手法を比較

	Urban Green Space Analysis	Urban Scaling Laws
分野	Urban Studies	Urban Studies
系統	Process / pipeline	Regression model
提唱年	2014	2007
提唱者	Landscape ecology and environmental-justice scholarship (e.g. Wolch, Byrne & Newell)	Luís Bettencourt & Geoffrey West
種類	Measurement of green space quantity, vegetation cover, and accessibility	Power-law regression of urban indicators against population size
原典	Wolch, J. R., Byrne, J., & Newell, J. P. (2014). Urban green space, public health, and environmental justice: The challenge of making cities 'just green enough'. Landscape and Urban Planning, 125, 234–244.	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.
別名	Green Space Provision Analysis, Urban Greenness Assessment, Green Space Accessibility Analysis, NDVI Greenness Mapping	Urban Scaling, Settlement Scaling Theory, Power-Law Urban Scaling, Superlinear and Sublinear Urban Scaling
関連	4	4
概要	Urban green space analysis measures how much vegetation and parkland a city provides and how fairly residents can reach it, combining remote-sensing greenness, per-capita provision, and accessibility into evidence for planning and public health. Satellite vegetation indices such as NDVI map greenness pixel by pixel; per-capita ratios benchmark provision against standards; and gravity or threshold accessibility measures show who lives within reach of a park. As Wolch, Byrne and Newell argued, the analysis is inseparable from environmental justice — green space is unevenly distributed, and its provision must be designed to be 'just green enough' without driving displacement.	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.