A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal

Cybergeo

关键词: latent class growth modeling/modelling;    k-means;    clustering;    neighborhood/neighbourhood;    poverty;    trajectories;   

DOI  :  10.4000/cybergeo.27035

来源: DOAJ

【 摘 要 】

In recent years several studies have examined changes in the distribution of poverty in the North American cities, with most empirical work assessing neighborhood change between two time points. This paper aims to make a methodological contribution to the study of neighborhood change, by comparing two classification methods, one classical (k-means clustering) the other more novel (Latent Class Growth Modelling; LCGM) to identify groups of census tracts having followed similar trajectories of poverty in the Montreal metropolitan area, Canada. Here trajectories of poverty are measured over a twenty-year period, using five time points. The relative performance of the LCGM vs. the k-means clustering was assessed using a series of multinomial logistic regressions examining how different socioeconomic variables were associated with the trajectories of poverty. Results showed that k-means and LCGM identified similar groups of census tracts characterized by ascending, descending, or stable poverty levels throughout the period, with LGCM only marginally outperforming k-means clustering.

【 授权许可】

Unknown