Measuring neighbourhood social and economic change for urban health studies

Usama Bilal, Manuel Franco, Bryan Lau, David Celentano and Thomas Glass
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Usama Bilal: Drexel University, USA
Manuel Franco: Universidad de Alcalá de Henares, Spain
Bryan Lau: Johns Hopkins Bloomberg School of Public Health, USA
David Celentano: Johns Hopkins Bloomberg School of Public Health, USA
Thomas Glass: Johns Hopkins Bloomberg School of Public Health, USA

Urban Studies, 2020, vol. 57, issue 6, 1301-1319

Abstract: Neighbourhood change is a complex phenomenon. To study its consequences for health outcomes, we developed a measure of neighbourhood social and economic change for all census tracts ( n = 2272) in the entire city of Madrid (Spain) in two epochs (2005–2009 and 2009–2013). We used a finite mixture modelling approach with 16 indicators from several administrative sources. We found four types of neighbourhoods: Decreasing Socioeconomic Status (SES) areas with increased diversity and decreased socioeconomic status; New Housing/Gentrification areas with high residential mobility, new housing construction and with markers of gentrification in the crisis epoch; Increasing SES areas with increased socioeconomic status and decreased diversity; and Aging areas with an aging population, low residential mobility and no new construction. We describe the baseline predictors of these types of change, finding that there is a potential widening of socioeconomic gaps, as Increasing SES areas start with higher SES, and Decreasing SES areas start with lower SES. We found a change in the spatial distribution of these types between the first and second epochs, as New Housing/Gentrification areas became more common in the centre of the city. We discuss two potential applications of this type of model to the study of the consequences of residential environment changes for health determinants and health outcomes, with a particular emphasis on retail food environments and diabetes incidence.

Keywords: analysis; finite mixture model; latent class; longitudinal data; neighbourhoods; residential environments; Spain; 分æž; æœ‰é™ æ··å ˆæ¨¡åž‹; 潜在阶级; çºµå ‘æ•°æ ®; 街区; å±…ä½ çŽ¯å¢ƒ; è¥¿ç ­ç‰™ (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:57:y:2020:i:6:p:1301-1319

DOI: 10.1177/0042098019880754

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