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	Urban Resilience Assessment	Urban Scaling Laws
Ämnesområde	Urban Studies	Urban Studies
Familj	Process / pipeline	Regression model
Ursprungsår	2016	2007
Upphovsperson	Resilience-theory and urban-planning scholarship (synthesised by Meerow, Newell & Stults)	Luís Bettencourt & Geoffrey West
Typ	Framework or composite index assessing a city's capacity to absorb and adapt to disturbance	Power-law regression of urban indicators against population size
Ursprungskälla	Meerow, S., Newell, J. P., & Stults, M. (2016). Defining urban resilience: A review. Landscape and Urban Planning, 147, 38–49.	Bettencourt, L. M. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301–7306.
Alias	City Resilience Index, Urban Resilience Framework, Resilience Capacity Assessment, Disaster Resilience of Place	Urban Scaling, Settlement Scaling Theory, Power-Law Urban Scaling, Superlinear and Sublinear Urban Scaling
Närliggande	4	4
Sammanfattning	Urban resilience assessment evaluates how well a city can absorb, adapt to, and recover from shocks such as floods, earthquakes and pandemics and from chronic stresses such as poverty and ageing infrastructure. Most assessments are framework-driven composite indices: they define resilience dimensions — infrastructural, social, economic, ecological and institutional — gather indicators for each, normalise and weight them, and aggregate to a resilience score or profile. Because, as Meerow, Newell and Stults documented, 'urban resilience' is defined in conflicting ways across the literature, every assessment must first take a position on what resilience means, for whom, and against which disturbances.	Urban scaling laws describe how the aggregate properties of cities — wealth, innovation, infrastructure, crime — change systematically with population size, following power laws rather than growing in simple proportion. Building on the 2007 work of Luís Bettencourt, Geoffrey West and colleagues, the framework shows that socioeconomic outputs typically scale superlinearly (a doubling of population more than doubles GDP and patents) while infrastructure scales sublinearly (larger cities need proportionally fewer roads and cables per person), with a single exponent β capturing the regularity across an entire urban system.