Flexible Spatial Multilevel Modeling of Neighborhood Satisfaction in Beijing

摘要

This article develops an innovative and flexible Bayesian spatial multilevel model to examine the sociospatial variations in perceived neighborhood satisfaction, using a large-scale household satisfaction survey in Beijing. In particular, we investigate the impact of a variety of housing tenure types on neighborhood satisfaction, controlling for household and individual sociodemographic attributes and geographical contextual effects. The proposed methodology offers a flexible framework for modeling spatially clustered survey data widely used in social science research by explicitly accounting for spatial dependence and heterogeneity effects. The results show that neighborhood satisfaction is influenced by individual, locational, and contextual factors. Homeowners, except those of resettlement housing, tend to be more satisfied with their neighborhood environment than renters. Moreover, the impacts of housing tenure types on satisfaction vary significantly in different neighborhood contexts and spatial locations.