% higher) than at the city scale (17.91% higher) through 2025. Temporally, while global diversity rises, inequality decreases by 1.15% in the Global North but increases by 14.96% in the Global South. This divergence is associated with a scale-dependent decoupling, in which the Global South prioritizes aggregate infrastructure growth over equitable distribution. Our findings underscore the importance of balanced development in urban policies and regional planning across continents, highlighting the pivotal role of infrastructure diversity as a bridge connecting various SDGs to basic urban services.
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Data availability
Global building footprint data are available in the Global ML Building Footprints datasets on GitHub (excluding China). (https://github.com/microsoft/GlobalMLBuildingFootprints) Impervious surface data can be downloaded from the Star Cloud Data Service Platform. (https://data-starcloud.pcl.ac.cn/iearthdata) China’s building footprint data can be accessed from the CBRA and CMAB datasets. (https://zenodo.org/records/7500612, https://doi.org/10.6084/m9.figshare.27992417.v2) Google Earth satellite imagery can be accessed via the open map service application program interface (Google Earth API) provided by Google. (https://www.google.com/earth) The boundaries of major human settlements can be obtained from the global human settlement layer project of the European Commission’s joint research centre. (https://human-settlement.emergency.copernicus.eu/datasets.php) POI data, AOI data, and land use data can be accessed from the OpenStreetMap (OSM) community. (https://www.openstreetmap.org) A PDF describing the OSM shapefiles can be downloaded from the link. (https://download.geofabrik.de/osm-data-in-gis-formats-free.pdf) Grid-level per capita GDP data can be downloaded from the paper “Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita PPP over 1990-2022.” (https://zenodo.org/records/16741980) Grid-level population data comes from the LandScan global population database. (https://landscan.ornl.gov) Data on the HDI are from the United Nations Development Programme. (https://hdr.undp.org).
Code availability
The codes used for data processing and analyses are publicly available at: (https://github.com/RCAIG/Infrastructure_diversity).
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Acknowledgements
This research has received funding from the Global STEM Professorship, Hong Kong SAR Government (P0039329), Hong Kong RGC (grant reference # 15300923), and Hong Kong Polytechnic University (P0046482 and P0038446).
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Zhixing Chen: conceptual development, methodology, writing-original draft, investigation, visualization, validation, software, formal analysis. Qihao Weng: conceptual development, methodology, writing-review & editing, funding acquisition, investigation, project administration, resources, supervision.
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Chen, Z., Weng, Q. Pathways to spatial equity: lessons from global patterns of urban infrastructure diversity. npj Urban Sustain (2026). https://doi.org/10.1038/s42949-026-00378-1
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DOI: https://doi.org/10.1038/s42949-026-00378-1
