Pennsylvania Hospital Accessibility Metrics from Revised E2SFCA Model

Pennsylvania hospital accessibility metrics derived from a 2023 study integrating large-scale human mobility data. The dataset contains results from a revised Enhanced Two-Step Floating Catchment Area model, which calibrates dynamic catchment boundaries and hospital-specific distance decay functions. Author Ali Khosravi Kazazi published the findings in April 2026.

Use Cases

• Analyze the correlation between revised accessibility scores and household income (r=0.31) to study socioeconomic disparities.
• Compare model outputs against poor/fair health status metrics (r=-0.12) to validate health outcome associations.
• Examine hospital-specific distance decay functions to understand heterogeneous patient attraction patterns.
• Use dynamic, visit-weighted catchment boundaries to replace static thresholds in traditional accessibility models.
• Assess inequality in healthcare access revealed by the revised model versus traditional distance-centric approaches.

Strengths

• Model outputs are empirically validated against real-world health outcomes and income data.
• The calibration uses large-scale human mobility data from 2023 for Pennsylvania.
• Results show statistically significant correlations (p=0.011) with key socioeconomic variables.

Limitations

• The dataset is very small at 5.5 KB, indicating limited raw data or aggregated summary outputs.
• Spatial coverage is restricted to Pennsylvania, limiting generalizability.
• Specific row counts and granular column details are unknown.

Provenance

Source

Study by Ali Khosravi Kazazi, published on figshare.

Collection Method

Model calibration integrating anonymized human mobility visitation patterns between neighborhoods and hospitals.

Time Range

2023

Freshness

Data reflects 2023 mobility patterns and was last updated in April 2026.

Geography

Pennsylvania, USA