<![CDATA[This study analyzes spatial autocorrelation patterns and maps unmet need associated with the allocation of the Program Sembako (Basic Food Commodities Program) quota in Sidoarjo Regency by incorporating spatial analysis into the policy assessment framework. A quantitative Exploratory Spatial Data Analysis is employed, utilizing Global Moran's I and Local Moran's I, alongside the Coady-Grosh-Hoddinott (CGH) Index for measuring targeting measurement. The findings reveal significant positive spatial autocorrelation, indicating that unmet need is spatially clustered rather than randomly distributed. Micro-spatial analysis showed that the northern urban and peri-urban corridor of Sidoarjo Regency tends to form high-high clusters characterized by high unmet need, whereas western agrarian areas display a stable and progressive pattern in food assistance distribution. These results are consistent with the CGH Index, which demonstrates allocation deficits and regressivity in urban and peri-urban zones. The policy implications emphasizes the critical need to implement Geographic Targeting method to enable asymmetric quota reallocation for enhanced spatial equity]]>
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