방법 비교

	Gentrification Analysis	Neighborhood Effects Analysis
분야	Urban Studies	Urban Studies
계열	Process / pipeline	Process / pipeline
기원 연도	1979	2002
창시자	Ruth Glass (term, 1964); Neil Smith (rent-gap theory)	Robert J. Sampson (and the Chicago neighbourhood-effects tradition)
유형	Pipeline for detecting and measuring neighbourhood socioeconomic upgrading and displacement	Pipeline for estimating the contextual/causal effect of neighbourhood on individual outcomes
원전	Smith, N. (1979). Toward a theory of gentrification: A back to the city movement by capital, not people. Journal of the American Planning Association, 45(4), 538–548.	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.
별칭	Gentrification Measurement, Neighbourhood Upgrading Analysis, Rent Gap Analysis, Displacement Risk Analysis	Neighbourhood Effects Modelling, Contextual Effects Analysis, Multilevel Neighbourhood Analysis, Place Effects Estimation
관련	4	4
요약	Gentrification analysis is the set of methods used to detect, measure, and map the process by which a previously disinvested, lower-income neighbourhood is upgraded through an influx of capital and higher-status residents, often displacing the existing population. It typically combines repeated small-area census data on income, education, tenure, and rents with housing-market indicators to compute change indices that flag where socioeconomic status is rising fastest. Grounded in Neil Smith's 1979 rent-gap theory, the analysis frames gentrification as the reinvestment of capital in places where the gap between actual and potential land rent has grown large enough to be profitable.	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.