  Spatially referenced data are increasingly available thanks to the
development of modern GPS technology. They also provide rich opportunities for
spatial analytics in the field of marketing science. Our main interest is to
propose a new efficient statistical framework to conduct spatial segmentation
analysis for restaurants located in a metropolitan area in the U.S. The spatial
segmentation problem poses important statistical challenges: selecting the
optimal number of underlying structures of market segments, capturing complex
and flexible spatial structures, and resolving any possible small n and large p
issue which can be typical in latent class analysis. Existing approaches try to
tackle these issues in heuristic ways or seem silent on them. To overcome these
challenges, we propose a new statistical framework based on regularized
Bayesian spatial mixture regressions with Dirichlet process integrating ridge
or lasso regularization. Our simulation study demonstrates that the proposed
models successfully recover the underlying spatial clustering structures and
outperforms two existing benchmark models. In the empirical analysis using
online customer satisfaction data from the Yelp, our models provides
interesting insights on segment-level key drivers of customer satisfaction and
interpretable relationships between regional demographics and restaurants'
characteristics.
