Identifying clusters in spatial data via sequential importance sampling

Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.