How much are built environments changing, and where?: Patterns of change by neighborhood sociodemographic characteristics across seven U.S. metropolitan areas. (November 2016)

• Record Type: Journal Article
• Authors: Hirsch, Jana A.; Grengs, Joe; Schulz, Amy; Adar, Sara D.; Rodriguez, Daniel A.; Brines, Shannon J.; Diez Roux, Ana V.
• Is Part Of: Social science & medicine. Volume 169(2016)
• Page Start: 97
• Page End: 105
• Publication Date: 2016-11
• Subjects: ACS American Community Survey -- ANOVA Analysis of Variance -- GIS Geographic Information Systems -- HH Statistically significant cluster of high increases surrounded by high increases -- IQR Interquartile Range -- NETS National Establishment Time Series -- SIC Standard Industrial Classification; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; Medicine -- Periodicals; Médecine sociale -- Périodiques; Anthropologie médicale -- Périodiques; Santé publique -- Périodiques; Psychologie -- Périodiques; Médecine -- Périodiques; Electronic journals; 362.105
• DOI: 10.1016/j.socscimed.2016.09.032
• Languages: English
• ISSNs: 0277-9536

Abstract

Abstract: Investments in neighborhood built environments could increase physical activity and overall health. Disproportionate distribution of these changes in advantaged neighborhoods could inflate health disparities. Little information exists on where changes are occurring. This paper aims to 1) identify changes in the built environment in neighborhoods and 2) investigate associations between high levels of change and sociodemographic characteristics. Using Geographic Information Systems, neighborhood land-use, local destinations (for walking, social engagement, and physical activity), and sociodemographics were characterized in 2000 and 2010 for seven U.S. cities. Linear and change on change models estimated associations of built environment changes with baseline (2000) and change (2010–2000) in sociodemographics. Spatial patterns were assessed using Global Moran's I to measure overall clustering of change and Local Moran's I to identify statistically significant clusters of high increases surrounded by high increases (HH). Sociodemographic characteristics were compared between HH cluster and other tracts using Analysis of Variance (ANOVA). We observed small land-use changes but increases in the destination types. Greater increases in destinations were associated with higher percentage non-Hispanic whites, percentage households with no vehicle, and median household income. Associations were present for both baseline sociodemographics and changes over time. Greater increases in destinations were associated with lower baseline percentage over 65 but higher increases in percentage over 65 between 2000 and 2010. Global Moran's indicated changes were spatially clustered. HH cluster tracts started with a higher percentage non-Hispanic whites and higher percentage of households without vehicles. Between 2000 and 2010, HH cluster tracts experienced increases in percent non-Hispanic white, greater increases in median household income, and larger decreases in percent of households without a vehicle. Changes in the built environment are occurring in neighborhoods across a diverse set of U.S. metropolitan areas, but are patterned such that they may lead to increased health disparities over time. Highlights: Neighborhood destinations increased, potentially promoting walkable neighborhoods. Change in destinations occurred in advantaged neighborhoods (e.g. white, wealthier). Neighborhoods experienced increases in both destinations and advantage. Neighborhood built environment changes may increase health disparities over time.