Binary segmentation methods for identifying boundaries of spatial domains

Spatial clustering is an important component of spatial data analysis. The aim is to identify 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. We focus on identifying homogenous sub-regions in an ecology data set. We use binary data indicating the presence or absence of a certain plant species which are observed over a two-dimensional lattice. The problem of finding regional homogenous domains is known as segmentation, partitioning or clustering. To solve this problem we propose to use change-point methodology. We develop new methods based on a binary segmentation algorithm which is a well-known multiple change-point detection method. The proposed algorithms are applied to artificially generated data to illustrate their usefulness. Our results show that the proposed methodologies are effective in identifying multiple domains and their boundaries in two dimensional spatial data.