Spatial analysis of landscape and sociodemographic factors associated with green stormwater infrastructure distribution in Baltimore, Maryland and Portland, Oregon

Ashley Baker, Emma Brenneman, Heejun Chang, Lauren McPhillips, Marissa Matsler

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This study explores the spatial distribution of green stormwater infrastructure (GSI) relative to sociodemographic and landscape characteristics in Portland, OR, and Baltimore, MD, USA at census block group (CBG) and census tract scales. GSI density is clustered in Portland, while it is randomly distributed over space in Baltimore. Variables that exhibit relationships with GSI density are varied over space, as well as between cities. In Baltimore, GSI density is significantly associated with presence of green space (+), impervious surface coverage (+), and population density (−) at the CBG scale; though these relationships vary over space. At the census tract scale in Baltimore, a different combination of indicators explains GSI density, including elevation (+), population characteristics, and building characteristics. Spatial regression analysis in Portland indicates that GSI density at the CBG scale is associated with residents identifying as White (−) and well-draining hydrologic soil groups A and B (−). At both census tract and CBG scales, GSI density is associated with median income (−) and sewer pipe density (−). Hierarchical modelling of GSI density presents significant spatial dependence as well as group dependence implicit to Portland at the census tract scale. Significant results of this model retain income and sewer pipe density as explanatory variables, while introducing the relationship between GSI density and impervious surface coverage. Overall, this research offers decision-relevant information for urban resilience in multiple environments and could serve as a reminder for cities to consider who is inherently exposed to GSI benefits.

Original languageEnglish (US)
Pages (from-to)461-473
Number of pages13
JournalScience of the Total Environment
Volume664
DOIs
StatePublished - May 10 2019

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All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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