Integrating spatial data science and bayesian INLA modeling for waste generation assessment in Bali

Solid waste management remains a pressing environmental concern in rapidly developing regions such as Bali, where population growth and tourism expansion intensify waste generation pressures. This study integrates a Bayesian spatio-temporal modeling framework using the Integrated Nested Laplace Approximation (INLA) to examine the dynamics of Annual Waste Generation (AWG) and Domestic Waste Generation (DWG) across districts. The INLA approach effectively captures spatial dependence and temporal variability, providing more accurate estimates than conventional regression models.Spatial diagnostic analysis using Local Moran’s I identified nineteen significant local spatial statistics (p < 0.05), revealing four key districts with distinct spatial behaviors. Badung exhibited a strong High–High cluster pattern (Ii up to 0.50922; Z.Ii ≈ 2.87), confirming high waste generation both locally and among its neighbors, driven by intensive tourism and economic activities. In contrast, Bangli displayed a Low–High pattern (Z.Ii ≈ − 2.30), suggesting a spatial outlier effect where low waste levels are adjacent to higher-producing areas. Buleleng and Karangasem showed weaker High–Low and mixed Low–Low/High–Low associations, reflecting spatial contrasts in waste management performance.The results highlight strong spatial heterogeneity and seasonal growth trends, where fluctuations in waste generation align with major tourism and cultural cycles. By combining Bayesian inference and spatial clustering diagnostics, this study provides an empirical foundation for spatially targeted policy design and supports the Sustainable Development Goals (SDG 11 and SDG 12). The findings underscore the value of spatially explicit modeling in guiding data-driven, context-specific waste management strategies in island environments.