US Cancer Incidence Dataset at the ZCTA Level Interpolated by Multi-Constraint Monte Carlo Simulation

This project develops a high-resolution, multi-scale cancer dataset in the U.S. by using a multi-constraint Monte Carlo simulation method to estimate suppressed county-level cancer data and further downscale them to ZIP Code Tabulation Areas (ZCTAs). This method integrates population subgroup structures and macro-level incidence rates as constraints, ensuring consistency and reliability across spatial scales. The resulting dataset spans multiple geographic units, from state and county levels to ZCTAs, enabling comprehensive analyses of cancer burden, facilitating in-depth spatial analyses, and designing precision public health interventions across multiple scales.