Hospital Distance Decay Model Calibration Results

Hospital-level distance decay model calibration data contains statistical fitting results for power-law or exponential functional forms. The dataset includes parameter estimates, R², and AIC values for model evaluation. It was created by Ali Khosravi Kazazi and published on figshare in April 2026.

Use Cases

• Compare the goodness-of-fit (R²) between power-law and exponential decay models for different hospitals.
• Select the best-fitting functional form for a given hospital using Akaike Information Criterion (AIC) values.
• Apply calibrated parameter estimates to predict patient visit volumes based on travel distance.
• Analyze the variability in distance decay parameters across different hospital locations.

Strengths

• Includes standard model comparison metrics: R² and AIC values.
• Provides explicit functional form selection (power-law or exponential) for each model.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Dataset is very small at 30.6 KB, indicating limited scope and likely a small number of hospital models.
• No sample data or column definitions are provided, obscuring the exact structure.
• The geographic coverage and number of hospitals included are unknown.

Provenance

Source

figshare, author Ali Khosravi Kazazi.

Collection Method

Statistical fitting results from model calibration.

Freshness

Last updated in April 2026.