A dual-method approach toward measuring the built environment - sampling optimization, validity, and efficiency of using GIS and virtual auditing

Health Place. 2021 Jan:67:102482. doi: 10.1016/j.healthplace.2020.102482. Epub 2020 Dec 29.

Abstract

In recent years, GIS and virtual auditing have been widely used to measure the built environment, and each method carries its strengths and weaknesses. To generate higher quality, more cost-effective, and less time-consuming measures, it is necessary to explore dual- or multi-method strategy toward sampling optimization, improvement of measurement, and enhancement of efficiency. To justify the proposed dual-method approach, the study has three major objectives. First, it examines the uncertainties associated with different sample sizes by using GIS to generate scenarios that contrast the validity of measurements to aid sampling optimization in auditing. Second, it compares the validity of GIS measures with those generated through Google Street View Auditing (GSVA) by human raters. Third, it further examines the efficiency of the proposed dual-method approach in comparison to the two individual methods. Such investigation generates several novel findings. First, the study presents important evidence to support that GIS measures can offer sampling guidance applicable to the GSVA method. It leads to a recommendation of sampling sizes (5%-20%) for cases in settings with a mixture of affluent and disadvantaged neighborhoods. Results further indicate that different communities and certain individual features and characteristics may demand different sampling practices. Second, the study found that while GSVA is trustworthy for most characteristic variables, especially those that required subjective input, GIS provides well-validated measures for certain objective environmental attributes. Furthermore, the study reports that a dual-method approach of GIS and GSVA had a lower financial and time burden than using GSVA alone and is thus recommended as a comprehensive solution for optimal measurement of an objective built environment in mixed urban neighborhoods.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Built Environment*
  • Environment Design
  • Geographic Information Systems*
  • Humans
  • Research Design
  • Residence Characteristics