Variation in the definitions of spatial units substantially impacts the validation of causal relationships between individual and ecological contexts. Acknowledging this significant influence of neighborhood definition on research outcomes, our study seeks to introduce a scalable methodological innovation by synthesizing Tolbert and Killian’s (1987) proportional flow methodology with Kwan’s (2012a; 2012b; 2018) lessons in defining neighborhoods. Our new solution, providing scalable Census tract-level ‘Commuting Zones’, allows researchers to account for the scale of spatial units and their internal and inter-unit mobility patterns.

Commuting zones are based on the daily travel between where people live and work. Our methodology uses this information, derived from the public LEHD-LODES data, to define neighborhood areas based on strong commuting patterns. This approach has the potential to yield a more accurate and meaningful understanding of human behavior and health outcomes, calibrated to the realities of lived experiences rather than constrained by arbitrary administrative boundaries. Both researchers (interested in the neighborhood effect) and practitioners (interested in program and service delivery) can benefit from clearer approximations.