Are intersectoral GDP contributions similar with nearby states? A semi-model-based spatial cluster analysis

Intersectoral Gross Domestic Product (GDP) contributions reflect economic development in different industries. A good understanding of clusters of intersectoral GDP contributions among different subregions plays a vital role in making local policies and building economic development strategies. We propose a semi-model-based clustering method, i.e, a Markov random field constraint mixture of finite mixtures model to tackle this issue. Our proposed method has the advantage of 1) data-driven determination of the final number of clusters and 2) allowing for both locally spatially contiguous clusters and globally discontiguous clusters. Posterior inference is performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. We demonstrate the performance of the proposed method using both simulation studies and a real-world example where intersectoral GDP contribution of year 2019 data, obtained from the U.S. Bureau of Economic Analysis, is studied.