Panel Data GIS Modeling for Post-Disaster Policy Cost Impact

The patent segments the prediction model into multiple distinct components: (1) data collection module that gathers construction cost data, disaster data, and policy data; (2) panel data model module that structures the segmented data with region and time dimensions; (3) difference-in-differences estimator module that calculates policy impacts; and (4) confounding variable control module that adjusts for external factors. This segmentation allows each component to be optimized independently while maintaining overall prediction accuracy.

The patent introduces panel data structure that adds two dimensions to the analysis: regional dimension (different disaster-affected areas) and temporal dimension (pre-disaster and post-disaster time periods). This dimensional expansion enables the model to capture spatial heterogeneity and time-varying effects, significantly improving prediction accuracy by accounting for region-specific and time-specific factors that single-dimension models miss.

The patent implements preliminary diagnostic tests (Breusch-Pagan test and Hausman test) before selecting the final panel data model specification. The Breusch-Pagan test preliminarily checks for heteroskedasticity and determines whether random effects or fixed effects are appropriate. The Hausman test preliminarily assesses whether fixed effects or random effects model is suitable. These preliminary actions guide the model selection process and ensure appropriate model specification, improving computation accuracy while providing a systematic framework that reduces selection complexity.

The patent incorporates a feedback mechanism where diagnostic test results directly inform model specification choices. The Breusch-Pagan test results feed into the decision between pooled OLS, random effects, or fixed effects models. The Hausman test results provide feedback on whether to use fixed effects or random effects. This feedback loop ensures that the model selection is data-driven and statistically justified, improving policy impact computation accuracy while maintaining a manageable selection process through clear decision rules.

The patent employs a comprehensive data collection framework that serves multiple functions simultaneously: (1) constructing the panel data structure for regional and temporal analysis; (2) identifying treatment and control groups for difference-in-differences estimation; (3) measuring confounding variables for bias adjustment; and (4) providing inputs for diagnostic tests. This multi-functional data collection approach ensures unbiased policy impact estimates while avoiding redundant data gathering efforts, as the same data infrastructure supports multiple analytical requirements.

The patent introduces confounding variables as intermediary factors that mediate the relationship between disaster policies and construction cost variations. Variables such as macroeconomic conditions, demographic characteristics, and construction market factors serve as intermediaries that capture unobserved heterogeneity and external shocks. By incorporating these intermediary variables into the panel data model, the analysis controls for confounding effects and produces unbiased policy impact estimates, while the structured data collection process manages the complexity through systematic variable selection.