Management Strategy Evaluation (MSE) tools are inspired by the need for transparency, efficiency, and collaboration in harvest control rule (HCR) management. MSEs provide quantified metrics of the HCR performances and indicate the goodness in multiple dimensions, but providing HCR rankings based on such criteria is uncommon or use a simple Weight Sum Method (WSM). Acknowledging some theoretical limitations of the WSM, we propose using Technique for Order Preference by Similarity to Ideal Solution Method (TOPSIS) as an efficient alternative algorithm for recommending HCRs and conduct a sensitivity analysis of management objectives under the two frameworks, one based on simulated history and the other on the history of North Pacific Albacore (NPALB). Two conclusions are drawn based on the computation of the HCR ranking differences generated with the WSM and TOPSIS: (1) The alteration in the overall ranking of HCRs is visible, and its influence could vary substantially with user preference with theoretical merits. (2) It is common to notice shifts in the ranking for top HCRs, which potentially contributes valuable insights for practical decision making.

Cancer incidence exhibits substantial spatial disparities associated with environmental, behavioral, built-environment, healthcare access, and socioeconomic conditions, yet the extent to which these county-level associations vary geographically and across spatial scales remains insufficiently understood. This study evaluates an explainable spatial epidemiology workflow that integrates Random Forest (RF), SHapley Additive exPlanations (SHAP), permutation importance, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site cancer as a composite benchmark, colorectal cancer, female breast cancer, and melanoma of the skin across Texas, USA. All outcomes were screened from a common leakage-safe pool of 44 predictors. RF-SHAP/permutation screening was conducted only in spatially separated discovery counties, and the retained predictors were subsequently evaluated using OLS, GWR, and MGWR in independent confirmation counties. The screening procedure retained 19–23 predictors across outcomes, reducing model dimensionality by approximately 48–57%. Although screening substantially reduced AICc, in-sample also decreased, indicating improved parsimony and complexity-adjusted fit rather than improved explanatory performance; same-cardinality random, correlation-based, and LASSO benchmarks further showed that the advantage of RF-based screening was outcome- and model-dependent. In independent confirmation analyses, GWR was preferred by AICc for all-site cancer (AICc = 442.79), whereas OLS was preferred for colorectal cancer (356.48), female breast cancer (367.50), and melanoma (234.56); MGWR was not AICc-preferred for any outcome. However, where estimation was feasible, MGWR provided complementary multiscale information by distinguishing fitted associations characterized by near-global versus more localized spatial bandwidths. Five-fold nested spatial-block cross-validation showed stronger geographic predictive performance for RF, with pooled values of 0.422, 0.235, 0.442, and 0.341 for all-site, colorectal, breast, and melanoma outcomes, respectively, whereas GWR produced negative held-out for all four outcomes. These findings support explainable-ML screening primarily as a transparent dimensionality reduction strategy and demonstrate complementary roles for spatial modeling: AICc evaluates whether additional spatial complexity is justified, MGWR characterizes predictor-specific spatial scales where feasible, and spatial-block validation evaluates geographic predictive generalization. The resulting associations and spatial scales are interpreted as ecological and descriptive rather than causal.

Cancer incidence exhibits substantial spatial disparities linked to environmental, behavioral, built-environment, healthcare-access, and socioeconomic conditions, yet the spatial scales at which these relationships operate remain insufficiently understood. This study develops an explainable spatial epidemiology workflow that integrates Random Forest, SHapley Additive exPlanations (SHAP), Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site, colon, breast, and skin cancers across Texas, USA. Random Forest and SHAP were used to identify outcome-specific nonlinear predictor relevance, and OLS, GWR, and MGWR were used to compare global, local, and multiscale spatial associations before and after RF-SHAP feature screening. MGWR generally achieved higher model fit than OLS and GWR. Before RF-SHAP screening, MGWR R² values were 0.701 for all-site cancer, 0.516 for colon cancer, 0.499 for breast cancer, and 0.694 for skin cancer, compared with OLS R² values of 0.343, 0.338, 0.257, and 0.368. RF-SHAP reduced predictors by about one-half and consistently improved AICc. The results show that environmental exposures, activity-related conditions, transportation access, screening, food insecurity, and chronic health indicators contribute to spatial differences in cancer incidence. The framework links nonlinear machine-learning evidence with spatially explicit interpretation for transferable epidemiological analysis.

High‐dimensional environmental datasets stored in NetCDF format often suffer from sparsity, irregular sampling, and missing observations, which limit their utility for environmental modeling and geospatial analysis. To address these challenges, we propose a diffusion model‐based imputation strategy that uses generative learning to reconstruct multivariable spatial fields from incomplete temporal snapshots. The proposed method leverages multivariable dependencies among environmental variables, enabling the diffusion process to model complex multidimensional spatial patterns in NetCDF‐based geospatial raster fields. Experimental results on atmosphere datasets demonstrate that the proposed approach consistently improves reconstruction accuracy compared to conventional interpolation and machine learning baselines, while preserving local spatial structures. This strategy provides a reproducible framework for imputing missing values in high‐dimensional NetCDF data, thereby potentially supporting more reliable environmental monitoring and resilience planning in GIS field.