Evaluation of Environmental and Neighborhood Satisfaction Across the Socio-demographic Gradients Using and Geospatial Data - Ntombifuthi Nzimande; Lwando Royimani; Thabo Lephoto (SAGE Open)
Residential satisfaction assessment has exponentially grown over the past decades, yet scant attention has focused on inferring geospatial methods to statistical tools based on residential satisfaction questionnaire data. This has resulted in urban planning strategies neglecting environmental structures to improve residents’ overall wellbeing as geospatial data allows for objective characterization of these physical, environmental characteristics rather than relying on subjective estimates of the residential determinants. We hypothesized that the heterogeneity in residents' perceptions pertaining to their environmental and neighborhood satisfaction is largely informed by differences in their socio-economic and demographic factors. Structured questionnaires were administered in housing estates found in Budapest, Hungary and analyzed using residential satisfaction indices, multiple linear regression. NDVI was calculated using the Sentinel 2 data and employed to assess the environmental quality in each district. Regression results found education level, gender, ethnic group, and tenureship to be the socio-demographics variables that significantly affect overall residential satisfaction. The habitability index was developed comprising of the neighborhood and environmental components with the majority of the variables in both components contributing above 70% across all the study areas, except Újpalota. Results further showed that the use of geospatial data provides an objective mechanism for estimating residents’ environmental satisfaction. Hence, this study has shown the importance and advantages of integrating several fields to produce comprehensive solutions to the problems at hand. Specifically, the integration of geospatial tools with residential satisfaction questionnaires allows for innovative and practical solutions that urban planning decision-makers can utilize to identify, predict and mitigate residential dissatisfaction.