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	Mixed-Use Index	Urban Scaling Laws
Field	Urban Studies	Urban Studies
Family	Process / pipeline	Regression model
Year of origin	1997	2007
Originator	Cervero & Kockelman (land-use diversity / 3Ds); Frank et al. (entropy walkability term)	Luís Bettencourt & Geoffrey West
Type	Index of how evenly land uses are mixed within an area	Power-law regression of urban indicators against population size
Seminal source	Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219.	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.
Aliases	Land-Use Mix Entropy, Land-Use Diversity Index, Herfindahl Land-Use Index, Entropy Land-Use Mix	Urban Scaling, Settlement Scaling Theory, Power-Law Urban Scaling, Superlinear and Sublinear Urban Scaling
Related	4	4
Summary	A mixed-use index measures how evenly different land uses — residential, retail, office, civic, industrial — are blended within an area, turning the planning ideal of vibrant, walkable mixed-use districts into a number. The dominant formulation borrows the entropy measure from information theory: a value near zero when one use dominates and near one when uses are perfectly balanced. Popularised through the 'density, diversity, design' framework of Cervero and Kockelman and embedded in walkability indices by Frank and colleagues, these indices quantify land-use diversity for studies of travel behaviour, walkability and urban vitality.	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.