Research Mentor(s): Natalie Colabianchi, Associate Professor
Research Mentor School/College/Department: Applied Exercise Science, School of Kinesiology
Presentation Date: Thursday, April 22, 2021
Session: Session 4 (2pm-2:50pm)
Breakout Room: Room 6
Relevance: The built environment plays a significant role in contributing to health outcomes. The Environment and Policy Lab (EPL) is researching the association between the built environment and health outcomes in a cost-effective, accessible way by using Google Earth to assess neighborhood features. However, there are challenges in assessing certain neighborhood features. This study assessed the inter-rater reliability of several features of the built environment. Methods: Inter-rater reliability was assessed in a sample of audited street segments (N = 226) in the residential neighborhoods of participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Each street segment was rated by two trained individuals. Characteristics of the built and social environment (BSE) assessed in this analysis included sidewalk maintenance, streetlights, number of trees, landscaping maintenance, and bike lanes. Cohen’s kappa (K) was used to estimate agreement between ratings. Results: Inter-rater reliability was highest for bike lanes (K = .82) and lower for streetlights (K = .46) and number of trees (K = .41). Landscaping maintenance had the lowest reliability (K = .18). Conclusion: Inter-rater reliability analysis determines how reliable Google Earth is in assessing these neighborhood features, which is crucial to determining which features can be examined in future analyses as predictors of health outcomes. Our results show that the more objective features had moderate to high inter-rater reliability and more subjective features had low inter-rater reliability. Therefore, straightforward and clear-cut features are what can be reliably assessed through Google Earth.
Authors: Cameron Slavkin, Ian Lang, Cathy Antonakos, Natalie Colabianchi
Research Method: Data Collection and Analysis