Porównaj metody

	Shrinking Cities Analysis	Urban Sprawl Measurement
Dziedzina	Urban Studies	Urban Studies
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2014	2014
Twórca	Shrinking Cities research network; Haase, Rink, Grossmann, Bernt, Mykhnenko (synthesis)	Reid Ewing & Shima Hamidi (building on Galster et al.)
Typ	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
Źródło pierwotne	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.
Inne nazwy	Urban Shrinkage Analysis, Urban Decline Analysis, Right-Sizing Analysis, Depopulation Analysis	Sprawl Index, Compactness Index of Sprawl, Ewing Sprawl Index, Composite Sprawl Measure
Pokrewne	4	4
Podsumowanie	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.