Fast food consumption is one of the major causes of rising obesity rates. Fast food consumers are mostly residents located in the service area —the fast food outlet's surrounding area. Conventional buffer approaches may exhibit bias in measuring service areas by ignoring the local community's detailed spatial configuration and transportation preferences.

This study uses fast food outlets and their visits provided by a mobile phone-based dataset named SafeGraph and applies a novel context-based crystal growth algorithm (CG) to delineate improved service areas of fast food outlets in Chicago. We also explore how socioeconomic variables in service areas by CG and buffer-based approaches are related to visits to fast food outlets.

Results show that compared to conventional buffers, CG produces improved service areas as it excludes inaccessible barriers and adjusts the accessible areas by transportation preferences. Further, the model using service areas of public transport users by CG yields the best performance. Additionally, the rate of single-mother households and the number of other fast food outlets nearby are positively related to fast food visits in all models. Findings acknowledge the advantages of CG and help make policy interventions to reduce fast food consumption.