We present a Bayesian framework for inferring spatial preferences of recombination from multiple putative recombinant nucleotide sequences. Phylogenetic recombination detection has been an active area of research for the last 15 years. However, only recently attempts to summarize information from several instances of recombination have been made. We propose a hierarchical model that allows for simultaneous inference of recombination breakpoint locations and spatial variation in recombination frequency. The dual multiple change-point model for phylogenetic recombination detection resides at the lowest level of our hierarchy under the umbrella of a common prior on breakpoint locations. The hierarchical prior allows for information about spatial preferences of recombination to be shared among individual data sets. To overcome the sparseness of breakpoint data, dictated by the modest number of available recombinant sequences, we a priori impose a biologically relevant correlation structure on recombination location log odds via a Gaussian Markov random field hyperprior. To examine the capabilities of our model to recover spatial variation in recombination frequency, we simulate recombination from a predefined distribution of breakpoint locations. We then proceed with the analysis of 42 human immunodeficiency virus (HIV) intersubtype gag recombinants and identify a putative recombination hotspot.