2024/01/10 by Thierry Feuillet, Étienne Cossart, Etienne Cossart +9 · 7 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Akaike information criterion #Computer science #Econometrics #Geography #Intraclass correlation #Mathematics #Multilevel model #Random effects model #Regression #Sample (material) #Set (abstract data type) #Spatial and Panel Data Analysis #Statistics #Urban Transport and Accessibility #Urban, Neighborhood, and Segregation Studies #Variance (accounting)
paper · pdf · doi:10.1111/gean.12385
published in Geographical Analysis 56(3), 554-572 (Wiley)
openalex publication_date 2024/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Multilevel models are one of the main statistical methods used in modeling contextual effects in social sciences. A common limitation of these methods is the use pre‐set boundaries—usually administrative units—to define contexts, when these boundaries do not always match up with the “true” causally relevant contexts that may affect the outcomes of interest. In this study applied to the obesity geography in the Paris area (France), we propose a new spatially explicit two‐step procedure to tackle this methodological issue. The first step consists in estimating a geographically weighted regression model, then using it to reveal and delineate relevant nonstationarity‐based data‐driven spatial contexts, and finally including them as a random effect into a random slope multilevel model. In applying this hybrid methodology for modeling body mass index within a sample of 9,089 French adults, we demonstrate that it outperforms administrative‐based multilevel models in terms of decreasing Akaike information criteria, and is better at accounting for contextual effects through intraclass correlation coefficient and increasing slope variance. We suggest that this procedure might be generalized to quantitative geographical analyses involving contextual effects.