Comparar métodos

	Smart City Index	Urban Scaling Laws
Área	Urban Studies	Urban Studies
Família	Process / pipeline	Regression model
Ano de origem	2011	2007
Autor original	Giffinger et al. (smart-city dimensions); Caragliu, Del Bo & Nijkamp (smart-city concept)	Luís Bettencourt & Geoffrey West
Tipo	Composite index aggregating indicators across smart-city dimensions	Power-law regression of urban indicators against population size
Fonte seminal	Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82.	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.
Outros nomes	Smart City Ranking, Cities in Motion Index, Smart-City Composite Indicator, Smart City Performance Index	Urban Scaling, Settlement Scaling Theory, Power-Law Urban Scaling, Superlinear and Sublinear Urban Scaling
Relacionados	4	4
Resumo	A smart city index is a composite indicator that scores and ranks cities on how 'smart' they are across several dimensions — typically economy, people, governance, mobility, environment and living. Each dimension gathers many raw indicators that are normalised onto a common scale, weighted, and aggregated first into dimension scores and then into a single overall number. Prominent examples such as the European smart-cities ranking of Giffinger and colleagues and the IESE Cities in Motion Index made this six-axis framing standard, turning a sprawling, contested concept into a benchmark cities can be compared on.	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.