A framework for applying computational methods to identify optimal open space location for physical activity

The public health field is faced with much concern about low levels of physical activity (PA) and increased risks of chronic diseases which are associated with numerous health conditions and generally lead to lower life expectancy in most developed countries. Open spaces such as parks, plazas, and greenways play a critical role in promoting PA in communities by providing accessible and convenient settings where people can engage in various forms of PA. This paper presents a well-structured framework that uses a streamlined approach to analyse multiple physical and environmental factors influencing the use of open spaces for PA. Using vector data, scripts were developed to automate geospatial analysis including network analysis, proximity analysis, transit access density analysis, and the Shannon diversity index through the QGIS Python API. The results demonstrate the potential of computational methods to fully automate workflows for identifying optimal open space locations that support convenient PA engagement in communities.