PROJECT ABSTRACT – Component: Project 1 Urban greenspaces are recognized as an effective tool for increasing climate change resilience by providing environmental, social, and public health benefits. However, unintended consequences of greenspaces have been observed, such as increased fire risks, allergens and vectors and animal hosts of infectious diseases. To effectively incorporate urban greenspaces into urban planning and design, it is critical for planners and designers to understand the broader impacts and trade-offs of urban greenspace development on public health outcomes. Specific compositions and morphologies of greenspaces may generate different environmental and health outcomes but these are poorly explored and nuanced approaches are critically lacking in the practice of urban planning. To address this knowledge gap, our proposed study aims to examine how urban greenspace designs can increase climate change resilience and public health. The study objectives are to: (1) evaluate the public health trade-offs of urban greenspaces with varying composition and morphology using a case study of heat-related illness and mosquito-borne disease risk in a large urban city, Phoenix, AZ; (2) develop a health effects assessment tool that can be used to predict the health impacts of proposed urban greenspace designs and plans; and (3) link scientific research and community engagement through greenspace design activities to co-produce knowledge, and elicit preferences and priorities in the impacts of greenspace design. To achieve these goals, we will integrate data on climate/weather, heat-related illnesses, mosquito abundance, and socio- demographics on urban greenspaces in Phoenix and Tucson metropolitan areas in Arizona with assistance from the Integrated Data Visualization Core. We will use the conditional spatial autoregression models (CAR) to analyze the associations between urban greenspaces (e.g., types, composition, and morphology) and heat- related illnesses and mosquito abundance. We will develop a health effects assessment tool based on a machine learning model trained with data from Phoenix metropolitan area and validated based on field collections conducted within the Tucson metropolitan area. This tool will be used to evaluate the performance of current urban greenspaces and different design scenarios of future urban greenspace development, to estimate varying levels of heat resilience, potential climate change-related health effects, and to identify inequities exacerbated by inequitable distribution of greenspace. Finally, we will develop a platform for community members to co-produce knowledge through observations of their environment and engage decision-makers and community members to develop priorities for greenspace planning.