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The degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The Local G statistic identified 42 significant spatial association pockets, whereas the Global G statistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran's I statistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability.