Identifying Optimal Locations for Urban Green Infrastructure to Reduce Health Inequalities: A GIS-Based Approach to combine Health, Land-use, Socioeconomics and Ecosystem Services in Stockholm

Abstract: Cities are growing at unprecedented rates and are expected to be home to 70% of the world’s population in 2050. In this process, they face challenges such as densification, rapid population growth and loss of land and ecosystem services. Cities also have to remain livable and accessible to all. In 2014, the Swedish Public Health Agency declared that it would aim to close all avoidable health inequalities within one generation. In order to reach these objectives while also complying with the Sustainable Development Goals, urban green infrastructure (UGI) has been increasingly viewed as a powerful instrument that cities can utilize to help them meet their sustainability and human health targets. As nature-based solutions, UGI can greatly contribute to building resilience in urban areas by providing a number of ecosystem services. Simultaneously, UGI have also been shown to possess equigenic functions – the capacity to support the health of the least advantaged population groups equally or more so than the most privileged. This study has therefore aimed to operationalize a methodology to help identify optimal locations for developing and managing UGI in Stockholm with the aim of prioritizing health and minimizing impacts on existing ecosystems. This was done by drawing on 3 spatial datasets (land-cover, health and healthcare consumption, socioeconomics) and combining them using a GIS. The resulting maps are made for individual as well as aggregated health indicators. They display multiple optimal location clusters that were often located in the outer parts of the city, notably in the north-western and south-eastern boroughs. The inner-city however, showed little need for equigenic UGI improvements. The results and the implications of this methodology are discussed in relation to several aspects of UGI, including quality, quantity and accessibility, gentrification and UGI’s role in the smart city. Suggestions for future research building on this methodology are also provided.