The food environment is a target for policy interventions aimed at improving diet, but there is a lack of consistent evidence to guide such policy interventions, partially attributed to spatial measures of the food environment that are too narrow in scope. This study developed a workflow in the R programming language for characterising food access using multivariate methods adapted from community ecology, not previously applied in food environment research. The workflow was tested in the city of Perth, Western Australia and applied to both standard business directory data and open, crowdsourced data. The workflow was run on all Census Mesh Blocks (MB) in the Perth urban area, using the following steps: 1) collect and process food outlet and MB data, calculating a 15-min walking network isochrone around each MB centroid; 2) cluster the MB based on food outlet availability and verify identified clusters using multivariate methods; and 3) profile clusters using measures of diversity and accessibility. The results show three zones of food access in Perth, differentiated by the type and abundance of food outlets available, with better accessibility and diversity in established suburbs. The workflow is a flexible and quick tool that can help professionals and practitioners to calculate a comprehensive set of indicators of food access in any location where food outlet data is available. The combination of open and standard business directory data provides a more complete picture of the food environment than one dataset alone.