Abstract ID: 3.13073 | Not reviewed | Requested as: Talk | TBA | TBA

This study informs on the availability of the Urban Centre Data Base (UCDB; Mari Rivero, 2024); a database of over 11,000 cities of over 50,000 inhabitants each, and their use in locating and quantitatively characterising cities in mountainous regions at global scale. The study also shows locally derived analytics on slope and elevation variations calculated within city perimeters; as well as elevation, slope and land cover in the proximity of cities and settlements, to further characterize mountain city environments and the societal resilience within. The study aims to spur a debate on- how is a mountain city defined? – as different sets of mountain cities can be obtained by intersecting UCDB with different spatial delineations of mountains. By using “The inventory of world mountains ” (Snethlage et al., 2022) we show that different sets of mountain cities can also be generated by changing the spatial selection parameters. We aim to show that the community of mountain researchers and policy makers may customize spatial selection parameters and mountain definitions to obtain a set of mountain cities that best suits the research and policy interests. With local case studies focusing on study areas of 100x100km we aim to show that for every city and location on Earth one can derive analytics that quantify slope and elevation within cities and settlements outlines, as well as elevation, slope and land cover in the proximity of settlements, systematically, quantitatively, and visually-analytically characterizing the interactions of settlement dynamics with the surrounding topography. We will show five case studies to point to the landscape diversity of different mountain cities and show the analytics for explaining that diversity. These local analytics can be of use to to improve our understanding of the resilience of mountain settlements to the impacts of hazards and climate change.

Mari Rivero, Ines; et al. (2024): GHS-UCDB R2024A – GHS Urban Centre Database 2025.

Snethlage, M.A., Geschke, J., Ranipeta, A. et al. A hierarchical inventory of the world’s mountains for global comparative mountain science. Sci Data 9, 149 (2022).