Over the past two decades, Cambodia has achieved notable progress in poverty reduction. However, monitoring subnational poverty dynamics remains challenging due to the reliance on aggregated national statistics, which impedes the effective tracking of subnational trends critical for achieving the Sustainable Development Goals (SDGs) on poverty eradication. This study utilizes Bayesian hierarchical geostatistical models to estimate multidimensional poverty rates across Cambodia at varying spatial resolutions for the period 2000 to 2014. By integrating georeferenced household survey data, satellite imagery, and administrative records, the analysis generates poverty maps that address the limitations of traditional national-level statistics. The findings reveal pronounced spatial heterogeneity in poverty reduction, with significant improvements observed in regions benefiting from targeted interventions, such as Special Economic Zones (SEZs) and localized infrastructure projects in Battambang province. Furthermore, the study highlights a divergence between monetary and multidimensional poverty metrics. This misalignment is particularly evident in areas such as the Tonlé Sap and Cambodia’s coastal regions, where unique socioeconomic factors might shape poverty dynamics. These findings emphasize the critical importance of designing tailored, region-specific policy responses to address localized poverty effectively.
