方法对比

	Shrinking Cities Analysis	Urban Sprawl Measurement
领域	Urban Studies	Urban Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2014	2014
提出者	Shrinking Cities research network; Haase, Rink, Grossmann, Bernt, Mykhnenko (synthesis)	Reid Ewing & Shima Hamidi (building on Galster et al.)
类型	Descriptive pipeline for analysing urban population and economic decline, vacancy, and right-sizing	Composite index combining multiple dimensions of urban form into a sprawl/compactness score
开创性文献	Haase, A., Rink, D., Grossmann, K., Bernt, M., & Mykhnenko, V. (2014). Conceptualizing urban shrinkage. Environment and Planning A, 46(7), 1519–1534.	Ewing, R., & Hamidi, S. (2015). Compactness versus sprawl: A review of recent evidence from the United States. Journal of Planning Literature, 30(4), 413–432.
别名	Urban Shrinkage Analysis, Urban Decline Analysis, Right-Sizing Analysis, Depopulation Analysis	Sprawl Index, Compactness Index of Sprawl, Ewing Sprawl Index, Composite Sprawl Measure
相关	4	4
摘要	Shrinking cities analysis is the study of cities and neighbourhoods that are losing population and economic activity, tracing the demographic decline, job loss, housing vacancy, and infrastructural over-capacity that follow, and the 'right-sizing' planning responses they provoke. It treats shrinkage not as the temporary failure of a growth path but as a distinct, often persistent urban trajectory requiring its own descriptive tools. The conceptual synthesis by Haase and colleagues in 2014 frames urban shrinkage as a multidimensional process linking population loss, economic restructuring, and changes in the built environment.	Urban sprawl measurement quantifies how compact or sprawling a metropolitan region is by combining several distinct dimensions of urban form into a single composite index. The dominant approach, developed by Reid Ewing, Shima Hamidi and colleagues, captures four factors — development density, land-use mix, activity centering, and street-network connectivity — and folds standardized indicators of each into one score, calibrated so the average region equals 100 and higher values mean greater compactness. Because sprawl is multidimensional, no single variable such as density adequately describes it, which is why the composite-index strategy has become the standard for comparing regions and linking form to outcomes.