<![CDATA[This research aims to examine the spatial analysis autocorrelation to determine the pattern of relationships or correlations between locations (observations). In the case of the percentage of poverty in Mesuji Regency and the influence of agricultural land area, this method will provide important information in analyzing the relationship between the characteristics of poverty between regions. Therefore, in this study, a spatial autocorrelation analysis was carried out on the percentage of population poverty data in 2017. The methods used were the Morans I test and the Local Indicator of Spatial Autocorrelation (LISA). The results of the spatial autocorrelation of poverty among 7 sub-districts in Mesuji Regency in 2017 are spatially clustered. Poverty grouping occurs where there are sub-districts that have almost the same observational value as sub-districts that are located close to each other or neighbors.There is one grouping based on the level of poverty, which consists of one high-high cluster, namely Panca Jaya District. low-low cluster group. While the high-low outliers and low-high-outliers categories were not found in the inter-district research area in Mesuji Regency. Variable Agricultural land area has a negative and significant effect on the percentage of poor people in Mesuji Regency in 7 Districts in a statistical model, increasing agricultural land will decrease the percentage of the poor.]]>
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