Spatial predictive properties of built environment characteristics assessed by drop-and-spin virtual neighborhood auditing.

ABSTRACT:

Background

Virtual neighborhood audits have been used to visually assess characteristics of the built environment for health research. Few studies have investigated spatial predictive properties of audit item responses patterns, which are important for sampling efficiency and audit item selection. We investigated the spatial properties, with a focus on predictive accuracy, of 31 individual audit items related to built environment in a major Metropolitan region of the Northeast United States.

Methods

Approximately 8000 Google Street View (GSV) scenes were assessed using the CANVAS virtual audit tool. Eleven trained raters audited the 360^° view of each GSV scene for 10 sidewalk-, 10 intersection-, and 11 neighborhood physical disorder-related characteristics. Nested

SUBMITTER: Plascak JJ 

PROVIDER: S-EPMC7257196 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature