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Floating catchment area (FCA) metrics are a family of measures used to quantify spatial accessibility. Spatial accessibility fuses the concepts of accessibility and availability to describe the relationship between the supply and demand of resources. FCA metrics have been applied to measure accessibility of numerous amenities including health services, public transportation, and recreation areas. Macro-level data such as census tracts and census blocks are often used to represent population locations in the calculation of FCA metrics. This approach is susceptible to masking geographic variability that may exist at less aggregated levels, such as at the individual level. This research explores how level of data aggregation can impact the measurement and interpretation of FCA based metrics. Our case study uses the E2SFCA method to measure and compare the spatial accessibility of public parks using micro- and macro-level population representations. Our analysis shows a general agreement in the FCA results among various levels of data aggregation. As expected, error in the FCA measurements generally increased as larger areal units were used; however, our results also show a somewhat nuanced picture, as particularities arising from the Modifiable Areal Unit Problem and the gravity-based calculation of the E2SFCA appear to affect the resulting differences among FCA values. Because obtaining and using individual based data can be challenging for researchers, macro-level data are often the only reliable alternative. Our findings provide insight into the limitations associated with using macro-level data to measure and interpret spatial accessibility.
Bryant et al. (Wed,) studied this question.