Projects per year
In this article we argue that the recent focus on the suburbanization of poverty is problematic because of the ambiguities and inconsistencies in defining suburbia. To improve transparency, replicability, and comparability, we suggest that research on the geographical changes to the distribution of poverty should focus on three questions: (1) How centralized is urban poverty? (2) To what extent is it decentralizing? (3) Is it becoming spatially dispersed? With respect to all three questions, the issue of quantifying uncertainty has been underresearched. The main contribution of the article is to provide a practical and robust solution to the problem of inference based on a Bayesian multivariate conditional autoregressive (CAR) model, made accessible via the R software package CARBayes. Our approach can be applied to spatiotemporally autocorrelated data and can estimate both levels of and change in global relative centralization index (RCI), local RCIs, and dissimilarity indexes. We illustrate our method with an application to Scotland's four largest cities. Our results show that poverty was centralized in 2011 in Glasgow, Dundee, and Aberdeen. Poverty in Edinburgh, however, was decentralized: Nonpoor households tend to live closer to the center than poor ones and increasingly so. We also find evidence of statistically significant reductions in centralization of poverty in all four cities. To test whether this change is associated with poverty becoming more dispersed, we estimate changes to evenness and local decentralization of poverty, revealing complex patterns of change.
|Number of pages||13|
|Journal||Annals of the Association of American Geographers|
|Publication status||Published - 16 Sep 2016|