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

Bayesian Nonparametric Estimation for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations