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Residential Greenery and Health Outcomes in Californian Neighborhoods: A Multilevel Analysis Using Deep Learning Streetview Imagery

PROJECT DATE: to
This project examines how theorized mechanisms between urban greenery and health may be affected by measurement styles and vegetation classifications, and whether these relationships are further impacted by entrenched inequities in our residential environments. Although urban greenery is consistently recognized as a contributor to improved mental and physical health, access to these resources is uneven and largely shaped by structural and political forces. The long term goal of this research is to not only position residential greenery as an environmental amenity, but a policy-mediated resource that can be positioned to ameliorate existing health inequities. Leveraging contemporary Street View Imagery methods, these studies will connect California Health Interview Survey to the residential greenery contexts of participants at the individual-level, while considering how additional levels of influence and socioeconomic-based variables may affect these associations.

Organization

UC Davis Betty Irene Moore School of Nursing

PRIMARY INVESTIGATOR

Julia Zabala

Population

Adult

Years

Tags

machine learning, chis