  Basketball shot location data provide valuable summary information regarding
players to coaches, sports analysts, fans, statisticians, as well as players
themselves. Represented by spatial points, such data are naturally analyzed
with spatial point process models. We present a novel nonparametric Bayesian
method for learning the underlying intensity surface built upon a combination
of Dirichlet process and Markov random field. Our method has the advantage of
effectively encouraging local spatial homogeneity when estimating a globally
heterogeneous intensity surface. Posterior inferences are performed with an
efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show
that the inferences are accurate and that the method is superior compared to
the competing methods. Application to the shot location data of $20$
representative NBA players in the 2017-2018 regular season offers interesting
insights about the shooting patterns of these players. A comparison against the
competing method shows that the proposed method can effectively incorporate
spatial contiguity into the estimation of intensity surfaces.
