Recent contributions provide researchers with a useful toolbox to estimate counterfactual distributions of scalar random variables. These techniques have been widely applied in the literature. Typically, the dependent variable of interest has been a scalar and little consideration has been given to spatial factors. In this paper we propose a simple method to construct the counterfactual distribution of the location of a variable across space. We apply the spatial counterfactual technique to assess how much changes in individual characteristics of Hispanics in the Washington DC area account for changes in the distribution of their residential location choices.