Confronta i metodi

	Shrinking Cities Analysis	Neighborhood Effects Analysis
Campo	Urban Studies	Urban Studies
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2014	2002
Ideatore	Shrinking Cities research network; Haase, Rink, Grossmann, Bernt, Mykhnenko (synthesis)	Robert J. Sampson (and the Chicago neighbourhood-effects tradition)
Tipo	Descriptive pipeline for analysing urban population and economic decline, vacancy, and right-sizing	Pipeline for estimating the contextual/causal effect of neighbourhood on individual outcomes
Fonte seminale	Haase, A., Rink, D., Grossmann, K., Bernt, M., & Mykhnenko, V. (2014). Conceptualizing urban shrinkage. Environment and Planning A, 46(7), 1519–1534.	Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing "neighborhood effects": Social processes and new directions in research. Annual Review of Sociology, 28, 443–478.
Alias	Urban Shrinkage Analysis, Urban Decline Analysis, Right-Sizing Analysis, Depopulation Analysis	Neighbourhood Effects Modelling, Contextual Effects Analysis, Multilevel Neighbourhood Analysis, Place Effects Estimation
Correlati	4	4
Sintesi	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.	Neighborhood effects analysis estimates how much the place a person lives — its poverty, social cohesion, disorder, or institutions — shapes individual outcomes such as health, crime, educational attainment, and economic mobility, over and above the individual's own characteristics. It is dominated by multilevel (hierarchical) models that recognise people are nested within neighbourhoods, separating variation that lies between places from variation within them. The central methodological challenge, crystallised in Robert Sampson and colleagues' influential 2002 review, is distinguishing genuine contextual effects from selection bias: the fact that people do not sort into neighbourhoods at random.