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Autores
Orientador(es)
Resumo(s)
Subjective well-being, or how someone reports their own life satisfaction, has important applications
in areas like international development, public health, behavioral economics, and politics, but large scale surveys are labor-intensive and costly, and results often underrepresent certain populations. By
exploring available open-source spatial data features, we can build on the existing research to test the
feasibility of predicting subjective well-being with located data that is free and crowd-sourced. Findings
show that while urban green space, healthcare or social amenity density did not show significant
correlations to overall subjective well-being, urban green space and amenity density were found to be
significant predictors of related self-report features like environmental quality, or social activity. While
external variables are difficult to properly correlate to self-reported subjective well-being, there is
potential for further exploratory research on subjective well-being domains and possible spatial
drivers.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Subjective Well-being Spatial analysis Open-Source data SDG 3 - Good health and well-being
