A Reliable, Feasible Method to Observe Neighborhoods at High Spatial Resolution

Abstract

Introduction: Systematic social observation (SSO) methods traditionally measure neighborhoods at street level and have been performed reliably using virtual applications to increase feasibility. Research indicates that collection at even higher spatial resolution may better elucidate the health impact of neighborhood factors, but whether virtual applications can reliably capture social determinants of health at the smallest geographic resolution (parcel level) remains uncertain. This paper presents a novel, parcel-level SSO methodology and assesses whether this new method can be collected reliably using Google Street View and is feasible.

Methods: Multiple raters (N=5) observed 42 neighborhoods. In 2016, inter-rater reliability (observed agreement and kappa coefficient) was compared for four SSO methods: (1) street-level in person; (2) street-level virtual; (3) parcel-level in person; and (4) parcel-level virtual. Intra-rater reliability (observed agreement and kappa coefficient) was calculated to determine whether parcel-level methods produce results comparable to traditional street-level observation.

Results: Substantial levels of inter-rater agreement were documented across all four methods; all methods had >70% of items with at least substantial agreement. Only physical decay showed higher levels of agreement (83% of items with >75% agreement) for direct versus virtual rating source. Intra-rater agreement comparing street- versus parcel-level methods resulted in observed agreement >75% for all but one item (90%).

Conclusions: Results support the use of Google Street View as a reliable, feasible tool for performing SSO at the smallest geographic resolution. Validation of a new parcel-level method collected virtually may improve the assessment of social determinants contributing to disparities in health behaviors and outcomes.