首页> 外文期刊>The Science of the Total Environment >Spatial analysis of landscape and sociodemographic factors associated with green stormwater infrastructure distribution in Baltimore, Maryland and Portland, Oregon
【24h】

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

机译:与马里兰州巴尔的摩和俄勒冈州波特兰市的绿色雨水基础设施分布相关的景观和社会人口统计学因素的空间分析

获取原文
获取原文并翻译 | 示例
           

摘要

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. (C) 2019 Elsevier B.V. All rights reserved.
机译:这项研究探索了俄勒冈州波特兰市和美国马里兰州巴尔的摩市的人口普查街区组(CBG)和人口普查尺度的绿色雨水基础设施(GSI)相对于社会人口统计和景观特征的空间分布,而GSI密度则集中在波特兰随机分布在巴尔的摩的太空中。呈现出与GSI密度关系的变量随空间以及城市之间的变化而变化。在巴尔的摩,CBG规模的GSI密度与绿色空间(+),不透水的表面覆盖率(+)和人口密度(-)显着相关。尽管这些关系随空间而变化。在巴尔的摩的人口普查区域,不同的指标组合可以解释GSI密度,包括海拔(+),人口特征和建筑特征。波特兰的空间回归分析表明,CBG规模的GSI密度与居民识别为白色(-)以及排水良好的水文土壤A和B组(-)有关。在人口普查和CBG规模上,GSI密度都与中位数收入(-)和下水道密度(-)相关。 GSI密度的层次建模显示了人口普查范围内显着的空间依赖性以及隐含于波特兰的群体依赖性。该模型的重要结果保留了收入和下水道密度作为解释变量,同时介绍了GSI密度与不透水表面覆盖率之间的关系。总体而言,这项研究提供了与决策相关的信息,以在多种环境中提高城市的抗灾能力,并可以提醒城市考虑哪些人固有地享有GSI的利益。 (C)2019 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号