Panel data combines cross-sectional and time series data, and spatial panel analysis looks at how location affects observations. This study used the fast double bootstrap method to model poverty rates in the Flores islands. The results showed that the spatial dependence test indicated a spatial dependence, and the poverty modeling tended to use the SAR model. The SAR random effect model explained 77.38% of the variation in poverty rates but did not meet the normality assumption. However, the SAR random effect model with the Fast Double Bootstrap approach explained 99.83% of the diversity in poverty rates and met the normality assumption, showing better results than the common spatial panel.