Place‐based spatial accessibility quantifies the distribution of access to goods and services across space. The Two‐Step Floating Catchment Area (2SFCA) family of methods have become a default tool for spatial accessibility analysis in part due to their intuitive approach and interpretability. This family of methods relies on calculating catchment areas around supply locations to estimate the area and population that may utilize them. However, these “catchment areas” are generally defined by origin‐destination matrices of travel‐time, giving us point‐to‐point distances and not polygons with actual area. This means that population geographies (census tracts, blocks, etc.) are binarily included or excluded, with no room for partial inclusion. When using nongranular data, which is often the case due to data privacy restrictions, this has the potential to cause significant errors in accessibility measurements. In this article, we propose Areal 2SFCA: a new approach that considers the area of overlap between travel‐time polygons and population geographies. We demonstrate the effectiveness of the Areal 2SFCA method using a case study that compares the Enhanced Two‐Step Floating Catchment Area (E2SFCA) and Areal E2SFCA for the state of Illinois in the USA using multiple population granularities.
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